WorldWideScience

Sample records for automatic incident detection

  1. Intraoperative multichannel audio-visual information recording and automatic surgical phase and incident detection.

    Science.gov (United States)

    Suzuki, Takashi; Sakurai, Yasuo; Yoshimitsu, Kitaro; Nambu, Kyojiro; Muragaki, Yoshihiro; Iseki, Hiroshi

    2010-01-01

    Identification, analysis, and treatment of potential risk in surgical workflow are the key to decrease medical errors in operating room. For the automatic analysis of recorded surgical information, this study reports multichannel audio visual recording system, and its review and analysis system. Motion in operating room is quantified using video file size without motion tracking. Conversation among surgical staff is quantified using fast Fourier transformation and frequency filter without speech recognition. The results suggested the progression phase of surgical procedure.

  2. Automatic Analysis of Critical Incident Reports: Requirements and Use Cases.

    Science.gov (United States)

    Denecke, Kerstin

    2016-01-01

    Increasingly, critical incident reports are used as a means to increase patient safety and quality of care. The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval and analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this paper is to identify potential use cases for automatic methods that analyse critical incident reports. In more detail, we will describe how faceted search could offer an intuitive retrieval of critical incident reports and how text mining could support in analysing relations among events. To realise an automated analysis, natural language processing needs to be applied. Therefore, we analyse the language of critical incident reports and derive requirements towards automatic processing methods. We learned that there is a huge potential for an automatic analysis of incident reports, but there are still challenges to be solved.

  3. Real time freeway incident detection.

    Science.gov (United States)

    2014-04-01

    The US Department of Transportation (US-DOT) estimates that over half of all congestion : events are caused by highway incidents rather than by rush-hour traffic in big cities. Real-time : incident detection on freeways is an important part of any mo...

  4. Automatic Detection of Fake News

    OpenAIRE

    Pérez-Rosas, Verónica; Kleinberg, Bennett; Lefevre, Alexandra; Mihalcea, Rada

    2017-01-01

    The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news...

  5. Oocytes Polar Body Detection for Automatic Enucleation

    Directory of Open Access Journals (Sweden)

    Di Chen

    2016-02-01

    Full Text Available Enucleation is a crucial step in cloning. In order to achieve automatic blind enucleation, we should detect the polar body of the oocyte automatically. The conventional polar body detection approaches have low success rate or low efficiency. We propose a polar body detection method based on machine learning in this paper. On one hand, the improved Histogram of Oriented Gradient (HOG algorithm is employed to extract features of polar body images, which will increase success rate. On the other hand, a position prediction method is put forward to narrow the search range of polar body, which will improve efficiency. Experiment results show that the success rate is 96% for various types of polar bodies. Furthermore, the method is applied to an enucleation experiment and improves the degree of automatic enucleation.

  6. Automatic Smoker Detection from Telephone Speech Signals

    DEFF Research Database (Denmark)

    Alavijeh, Amir Hossein Poorjam; Hesaraki, Soheila; Safavi, Saeid

    2017-01-01

    This paper proposes an automatic smoking habit detection from spontaneous telephone speech signals. In this method, each utterance is modeled using i-vector and non-negative factor analysis (NFA) frameworks, which yield low-dimensional representation of utterances by applying factor analysis on G...

  7. Detecting Terrorism Incidence Type from News Summary

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah

    2012-01-01

    The paper presents the experiments to detect terrorism incidence type from news summary data. We have applied classification techniques on news summary data to analyze the incidence and detect the type of incidence. A number of experiments are conducted using various classification algorithms...... and results show that a simple decision tree classifier can learn incidence type with satisfactory results from news data....

  8. Automatic blood detection in capsule endoscopy video

    Czech Academy of Sciences Publication Activity Database

    Novozámský, Adam; Flusser, Jan; Tachecí, I.; Sulík, L.; Bureš, J.; Krejcar, O.

    2016-01-01

    Roč. 21, č. 12 (2016), s. 1-8, č. článku 126007. ISSN 1083-3668 R&D Projects: GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Automatic blood detection * capsule endoscopy video Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.530, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0466936.pdf

  9. Automatic Detection of Electric Power Troubles (ADEPT)

    Science.gov (United States)

    Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

    1988-11-01

    Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

  10. An automatically tuning intrusion detection system.

    Science.gov (United States)

    Yu, Zhenwei; Tsai, Jeffrey J P; Weigert, Thomas

    2007-04-01

    An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model.

  11. Automatic blood detection in capsule endoscopy video

    Science.gov (United States)

    Novozámský, Adam; Flusser, Jan; Tachecí, Ilja; Sulík, Lukáš; Bureš, Jan; Krejcar, Ondřej

    2016-12-01

    We propose two automatic methods for detecting bleeding in wireless capsule endoscopy videos of the small intestine. The first one uses solely the color information, whereas the second one incorporates the assumptions about the blood spot shape and size. The original idea is namely the definition of a new color space that provides good separability of blood pixels and intestinal wall. Both methods can be applied either individually or their results can be fused together for the final decision. We evaluate their individual performance and various fusion rules on real data, manually annotated by an endoscopist.

  12. Automatic Smoker Detection from Telephone Speech Signals

    DEFF Research Database (Denmark)

    Alavijeh, Amir Hossein Poorjam; Hesaraki, Soheila; Safavi, Saeid

    2017-01-01

    This paper proposes an automatic smoking habit detection from spontaneous telephone speech signals. In this method, each utterance is modeled using i-vector and non-negative factor analysis (NFA) frameworks, which yield low-dimensional representation of utterances by applying factor analysis...... on Gaussian mixture model means and weights respectively. Each framework is evaluated using different classification algorithms to detect the smoker speakers. Finally, score-level fusion of the i-vector-based and the NFA-based recognizers is considered to improve the classification accuracy. The proposed...... method is evaluated on telephone speech signals of speakers whose smoking habits are known drawn from the National Institute of Standards and Technology (NIST) 2008 and 2010 Speaker Recognition Evaluation databases. Experimental results over 1194 utterances show the effectiveness of the proposed approach...

  13. Spike detection II: automatic, perception-based detection and clustering.

    Science.gov (United States)

    Wilson, S B; Turner, C A; Emerson, R G; Scheuer, M L

    1999-03-01

    We developed perception-based spike detection and clustering algorithms. The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

  14. Detection of Cyberbullying Incidents on the Instagram Social Network

    OpenAIRE

    Hosseinmardi, Homa; Mattson, Sabrina Arredondo; Rafiq, Rahat Ibn; Han, Richard; Lv, Qin; Mishra, Shivakant

    2015-01-01

    Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments, and designed a labeling study for cyberbullying as well as image content using human labelers at th...

  15. Automatic system for detecting pornographic images

    Science.gov (United States)

    Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

    2002-09-01

    Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

  16. Potato Operation: automatic detection of potato diseases

    Science.gov (United States)

    Lefebvre, Marc; Zimmerman, Thierry; Baur, Charles; Guegerli, Paul; Pun, Thierry

    1995-01-01

    The Potato Operation is a collaborative, multidisciplinary project in the domain of destructive testing of agricultural products. It aims at automatizing pulp sampling of potatoes in order to detect possible viral diseases. Such viruses can decrease fields productivity by a factor of up to ten. A machine, composed of three conveyor belts, a vision system, a robotic arm and controlled by a PC has been built. Potatoes are brought one by one from a bulk to the vision system, where they are seized by a rotating holding device. The sprouts, where the viral activity is maximum, are then detected by an active vision process operating on multiple views. The 3D coordinates of the sampling point are communicated to the robot arm holding a drill. Some flesh is then sampled by the drill, then deposited into an Elisa plate. After sampling, the robot arm washes the drill in order to prevent any contamination. The PC computer simultaneously controls these processes, the conveying of the potatoes, the vision algorithms and the sampling procedure. The master process, that is the vision procedure, makes use of three methods to achieve the sprouts detection. A profile analysis first locates the sprouts as protuberances. Two frontal analyses, respectively based on fluorescence and local variance, confirm the previous detection and provide the 3D coordinate of the sampling zone. The other two processes work by interruption of the master process.

  17. Nasal pressure recordings for automatic snoring detection.

    Science.gov (United States)

    Lee, Hyo-Ki; Kim, Hojoong; Lee, Kyoung-Joung

    2015-11-01

    This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4% and a positive predictive value of 92.0% were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.

  18. Automatic detection of secondary creases in fingerprints

    Science.gov (United States)

    Vernon, David

    1993-10-01

    Human fingerprints comprise a series of whorls or ridges. In some special cases, these whorls are broken by so-called secondary creases--collinear breaks across a sequence of adjacent ridges. It is a working hypothesis that the presence of these secondary creases form a physical marker for certain human disorders. A technique to automatically detect such creases in fingerprints is described. This technique utilizes a combination of spatial filtering and region growing to identify the morphology of the locally fragmented fingerprint image. Regions are then thinned to form a skeletal model of the ridge structure. Creases are characterized by collinear terminations on ridges and are isolated by analyzing the Hough transform space derived from the ridge end points. Empirical results using both synthetic and real data are presented and discussed.

  19. Automatic detection of abnormalities in mammograms

    International Nuclear Information System (INIS)

    Suhail, Zobia; Sarwar, Mansoor; Murtaza, Kashif

    2015-01-01

    In recent years, an increased interest has been seen in the area of medical image processing and, as a consequence, Computer Aided Diagnostic (CAD) systems. The basic purpose of CAD systems is to assist doctors in the process of diagnosis. CAD systems, however, are quite expensive, especially, in most of the developing countries. Our focus is on developing a low-cost CAD system. Today, most of the CAD systems regarding mammogram classification target automatic detection of calcification and abnormal mass. Calcification normally indicates an early symptom of breast cancer if it appears as a small size bright spot in a mammogram image. Based on the observation that calcification appears as small bright spots on a mammogram image, we propose a new scale-specific blob detection technique in which the scale is selected through supervised learning. By computing energy for each pixel at two different scales, a new feature “Ratio Energy” is introduced for efficient blob detection. Due to the imposed simplicity of the feature and post processing, the running time of our algorithm is linear with respect to image size. Two major types of calcification, microcalcification and macrocalcification have been identified and highlighted by drawing a circular boundary outside the area that contains calcification. Results are quite visible and satisfactory, and the radiologists can easily view results through the final detected boundary. CAD systems are designed to help radiologists in verifying their diagnostics. A new way of identifying calcification is proposed based on the property that microcalcification is small in size and appears in clusters. Results are quite visible and encouraging, and can assist radiologists in early detection of breast cancer

  20. Automatic Detect and Trace of Solar Filaments

    Science.gov (United States)

    Fang, Cheng; Chen, P. F.; Tang, Yu-hua; Hao, Qi; Guo, Yang

    We developed a series of methods to automatically detect and trace solar filaments in solar Hα images. The programs are able to not only recognize filaments and determine their properties, such as the position, the area and other relevant parameters, but also to trace the daily evolution of the filaments. For solar full disk Hα images, the method consists of three parts: first, preprocessing is applied to correct the original images; second, the Canny edge-detection method is used to detect the filaments; third, filament properties are recognized through the morphological operators. For each Hα filament and its barb features, we introduced the unweighted undirected graph concept and adopted Dijkstra shortest-path algorithm to recognize the filament spine; then, using polarity inversion line shift method for measuring the polarities in both sides of the filament to determine the filament axis chirality; finally, employing connected components labeling method to identify the barbs and calculating the angle between each barb and spine to indicate the barb chirality. Our algorithms are applied to the observations from varied observatories, including the Optical & Near Infrared Solar Eruption Tracer (ONSET) in Nanjing University, Mauna Loa Solar Observatory (MLSO) and Big Bear Solar Observatory (BBSO). The programs are demonstrated to be effective and efficient. We used our method to automatically process and analyze 3470 images obtained by MLSO from January 1998 to December 2009, and a butterfly diagram of filaments is obtained. It shows that the latitudinal migration of solar filaments has three trends in the Solar Cycle 23: The drift velocity was fast from 1998 to the solar maximum; after the solar maximum, it became relatively slow and after 2006, the migration became divergent, signifying the solar minimum. About 60% filaments with the latitudes larger than 50 degree migrate towards the Polar Regions with relatively high velocities, and the latitudinal migrating

  1. Automatic Earthquake Detection by Active Learning

    Science.gov (United States)

    Bergen, K.; Beroza, G. C.

    2017-12-01

    In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

  2. Automatic change detection to facial expressions in adolescents

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Jiannong, Shi

    2016-01-01

    Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group) were...... automatic processing on fearful faces than happy faces. The present study indicated that adolescent’s posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information....... recruited to complete an emotional oddball task featuring on happy and one fearful condition. The measurement of event-related potential was carried out via electroencephalography and electrooculography recording, to detect visual mismatch negativity (vMMN) with regard to the automatic detection of changes...

  3. Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a

  4. Automatic invariant detection in dynamic web applications

    NARCIS (Netherlands)

    Groeneveld, F.; Mesbah, A.; Van Deursen, A.

    2010-01-01

    The complexity of modern web applications increases as client-side JavaScript and dynamic DOM programming are used to offer a more interactive web experience. In this paper, we focus on improving the dependability of such applications by automatically inferring invariants from the client-side and

  5. ALOGORITHMS FOR AUTOMATIC RUNWAY DETECTION ON VIDEO SEQUENCES

    Directory of Open Access Journals (Sweden)

    A. I. Logvin

    2015-01-01

    Full Text Available The article discusses algorithm for automatic runway detection on video sequences. The main stages of algorithm are represented. Some methods to increase reliability of recognition are described.

  6. Using Polarization features of visible light for automatic landmine detection

    NARCIS (Netherlands)

    Jong, W. de; Schavemaker, J.G.M.

    2007-01-01

    This chapter describes the usage of polarization features of visible light for automatic landmine detection. The first section gives an introduction to land-mine detection and the usage of camera systems. In section 2 detection concepts and methods that use polarization features are described.

  7. Automatic Detection of Wild-type Mouse Cranial Sutures

    DEFF Research Database (Denmark)

    Ólafsdóttir, Hildur; Darvann, Tron Andre; Hermann, Nuno V.

    , automatic detection of the cranial sutures becomes important. We have previously built a craniofacial, wild-type mouse atlas from a set of 10 Micro CT scans using a B-spline-based nonrigid registration method by Rueckert et al. Subsequently, all volumes were registered nonrigidly to the atlas. Using...... these transformations, any annotation on the atlas can automatically be transformed back to all cases. For this study, two rounds of tracing seven of the cranial sutures, were performed on the atlas by one observer. The average of the two rounds was automatically propagated to all the cases. For validation......, the observer traced the sutures on each of the mouse volumes as well. The observer outperforms the automatic approach by approximately 0.1 mm. All mice have similar errors while the suture error plots reveal that suture 1 and 2 are cumbersome, both for the observer and the automatic approach. These sutures can...

  8. Automatic detection of typical dust devils from Mars landscape images

    Science.gov (United States)

    Ogohara, Kazunori; Watanabe, Takeru; Okumura, Susumu; Hatanaka, Yuji

    2018-02-01

    This paper presents an improved algorithm for automatic detection of Martian dust devils that successfully extracts tiny bright dust devils and obscured large dust devils from two subtracted landscape images. These dust devils are frequently observed using visible cameras onboard landers or rovers. Nevertheless, previous research on automated detection of dust devils has not focused on these common types of dust devils, but on dust devils that appear on images to be irregularly bright and large. In this study, we detect these common dust devils automatically using two kinds of parameter sets for thresholding when binarizing subtracted images. We automatically extract dust devils from 266 images taken by the Spirit rover to evaluate our algorithm. Taking dust devils detected by visual inspection to be ground truth, the precision, recall and F-measure values are 0.77, 0.86, and 0.81, respectively.

  9. Automatic Emergence Detection in Complex Systems

    Directory of Open Access Journals (Sweden)

    Eugene Santos

    2017-01-01

    Full Text Available Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs, learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

  10. Automatically Detecting Authors’ Native Language

    Science.gov (United States)

    2011-03-01

    language (Na Rae Han, p.c.). Also, these language models can help ESL teachers to tailor their teaching meth- ods to the students’ native language. For...these language models provides this information to ESL teachers, they can help the students 1 more effectively. Detecting the authors’ native language is

  11. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

    2012-01-01

    Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...

  12. Automatic hair detection in the wild

    DEFF Research Database (Denmark)

    Julian, Pauline; Dehais, Christophe; Lauze, Francois Bernard

    2010-01-01

    This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. Th...

  13. Automatic detection of tooth cracks in optical coherence tomography images.

    Science.gov (United States)

    Kim, Jun-Min; Kang, Se-Ryong; Yi, Won-Jin

    2017-02-01

    The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than 100 μm. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

  14. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

    Sorensen, Thomas Lynggaard; Olsen, Ulrich L.; Conradsen, Isa

    2010-01-01

    An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose...

  15. Automatic food detection in egocentric images using artificial intelligence technology

    Science.gov (United States)

    Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

  16. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

    are considered weeds. We have used a Sugar beet field as a case for evaluating the proposed crop detection method. The suggested image processing consists of: 1) locating vegetation regions in the image by thresholding the excess green image derived from the orig- inal image, 2) calculate the Hough transform......Images from Unmanned Aerial Vehicles can provide information about the weed distribution in fields. A direct way is to quantify the amount of vegetation present in different areas of the field. The limitation of this approach is that it includes both crops and weeds in the reported num- bers. To get...... of the segmented image 3) determine the dominating crop row direction by analysing output from the Hough transform and 4) use the found crop row direction to locate crop rows....

  17. Comparing automatically detected reflective texts with human judgements

    OpenAIRE

    Ullmann, Thomas Daniel; Wild, Fridolin; Scott, Peter

    2012-01-01

    This paper reports on the descriptive results of an experiment comparing automatically detected reflective and not-reflective texts against human judgements. Based on the theory of reflective writing assessment and their operationalisation five elements of reflection were defined. For each element of reflection a set of indicators was developed, which automatically annotate texts regarding reflection based on the parameterisation with authoritative texts. Using a large blog corpus 149 texts were retr...

  18. Child vocalization composition as discriminant information for automatic autism detection.

    Science.gov (United States)

    Xu, Dongxin; Gilkerson, Jill; Richards, Jeffrey; Yapanel, Umit; Gray, Sharmi

    2009-01-01

    Early identification is crucial for young children with autism to access early intervention. The existing screens require either a parent-report questionnaire and/or direct observation by a trained practitioner. Although an automatic tool would benefit parents, clinicians and children, there is no automatic screening tool in clinical use. This study reports a fully automatic mechanism for autism detection/screening for young children. This is a direct extension of the LENA (Language ENvironment Analysis) system, which utilizes speech signal processing technology to analyze and monitor a child's natural language environment and the vocalizations/speech of the child. It is discovered that child vocalization composition contains rich discriminant information for autism detection. By applying pattern recognition and machine learning approaches to child vocalization composition data, accuracy rates of 85% to 90% in cross-validation tests for autism detection have been achieved at the equal-error-rate (EER) point on a data set with 34 children with autism, 30 language delayed children and 76 typically developing children. Due to its easy and automatic procedure, it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

  19. System for automatic detection of lung nodules exhibiting growth

    Science.gov (United States)

    Novak, Carol L.; Shen, Hong; Odry, Benjamin L.; Ko, Jane P.; Naidich, David P.

    2004-05-01

    Lung nodules that exhibit growth over time are considered highly suspicious for malignancy. We present a completely automated system for detection of growing lung nodules, using initial and follow-up multi-slice CT studies. The system begins with automatic detection of lung nodules in the later CT study, generating a preliminary list of candidate nodules. Next an automatic system for registering locations in two studies matches each candidate in the later study to its corresponding position in the earlier study. Then a method for automatic segmentation of lung nodules is applied to each candidate and its matching location, and the computed volumes are compared. The output of the system is a list of nodule candidates that are new or have exhibited volumetric growth since the previous scan. In a preliminary test of 10 patients examined by two radiologists, the automatic system identified 18 candidates as growing nodules. 7 (39%) of these corresponded to validated nodules or other focal abnormalities that exhibited growth. 4 of the 7 true detections had not been identified by either of the radiologists during their initial examinations of the studies. This technique represents a powerful method of surveillance that may reduce the probability of missing subtle or early malignant disease.

  20. Automatic detection of AutoPEEP during controlled mechanical ventilation

    Directory of Open Access Journals (Sweden)

    Nguyen Quang-Thang

    2012-06-01

    Full Text Available Abstract Background Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow does not return to zero before the next inspiration, AutoPEEP is present. This simple detection however requires the eye of an expert clinician at the patient’s bedside. An automatic detection of AutoPEEP should be helpful to optimize care. Methods In this paper, a platform for automatic detection of AutoPEEP based on the flow signal available on most of recent mechanical ventilators is introduced. The detection algorithms are developed on the basis of robust non-parametric hypothesis testings that require no prior information on the signal distribution. In particular, two detectors are proposed: one is based on SNT (Signal Norm Testing and the other is an extension of SNT in the sequential framework. The performance assessment was carried out on a respiratory system analog and ex-vivo on various retrospectively acquired patient curves. Results The experiment results have shown that the proposed algorithm provides relevant AutoPEEP detection on both simulated and real data. The analysis of clinical data has shown that the proposed detectors can be used to automatically detect AutoPEEP with an accuracy of 93% and a recall (sensitivity of 90%. Conclusions The proposed platform provides an automatic early detection of AutoPEEP. Such functionality can be integrated in the currently used mechanical ventilator for continuous monitoring of the patient-ventilator interface and, therefore, alleviate the clinician task.

  1. Automatic detection and severity measurement of eczema using image processing.

    Science.gov (United States)

    Alam, Md Nafiul; Munia, Tamanna Tabassum Khan; Tavakolian, Kouhyar; Vasefi, Fartash; MacKinnon, Nick; Fazel-Rezai, Reza

    2016-08-01

    Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming. In this paper, an automatic eczema detection and severity measurement model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature. Then the model automatically measures skin parameters used in the most common assessment tool called "Eczema Area and Severity Index (EASI)," by computing eczema affected area score, eczema intensity score, and body region score of eczema allowing both patients and physicians to accurately assess the affected skin.

  2. Automatic detection and visualisation of MEG ripple oscillations in epilepsy

    Directory of Open Access Journals (Sweden)

    Nicole van Klink

    2017-01-01

    Full Text Available High frequency oscillations (HFOs, 80–500 Hz in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

  3. Comparing Automatic CME Detections in Multiple LASCO and SECCHI Catalogs

    International Nuclear Information System (INIS)

    Hess, Phillip; Colaninno, Robin C.

    2017-01-01

    With the creation of numerous automatic detection algorithms, a number of different catalogs of coronal mass ejections (CMEs) spanning the entirety of the Solar and Heliospheric Observatory ( SOHO ) Large Angle Spectrometric Coronagraph (LASCO) mission have been created. Some of these catalogs have been further expanded for use on data from the Solar Terrestrial Earth Observatory ( STEREO ) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) as well. We compare the results from different automatic detection catalogs (Solar Eruption Event Detection System (SEEDS), Computer Aided CME Tracking (CACTus), and Coronal Image Processing (CORIMP)) to ensure the consistency of detections in each. Over the entire span of the LASCO catalogs, the automatic catalogs are well correlated with one another, to a level greater than 0.88. Focusing on just periods of higher activity, these correlations remain above 0.7. We establish the difficulty in comparing detections over the course of LASCO observations due to the change in the instrument image cadence in 2010. Without adjusting catalogs for the cadence, CME detection rates show a large spike in cycle 24, despite a notable drop in other indices of solar activity. The output from SEEDS, using a consistent image cadence, shows that the CME rate has not significantly changed relative to sunspot number in cycle 24. These data, and mass calculations from CORIMP, lead us to conclude that any apparent increase in CME rate is a result of the change in cadence. We study detection characteristics of CMEs, discussing potential physical changes in events between cycles 23 and 24. We establish that, for detected CMEs, physical parameters can also be sensitive to the cadence.

  4. Comparing Automatic CME Detections in Multiple LASCO and SECCHI Catalogs

    Energy Technology Data Exchange (ETDEWEB)

    Hess, Phillip [NRC Research Associate, U.S. Naval Research Laboratory, Washington, DC (United States); Colaninno, Robin C., E-mail: phillip.hess.ctr@nrl.navy.mil, E-mail: robin.colaninno@nrl.navy.mil [U.S. Naval Research Laboratory, Washington, DC (United States)

    2017-02-10

    With the creation of numerous automatic detection algorithms, a number of different catalogs of coronal mass ejections (CMEs) spanning the entirety of the Solar and Heliospheric Observatory ( SOHO ) Large Angle Spectrometric Coronagraph (LASCO) mission have been created. Some of these catalogs have been further expanded for use on data from the Solar Terrestrial Earth Observatory ( STEREO ) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) as well. We compare the results from different automatic detection catalogs (Solar Eruption Event Detection System (SEEDS), Computer Aided CME Tracking (CACTus), and Coronal Image Processing (CORIMP)) to ensure the consistency of detections in each. Over the entire span of the LASCO catalogs, the automatic catalogs are well correlated with one another, to a level greater than 0.88. Focusing on just periods of higher activity, these correlations remain above 0.7. We establish the difficulty in comparing detections over the course of LASCO observations due to the change in the instrument image cadence in 2010. Without adjusting catalogs for the cadence, CME detection rates show a large spike in cycle 24, despite a notable drop in other indices of solar activity. The output from SEEDS, using a consistent image cadence, shows that the CME rate has not significantly changed relative to sunspot number in cycle 24. These data, and mass calculations from CORIMP, lead us to conclude that any apparent increase in CME rate is a result of the change in cadence. We study detection characteristics of CMEs, discussing potential physical changes in events between cycles 23 and 24. We establish that, for detected CMEs, physical parameters can also be sensitive to the cadence.

  5. Automatic Detection of Vehicles Using Intensity Laser and Anaglyph Image

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

    Full Text Available In this work is presented a methodology to automatic car detection motion presents in digital aerial image on urban area using intensity, anaglyph and subtracting images. The anaglyph image is used to identify the motion cars on the expose take, because the cars provide red color due the not homology between objects. An implicit model was developed to provide a digital pixel value that has the specific propriety presented early, using the ratio between the RGB color of car object in the anaglyph image. The intensity image is used to decrease the false positive and to do the processing to work into roads and streets. The subtracting image is applied to decrease the false positives obtained due the markings road. The goal of this paper is automatically detect motion cars presents in digital aerial image in urban areas. The algorithm implemented applies normalization on the left and right images and later form the anaglyph with using the translation. The results show the applicability of proposed method and it potentiality on the automatic car detection and presented the performance of proposed methodology.

  6. Automatic detection and classification of leukocytes using convolutional neural networks.

    Science.gov (United States)

    Zhao, Jianwei; Zhang, Minshu; Zhou, Zhenghua; Chu, Jianjun; Cao, Feilong

    2017-08-01

    The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

  7. Corpus analysis and automatic detection of emotion-including keywords

    Science.gov (United States)

    Yuan, Bo; He, Xiangqing; Liu, Ying

    2013-12-01

    Emotion words play a vital role in many sentiment analysis tasks. Previous research uses sentiment dictionary to detect the subjectivity or polarity of words. In this paper, we dive into Emotion-Inducing Keywords (EIK), which refers to the words in use that convey emotion. We first analyze an emotion corpus to explore the pragmatic aspects of EIK. Then we design an effective framework for automatically detecting EIK in sentences by utilizing linguistic features and context information. Our system outperforms traditional dictionary-based methods dramatically in increasing Precision, Recall and F1-score.

  8. Automatic Hookworm Detection in Wireless Capsule Endoscopy Images.

    Science.gov (United States)

    Wu, Xiao; Chen, Honghan; Gan, Tao; Chen, Junzhou; Ngo, Chong-Wah; Peng, Qiang

    2016-07-01

    Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique to examine inflammatory bowel diseases and disorders. As one of the most common human helminths, hookworm is a kind of small tubular structure with grayish white or pinkish semi-transparent body, which is with a number of 600 million people infection around the world. Automatic hookworm detection is a challenging task due to poor quality of images, presence of extraneous matters, complex structure of gastrointestinal, and diverse appearances in terms of color and texture. This is the first few works to comprehensively explore the automatic hookworm detection for WCE images. To capture the properties of hookworms, the multi scale dual matched filter is first applied to detect the location of tubular structure. Piecewise parallel region detection method is then proposed to identify the potential regions having hookworm bodies. To discriminate the unique visual features for different components of gastrointestinal, the histogram of average intensity is proposed to represent their properties. In order to deal with the problem of imbalance data, Rusboost is deployed to classify WCE images. Experiments on a diverse and large scale dataset with 440 K WCE images demonstrate that the proposed approach achieves a promising performance and outperforms the state-of-the-art methods. Moreover, the high sensitivity in detecting hookworms indicates the potential of our approach for future clinical application.

  9. An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

    Directory of Open Access Journals (Sweden)

    Qiongjie Wang

    2017-05-01

    Full Text Available The application potential of very high resolution (VHR remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.

  10. A Novel Cascade Classifier for Automatic Microcalcification Detection.

    Directory of Open Access Journals (Sweden)

    Seung Yeon Shin

    Full Text Available In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC. Our framework comprises three classification stages: i a random forest (RF classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii a more complex discriminative restricted Boltzmann machine (DRBM classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC curve for detection of clustered μCs.

  11. Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration

    Directory of Open Access Journals (Sweden)

    Jose María Armingol

    2010-03-01

    Full Text Available There are increasing applications that require precise calibration of cameras to perform accurate measurements on objects located within images, and an automatic algorithm would reduce this time consuming calibration procedure. The method proposed in this article uses a pattern similar to that of a chess board, which is found automatically in each image, when no information regarding the number of rows or columns is supplied to aid its detection. This is carried out by means of a combined analysis of two Hough transforms, image corners and invariant properties of the perspective transformation. Comparative analysis with more commonly used algorithms demonstrate the viability of the algorithm proposed, as a valuable tool for camera calibration.

  12. Skin-contact sensor for automatic fall detection.

    Science.gov (United States)

    Narasimhan, Ravi

    2012-01-01

    This paper describes an adhesive sensor system worn on the skin that automatically detects human falls. The sensor, which consists of a tri-axial accelerometer, a microcon-troller and a Bluetooth Low Energy transceiver, can be worn anywhere on a subject's torso and in any orientation. In order to distinguish easily between falls and activities of daily living (ADL), a possible fall is detected only if an impact is detected and if the subject is horizontal shortly afterwards. As an additional criterion to reduce false positives, a fall is confirmed if the user activity level several seconds after a possible fall is below a threshold. Intentional falls onto a gymnastics mat were performed by 10 volunteers (total of 297 falls); ADL were performed by 15 elderly volunteers (total of 315 ADL). The fall detection algorithm provided a sensitivity of 99% and a specificity of 100%.

  13. Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning.

    Science.gov (United States)

    Zhou, Wei; Wu, Chengdong; Chen, Dali; Wang, Zhenzhu; Yi, Yugen; Du, Wenyou

    2017-01-01

    Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

  14. AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

    Directory of Open Access Journals (Sweden)

    P. Duncan

    2012-08-01

    Full Text Available Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI, South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

  15. Automatic detection of artifacts in converted S3D video

    Science.gov (United States)

    Bokov, Alexander; Vatolin, Dmitriy; Zachesov, Anton; Belous, Alexander; Erofeev, Mikhail

    2014-03-01

    In this paper we present algorithms for automatically detecting issues specific to converted S3D content. When a depth-image-based rendering approach produces a stereoscopic image, the quality of the result depends on both the depth maps and the warping algorithms. The most common problem with converted S3D video is edge-sharpness mismatch. This artifact may appear owing to depth-map blurriness at semitransparent edges: after warping, the object boundary becomes sharper in one view and blurrier in the other, yielding binocular rivalry. To detect this problem we estimate the disparity map, extract boundaries with noticeable differences, and analyze edge-sharpness correspondence between views. We pay additional attention to cases involving a complex background and large occlusions. Another problem is detection of scenes that lack depth volume: we present algorithms for detecting at scenes and scenes with at foreground objects. To identify these problems we analyze the features of the RGB image as well as uniform areas in the depth map. Testing of our algorithms involved examining 10 Blu-ray 3D releases with converted S3D content, including Clash of the Titans, The Avengers, and The Chronicles of Narnia: The Voyage of the Dawn Treader. The algorithms we present enable improved automatic quality assessment during the production stage.

  16. Automatic detection of animals in mowing operations using thermal cameras.

    Science.gov (United States)

    Steen, Kim Arild; Villa-Henriksen, Andrés; Therkildsen, Ole Roland; Green, Ole

    2012-01-01

    During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix) or pheasant (Phasianus colchicus) are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus) and fawns of roe deer (Capreolus capreolus) to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus) and a rabbit (Oryctolagus cuniculus) in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

  17. Expert knowledge for automatic detection of bullies in social networks

    NARCIS (Netherlands)

    Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of

  18. Detection of tuberculosis by automatic cough sound analysis.

    Science.gov (United States)

    Botha, G H Renier; Theron, Grant; Warren, Rob; Klopper, Marisa; Dheda, Kheertan; van Helden, Paul; Niesler, Thomas R

    2018-03-15

    Globally, tuberculosis (TB) remains one of the most deadly diseases. Although several effective diagnosis methods exist, in lower income countries clinics may not be in a position to afford expensive equipment and employ the trained experts needed to interpret results. In these situations, symptoms including cough are commonly used to identify patients for testing. However, self-reported cough has suboptimal sensitivity and specificity, which may be improved by digital detection. This study investigates a simple and easily applied method for TB screening based on the automatic analysis of coughing sounds. A database of cough audio recordings was collected and used to develop statistical classifiers. These classifiers use short-term spectral information to automatically distinguish between the coughs of TB positive and TB negative patients with an accuracy of 78% and an AUC of 0.95. When a set of five clinical measurements is available in addition to the audio, this accuracy improves to 82%. By choosing an appropriate decision threshold, the system can achieve a sensitivity of 95% at a specificity of approximately 72%. The experiments suggest that the classifiers are using some spectral information that is not perceivable by the human auditory system, and that certain frequencies are more useful for classification than others. We conclude that automatic classification of coughing sounds may represent a viable low-cost and low-complexity screening method for TB. © 2018 Institute of Physics and Engineering in Medicine.

  19. Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo; Medeiros, Fatima; Ushizima, Daniela

    2008-08-29

    Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).

  20. Automatic Constraint Detection for 2D Layout Regularization.

    Science.gov (United States)

    Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

    2016-08-01

    In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

  1. Development of an Automatic Detection Program of Halo CMEs

    Science.gov (United States)

    Choi, K.; Park, M. Y.; Kim, J.

    2017-12-01

    The front-side halo CMEs are the major cause for large geomagnetic storms. Halo CMEs can result in damage to satellites, communication, electrical transmission lines and power systems. Thus automated techniques for detecting and analysing Halo CMEs from coronagraph data are of ever increasing importance for space weather monitoring and forecasting. In this study, we developed the algorithm that can automatically detect and do image processing the Halo CMEs in the images from the LASCO C3 coronagraph on board the SOHO spacecraft. With the detection algorithm, we derived the geometric and kinematical parameters of halo CMEs, such as source location, width, actual CME speed and arrival time at 21.5 solar radii.

  2. Automatic Detection of Welding Defects using Deep Neural Network

    Science.gov (United States)

    Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang’an

    2018-01-01

    In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

  3. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong

    2015-09-18

    In this paper, we address the problem of constraint detection for layout regularization. As layout we consider a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important for digitizing plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate the layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm to automatically detect constraints. In our results, we evaluate the proposed framework on a variety of input layouts from different applications, which demonstrates our method has superior performance to the state of the art.

  4. Automatic sentence extraction for the detection of scientific paper relations

    Science.gov (United States)

    Sibaroni, Y.; Prasetiyowati, S. S.; Miftachudin, M.

    2018-03-01

    The relations between scientific papers are very useful for researchers to see the interconnection between scientific papers quickly. By observing the inter-article relationships, researchers can identify, among others, the weaknesses of existing research, performance improvements achieved to date, and tools or data typically used in research in specific fields. So far, methods that have been developed to detect paper relations include machine learning and rule-based methods. However, a problem still arises in the process of sentence extraction from scientific paper documents, which is still done manually. This manual process causes the detection of scientific paper relations longer and inefficient. To overcome this problem, this study performs an automatic sentences extraction while the paper relations are identified based on the citation sentence. The performance of the built system is then compared with that of the manual extraction system. The analysis results suggested that the automatic sentence extraction indicates a very high level of performance in the detection of paper relations, which is close to that of manual sentence extraction.

  5. 46 CFR 161.002-8 - Automatic fire detecting systems, general requirements.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting systems, general requirements... Systems § 161.002-8 Automatic fire detecting systems, general requirements. (a) General. An automatic fire... combined with other power failure alarm systems when specifically approved. (b) [Reserved] [21 FR 9032, Nov...

  6. Automatic behavior sensing for a bomb-detecting dog

    Science.gov (United States)

    Nguyen, Hoa G.; Nans, Adam; Talke, Kurt; Candela, Paul; Everett, H. R.

    2015-05-01

    Bomb-detecting dogs are trained to detect explosives through their sense of smell and often perform a specific behavior to indicate a possible bomb detection. This behavior is noticed by the dog handler, who confirms the probable explosives, determines the location, and forwards the information to an explosive ordnance disposal (EOD) team. To improve the speed and accuracy of this process and better integrate it with the EOD team's robotic explosive disposal operation, SPAWAR Systems Center Pacific has designed and prototyped an electronic dog collar that automatically tracks the dog's location and attitude, detects the indicative behavior, and records the data. To account for the differences between dogs, a 5-minute training routine can be executed before the mission to establish initial values for the k-mean clustering algorithm that classifies a specific dog's behavior. The recorded data include GPS location of the suspected bomb, the path the dog took to approach this location, and a video clip covering the detection event. The dog handler reviews and confirms the data before it is packaged up and forwarded on to the EOD team. The EOD team uses the video clip to better identify the type of bomb and for awareness of the surrounding environment before they arrive at the scene. Before the robotic neutralization operation commences at the site, the location and path data (which are supplied in a format understandable by the next-generation EOD robots—the Advanced EOD Robotic System) can be loaded into the robotic controller to automatically guide the robot to the bomb site. This paper describes the project with emphasis on the dog-collar hardware, behavior-classification software, and feasibility testing.

  7. ARCOCT: Automatic detection of lumen border in intravascular OCT images.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

    2017-11-01

    Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

  8. Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

    Science.gov (United States)

    Sentana, I. W. B.; Jawas, N.; Asri, S. A.

    2018-01-01

    Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

  9. Automatic particle detection in microscopy using temporal correlations.

    Science.gov (United States)

    Röding, Magnus; Deschout, Hendrik; Martens, Thomas; Notelaers, Kristof; Hofkens, Johan; Ameloot, Marcel; Braeckmans, Kevin; Särkkä, Aila; Rudemo, Mats

    2013-10-01

    One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms. Copyright © 2013 Wiley Periodicals, Inc.

  10. Machine learning-based automatic detection of pulmonary trunk

    Science.gov (United States)

    Wu, Hong; Deng, Kun; Liang, Jianming

    2011-03-01

    Pulmonary embolism is a common cardiovascular emergency with about 600,000 cases occurring annually and causing approximately 200,000 deaths in the US. CT pulmonary angiography (CTPA) has become the reference standard for PE diagnosis, but the interpretation of these large image datasets is made complex and time consuming by the intricate branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus of contrast and inhomogeneities with the pulmonary arterial blood pool. To meet this challenge, several approaches for computer aided diagnosis of PE in CTPA have been proposed. However, none of these approaches is capable of detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans. To overcome these shortcomings, it requires highly efficient and accurate identification of the pulmonary trunk. For this very purpose, in this paper, we present a machine learning based approach for automatically detecting the pulmonary trunk. Our idea is to train a cascaded AdaBoost classifier with a large number of Haar features extracted from CTPA image samples, so that the pulmonary trunk can be automatically identified by sequentially scanning the CTPA images and classifying each encountered sub-image with the trained classifier. Our approach outperforms an existing anatomy-based approach, requiring no explicit representation of anatomical knowledge and achieving a nearly 100% accuracy tested on a large number of cases.

  11. BgCut: automatic ship detection from UAV images.

    Science.gov (United States)

    Xu, Chao; Zhang, Dongping; Zhang, Zhengning; Feng, Zhiyong

    2014-01-01

    Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

  12. Automatic detection and decoding of honey bee waggle dances.

    Directory of Open Access Journals (Sweden)

    Fernando Wario

    Full Text Available The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°, well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.

  13. Automatic Detection of Animals in Mowing Operations Using Thermal Cameras

    Directory of Open Access Journals (Sweden)

    Ole Green

    2012-06-01

    Full Text Available During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix or pheasant (Phasianus colchicus are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus and fawns of roe deer (Capreolus capreolus to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus and a rabbit (Oryctolagus cuniculus in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

  14. Automatic correspondence detection in mammogram and breast tomosynthesis images

    Science.gov (United States)

    Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

    2012-02-01

    Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for

  15. Automatic Road Gap Detection Using Fuzzy Inference System

    Science.gov (United States)

    Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

    2011-09-01

    Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  16. Automatic detecting method of LED signal lamps on fascia based on color image

    Science.gov (United States)

    Peng, Xiaoling; Hou, Wenguang; Ding, Mingyue

    2009-10-01

    Instrument display panel is one of the most important parts of automobiles. Automatic detection of LED signal lamps is critical to ensure the reliability of automobile systems. In this paper, an automatic detection method was developed which is composed of three parts in the automatic detection: the shape of LED lamps, the color of LED lamps, and defect spots inside the lamps. More than hundreds of fascias were detected with the automatic detection algorithm. The speed of the algorithm is quite fast and satisfied with the real-time request of the system. Further, the detection result was demonstrated to be stable and accurate.

  17. An automatic detection software for differential reflection spectroscopy

    Science.gov (United States)

    Yuksel, Seniha Esen; Dubroca, Thierry; Hummel, Rolf E.; Gader, Paul D.

    2012-06-01

    Recent terrorist attacks have sprung a need for a large scale explosive detector. Our group has developed differential reflection spectroscopy which can detect explosive residue on surfaces such as parcel, cargo and luggage. In short, broad band ultra-violet and visible light is shone onto a material (such as a parcel) moving on a conveyor belt. Upon reflection off the surface, the light intensity is recorded with a spectrograph (spectrometer in combination with a CCD camera). This reflected light intensity is then subtracted and normalized with the next data point collected, resulting in differential reflection spectra in the 200-500 nm range. Explosives show spectral finger-prints at specific wavelengths, for example, the spectrum of 2,4,6, trinitrotoluene (TNT) shows an absorption edge at 420 nm. Additionally, we have developed an automated software which detects the characteristic features of explosives. One of the biggest challenges for the algorithm is to reach a practical limit of detection. In this study, we introduce our automatic detection software which is a combination of principal component analysis and support vector machines. Finally we present the sensitivity and selectivity response of our algorithm as a function of the amount of explosive detected on a given surface.

  18. Automatic Detection of Cortical Bones Haversian Osteonal Boundaries

    Directory of Open Access Journals (Sweden)

    Ilige Hage

    2015-10-01

    Full Text Available This work aims to automatically detect cement lines in decalcified cortical bone sections stained with H&E. Employed is a methodology developed previously by the authors and proven to successfully count and disambiguate the micro-architectural features (namely Haversian canals, canaliculi, and osteocyte lacunae present in the secondary osteons/Haversian system (osteon of cortical bone. This methodology combines methods typically considered separately, namely pulse coupled neural networks (PCNN, particle swarm optimization (PSO, and adaptive threshold (AT. In lieu of human bone, slides (at 20× magnification from bovid cortical bone are used in this study as proxy of human bone. Having been characterized, features with same orientation are used to detect the cement line viewed as the next coaxial layer adjacent to the outermost lamella of the osteon. Employed for this purpose are three attributes for each and every micro-sized feature identified in the osteon lamellar system: (1 orientation, (2 size (ellipse perimeter and (3 Euler number (a topological measure. From a training image, automated parameters for the PCNN network are obtained by forming fitness functions extracted from these attributes. It is found that a 3-way combination of these features attributes yields good representations of the overall osteon boundary (cement line. Near-unity values of classical metrics of quality (precision, sensitivity, specificity, accuracy, and dice suggest that the segments obtained automatically by the optimized artificial intelligent methodology are of high fidelity as compared with manual tracing. For bench marking, cement lines segmented by k-means did not fare as well. An analysis based on the modified Hausdorff distance (MHD of the segmented cement lines also testified to the quality of the detected cement lines vis-a-vis the k-means method.

  19. Automatic food detection in egocentric images using artificial intelligence technology.

    Science.gov (United States)

    Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

    2018-03-26

    To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

  20. An Automatic Cloud Detection Method for ZY-3 Satellite

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

    Full Text Available Automatic cloud detection for optical satellite remote sensing images is a significant step in the production system of satellite products. For the browse images cataloged by ZY-3 satellite, the tree discriminate structure is adopted to carry out cloud detection. The image was divided into sub-images and their features were extracted to perform classification between clouds and grounds. However, due to the high complexity of clouds and surfaces and the low resolution of browse images, the traditional classification algorithms based on image features are of great limitations. In view of the problem, a prior enhancement processing to original sub-images before classification was put forward in this paper to widen the texture difference between clouds and surfaces. Afterwards, with the secondary moment and first difference of the images, the feature vectors were extended in multi-scale space, and then the cloud proportion in the image was estimated through comprehensive analysis. The presented cloud detection algorithm has already been applied to the ZY-3 application system project, and the practical experiment results indicate that this algorithm is capable of promoting the accuracy of cloud detection significantly.

  1. Automatic detection of micronuclei by cell microscopic image processing.

    Science.gov (United States)

    Bahreyni Toossi, Mohammad Taghi; Azimian, Hosein; Sarrafzadeh, Omid; Mohebbi, Shokoufeh; Soleymanifard, Shokouhozaman

    2017-12-01

    With the development and applications of ionizing radiation in medicine, the radiation effects on human health get more and more attention. Ionizing radiation can lead to various forms of cytogenetic damage, including increased frequencies of micronuclei (MNi) and chromosome abnormalities. The cytokinesis block micronucleus (CBMN) assay is widely used method for measuring MNi to determine chromosome mutations or genome instability in cultured human lymphocytes. The visual scoring of MNi is time-consuming and scorer fatigue can lead to inconsistency. In this work, we designed software for the scoring of in vitro CBMN assay for biomonitoring on Giemsa-stained slides that overcome many previous limitations. Automatic scoring proceeds in four stages as follows. First, overall segmentation of nuclei is done. Then, binucleated (BN) cells are detected. Next, the entire cell is estimated for each BN as it is assumed that there is no detectable cytoplasm. Finally, MNi are detected within each BN cell. The designed Software is even able to detect BN cells with vague cytoplasm and MNi in peripheral blood smear. Our system is tested on a self-provided dataset and is achieved high sensitivities of about 98% and 82% in recognizing BN cells and MNi, respectively. Moreover, in our study less than 1% false positives were observed that makes our system reliable for practical MNi scoring. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Automatic detection of clustered microcalcifications in digitized mammogram films

    Science.gov (United States)

    Yu, Songyang; Guan, Ling; Brown, Stephen

    1999-01-01

    The existence of clustered microcalcifications is one of the important early signs of breast cancer. This paper presents an image processing procedure for the automatic detection of clustered microcalcifications in digitized mammograms. In particular, a sensitivity range of around one false positive per image is targeted. The proposed method consists of two main steps. First, possible microcalcification pixels in the mammograms are segmented out using wavelet features or both wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using the structure features extracted from the potential microcalcification objects. The classifiers used in these two steps are feedforward neutral networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free response operating characteristics curve is used to evaluate the performance. Results show that the proposed procedure gives quite satisfactory detection performance. In particular, a 93% mean true positive detection rate is achieved at the price of one false positive per image when both wavelet features and gray level statistical features are used in the first step.

  3. Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation

    Directory of Open Access Journals (Sweden)

    Wisnu Jatmiko

    2016-08-01

    Full Text Available Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from theGLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithmand improve the classification performance. The algorithm is testedagainst MIT-BIH arrhythmia database to measure theperformance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection,FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.

  4. Automatic ictal HFO detection for determination of initial seizure spread.

    Science.gov (United States)

    Graef, Andreas; Flamm, Christoph; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred; Matz, Gerald

    2013-01-01

    High-frequency oscillations (HFOs) are a reliable indicator for the epileptic seizure onset zone (SOZ) in ECoG recordings. We propose a novel method for the automatic detection of ictal HFOs in the ripple band (80-250 Hz) based on CFAR matched sub-space filtering. This allows to track the early propagation of ictal HFOs, revealing initial and follow-up epileptic activity on the electrodes. We apply this methodology to two seizures from one patient suffering from focal epilepsy. The electrodes identified are in very good accordance with the visual HFO analysis by clinicians. Furthermore the electrodes with initial HFO activity are correlated well with the SOZ (conventional v-activity).

  5. A Robust Automatic Ionospheric O/X Mode Separation Technique for Vertical Incidence Sounders

    Science.gov (United States)

    Harris, T. J.; Pederick, L. H.

    2017-12-01

    The sounding of the ionosphere by a vertical incidence sounder (VIS) is the oldest and most common technique for determining the state of the ionosphere. The automatic extraction of relevant ionospheric parameters from the ionogram image, referred to as scaling, is important for the effective utilization of data from large ionospheric sounder networks. Due to the Earth's magnetic field, the ionosphere is birefringent at radio frequencies, so a VIS will typically see two distinct returns for each frequency. For the automatic scaling of ionograms, it is highly desirable to be able to separate the two modes. Defence Science and Technology Group has developed a new VIS solution which is based on direct digital receiver technology and includes an algorithm to separate the O and X modes. This algorithm can provide high-quality separation even in difficult ionospheric conditions. In this paper we describe the algorithm and demonstrate its consistency and reliability in successfully separating 99.4% of the ionograms during a 27 day experimental campaign under sometimes demanding ionospheric conditions.

  6. Traffic incidents in motorways : An empirical proposal for incident detection using data from mobile phone operators

    NARCIS (Netherlands)

    Steenbruggen, John; Tranos, Emmanouil; Rietveld, P.

    2016-01-01

    This paper proves that mobile phone usage data is an easy to use, cheap and most importantly, reliable predictor of motorway incidents. Using econometric modelling, this paper provides a proof of concept of how mobile phone usage data can be utilised to detect motorway incidents. Greater Amsterdam

  7. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

  8. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

    Science.gov (United States)

    Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

    2017-10-15

    Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

  9. Automatic age-related macular degeneration detection and staging

    Science.gov (United States)

    van Grinsven, Mark J. J. P.; Lechanteur, Yara T. E.; van de Ven, Johannes P. H.; van Ginneken, Bram; Theelen, Thomas; Sánchez, Clara I.

    2013-03-01

    Age-related macular degeneration (AMD) is a degenerative disorder of the central part of the retina, which mainly affects older people and leads to permanent loss of vision in advanced stages of the disease. AMD grading of non-advanced AMD patients allows risk assessment for the development of advanced AMD and enables timely treatment of patients, to prevent vision loss. AMD grading is currently performed manually on color fundus images, which is time consuming and expensive. In this paper, we propose a supervised classification method to distinguish patients at high risk to develop advanced AMD from low risk patients and provide an exact AMD stage determination. The method is based on the analysis of the number and size of drusen on color fundus images, as drusen are the early characteristics of AMD. An automatic drusen detection algorithm is used to detect all drusen. A weighted histogram of the detected drusen is constructed to summarize the drusen extension and size and fed into a random forest classifier in order to separate low risk from high risk patients and to allow exact AMD stage determination. Experiments showed that the proposed method achieved similar performance as human observers in distinguishing low risk from high risk AMD patients, obtaining areas under the Receiver Operating Characteristic curve of 0.929 and 0.934. A weighted kappa agreement of 0.641 and 0.622 versus two observers were obtained for AMD stage evaluation. Our method allows for quick and reliable AMD staging at low costs.

  10. 46 CFR 161.002-10 - Automatic fire detecting system control unit.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting system control unit. 161.002-10...-10 Automatic fire detecting system control unit. (a) General. The fire detecting system control unit... inches) or other audible alarm that has an equivalent sound level and that is mounted at the control unit...

  11. Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

    Directory of Open Access Journals (Sweden)

    Annalisa Milella

    2012-09-01

    Full Text Available Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

  12. Vortex flows in the solar chromosphere. I. Automatic detection method

    Science.gov (United States)

    Kato, Y.; Wedemeyer, S.

    2017-05-01

    Solar "magnetic tornadoes" are produced by rotating magnetic field structures that extend from the upper convection zone and the photosphere to the corona of the Sun. Recent studies show that these kinds of rotating features are an integral part of atmospheric dynamics and occur on a large range of spatial scales. A systematic statistical study of magnetic tornadoes is a necessary next step towards understanding their formation and their role in mass and energy transport in the solar atmosphere. For this purpose, we develop a new automatic detection method for chromospheric swirls, meaning the observable signature of solar tornadoes or, more generally, chromospheric vortex flows and rotating motions. Unlike existing studies that rely on visual inspections, our new method combines a line integral convolution (LIC) imaging technique and a scalar quantity that represents a vortex flow on a two-dimensional plane. We have tested two detection algorithms, based on the enhanced vorticity and vorticity strength quantities, by applying them to three-dimensional numerical simulations of the solar atmosphere with CO5BOLD. We conclude that the vorticity strength method is superior compared to the enhanced vorticity method in all aspects. Applying the method to a numerical simulation of the solar atmosphere reveals very abundant small-scale, short-lived chromospheric vortex flows that have not been found previously by visual inspection.

  13. Automatic detection of interictal spikes using data mining models.

    Science.gov (United States)

    Valenti, Pablo; Cazamajou, Enrique; Scarpettini, Marcelo; Aizemberg, Ariel; Silva, Walter; Kochen, Silvia

    2006-01-15

    A prospective candidate for epilepsy surgery is studied both the ictal and interictal spikes (IS) to determine the localization of the epileptogenic zone. In this work, data mining (DM) classification techniques were utilized to build an automatic detection model. The selected DM algorithms are: Decision Trees (J 4.8), and Statistical Bayesian Classifier (naïve model). The main objective was the detection of IS, isolating them from the EEG's base activity. On the other hand, DM has an attractive advantage in such applications, in that the recognition of epileptic discharges does not need a clear definition of spike morphology. Furthermore, previously 'unseen' patterns could be recognized by the DM with proper 'training'. The results obtained showed that the efficacy of the selected DM algorithms is comparable to the current visual analysis used by the experts. Moreover, DM is faster than the time required for the visual analysis of the EEG. So this tool can assist the experts by facilitating the analysis of a patient's information, and reducing the time and effort required in the process.

  14. The application of automatic chemiluminescence machine in rapid immune detection

    International Nuclear Information System (INIS)

    Lin Aizhen; Li Xuanwei; Chen Binhong; Li Zhenqian; Chen Zhaoxuan

    2004-01-01

    Objective: To provide high-quality, rapid and dependable result for clinical practice, and give satisfactory service to patients of different economical status by supplementation with other labeling immune examination. With an innovative attitude, we carried out efficient technical reform on ACS180 automatic chemiluminescence machine, making it possible for patients to complete the whole process including examination, check-out, diagnosis and getting drugs. The reported will be issued within an hour, thus a rapid immune detection service was established in out-patients department. Methods: 1. ACS-180 automatic chemiluminescence machine is used based on the principle of chemiluminescence immune methods. 2. The reagents are provided by Ciba-Comig Company of USA, composed of anti acridinium ester antibody of liquid phase and particulate antigen of solid phase wrapped in magnetic powder. 3. Calibration and quality control: high and low concentration are set for each calibration fluid with attached standard curve. Product for quality controlling includes three concentration of low, moderate and high. Results: 1. rapid machine detection for sample: serum is replaced with plasma coagulated by heparin, and comparison among series of methods using serum or plasma suggest no significant difference exists. 2. The problem about fasting detection: chemiluminescence machine measure optical density directly, with the results hardly being influenced by turbidity. But attention should be paid to the treatment of lipid turbid samples. 3. Other innovations: (1) direct placement of sample tube on machine: a cushion is placed on sample plate to transfer sample to machine directly after centrifugation, saving time and reducing the accident in sample transference. (2) for HCG quantification in blood and urine, 'gold criteria' is used firstly in screening to determine approximately the dilution range, with an advantage of saving time and reagent as well as accuracy. (3) we design a

  15. Automatic delamination defect detection in radiographic sequence of rocket boosters

    International Nuclear Information System (INIS)

    Rebuffel, V.; Pires, S.; Caplier, A.; Lamarque, P.

    2003-01-01

    Solid rocket motors are routinely examined in real-time X-ray radioscopic mode. The large and cylindrical boosters are rotating between a high energy source and a two dimensional detector. The purpose of this control is to detect possible defects all through the sample. In the tangential configuration, the part of the object that intersects the X-rays beam is the peripheral one, allowing to detect the delamination defect between the propellant and the external metal envelope. But the defect detectability is very poor due to the strong attenuation of the X-rays through the motors. During the rotation of the booster, the system acquires a sequence of radiographs where the defects are visible over several successive instants. We have previously developed a real-time tomo-synthesis system, processing the radiographs on line, and based on a tomo-synthesis reconstruction algorithm in order to improve the signal-to-noise ratio. This system is installed at the industrial site of Kourou, and is currently used by the operators in charge of the visual inspection of the boosters. In this paper, we present a method that processes the digital images obtained by the system in the purpose of automatically extracting the delamination defects. Due to the size and the poor contrast of the defects, a single image is not sufficient to perform this detection. A spatio-temporal aspect is required for the algorithm to be robust and efficient. In a first step, the proposed method computes the apparent local displacement between the current radiograph and a reference one. This reference image is acquired at the beginning of the rotation, with few noise, and is supposed to be defect free. The apparent displacement is due to the non-perfect rotation positioning. It may be uniform or not, depending on the deformation of the insulation liner of the metallic wall. The images are then registered and compared. On the resulting difference image we apply a smoothed threshold to obtain an

  16. Automatic detection of blurred images in UAV image sets

    Science.gov (United States)

    Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

    2016-12-01

    Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of

  17. Acoustics of snoring and automatic snore sound detection in children.

    Science.gov (United States)

    Çavuşoğlu, M; Poets, C F; Urschitz, M S

    2017-10-31

    Acoustic analyses of snoring sounds have been used to objectively assess snoring and applied in various clinical problems for adult patients. Such studies require highly automatized tools to analyze the sound recordings of the whole night's sleep, in order to extract clinically relevant snore- related statistics. The existing techniques and software used for adults are not efficiently applicable to snoring sounds in children, basically because of different acoustic signal properties. In this paper, we present a broad range of acoustic characteristics of snoring sounds in children (N  =  38) in comparison to adult (N  =  30) patients. Acoustic characteristics of the signals were calculated, including frequency domain representations, spectrogram-based characteristics, spectral envelope analysis, formant structures and loudness of the snoring sounds. We observed significant differences in spectral features, formant structures and loudness of the snoring signals of children compared to adults that may arise from the diversity of the upper airway anatomy as the principal determinant of the snore sound generation mechanism. Furthermore, based on the specific audio features of snoring children, we proposed a novel algorithm for the automatic detection of snoring sounds from ambient acoustic data specifically in a pediatric population. The respiratory sounds were recorded using a pair of microphones and a multi-channel data acquisition system simultaneously with full-night polysomnography during sleep. Brief sound chunks of 0.5 s were classified as either belonging to a snoring event or not with a multi-layer perceptron, which was trained in a supervised fashion using stochastic gradient descent on a large hand-labeled dataset using frequency domain features. The method proposed here has been used to extract snore-related statistics that can be calculated from the detected snore episodes for the whole night's sleep, including number of snore episodes

  18. 46 CFR 161.002-9 - Automatic fire detecting system, power supply.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting system, power supply. 161.002-9 Section 161.002-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) EQUIPMENT, CONSTRUCTION, AND MATERIALS: SPECIFICATIONS AND APPROVAL ELECTRICAL EQUIPMENT Fire-Protective Systems § 161.002-9 Automatic fire detecting system, power...

  19. Automatic Detection of Inactive Solar Cell Cracks in Electroluminescence Images

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

    We propose an algorithm for automatic determination of the electroluminescence (EL) signal threshold level corresponding to inactive solar cell cracks, resulting from their disconnection from the electrical circuit of the cell. The method enables automatic quantification of the cell crack size an...

  20. Statistical language analysis for automatic exfiltration event detection.

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, David Gerald

    2010-04-01

    This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

  1. Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

    International Nuclear Information System (INIS)

    Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

    2007-01-01

    Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

  2. Tailoring automatic exposure control toward constant detectability in digital mammography

    Energy Technology Data Exchange (ETDEWEB)

    Salvagnini, Elena, E-mail: elena.salvagnini@uzleuven.be [Department of Imaging and Pathology, Medical Physics and Quality Assessment, KUL, Herestraat 49, Leuven B-3000, Belgium and SCK-CEN, Boeretang 200, Mol 2400 (Belgium); Bosmans, Hilde [Department of Imaging and Pathology, Medical Physics and Quality Assessment, KUL, Herestraat 49, Leuven B-3000, Belgium and Department of Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium); Struelens, Lara [SCK-CEN, Boeretang 200, Mol 2400 (Belgium); Marshall, Nicholas W. [Department of Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium)

    2015-07-15

    Purpose: The automatic exposure control (AEC) modes of most full field digital mammography (FFDM) systems are set up to hold pixel value (PV) constant as breast thickness changes. This paper proposes an alternative AEC mode, set up to maintain some minimum detectability level, with the ultimate goal of improving object detectability at larger breast thicknesses. Methods: The default “OPDOSE” AEC mode of a Siemens MAMMOMAT Inspiration FFDM system was assessed using poly(methyl methacrylate) (PMMA) of thickness 20, 30, 40, 50, 60, and 70 mm to find the tube voltage and anode/filter combination programmed for each thickness; these beam quality settings were used for the modified AEC mode. Detectability index (d′), in terms of a non-prewhitened model observer with eye filter, was then calculated as a function of tube current-time product (mAs) for each thickness. A modified AEC could then be designed in which detectability never fell below some minimum setting for any thickness in the operating range. In this study, the value was chosen such that the system met the achievable threshold gold thickness (T{sub t}) in the European guidelines for the 0.1 mm diameter disc (i.e., T{sub t} ≤ 1.10 μm gold). The default and modified AEC modes were compared in terms of contrast-detail performance (T{sub t}), calculated detectability (d′), signal-difference-to-noise ratio (SDNR), and mean glandular dose (MGD). The influence of a structured background on object detectability for both AEC modes was examined using a CIRS BR3D phantom. Computer-based CDMAM reading was used for the homogeneous case, while the images with the BR3D background were scored by human observers. Results: The default OPDOSE AEC mode maintained PV constant as PMMA thickness increased, leading to a reduction in SDNR for the homogeneous background 39% and d′ 37% in going from 20 to 70 mm; introduction of the structured BR3D plate changed these figures to 22% (SDNR) and 6% (d′), respectively

  3. Video monitoring of visible atmospheric emissions: from a manual device to a new fully automatic detection and classification device; Video surveillance des rejets atmospheriques d'un site siderurgique: d'un systeme manuel a la detection automatique

    Energy Technology Data Exchange (ETDEWEB)

    Bardet, I.; Ryckelynck, F.; Desmonts, T. [Sollac, 59 - Dunkerque (France)

    1999-11-01

    Complete text of publication follows: the context of strong local sensitivity to dust emissions from an integrated steel plant justifies the monitoring of the emissions of abnormally coloured smokes from this plant. In a first step, the watch is done 'visually' by screening and counting the puff emissions through a set of seven cameras and video recorders. The development of a new device making automatic picture analysis allowed to render the inspection automatic. The new system detects and counts the incidents and sends an alarm to the process operator. This way for automatic detection can be extended, after some tests, to other uses in the environmental field. (authors)

  4. Exploring the Thermal Limits of IR-Based Automatic Whale Detection (ETAW)

    Science.gov (United States)

    2015-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Exploring the thermal limits of IR -based automatic whale...mammal detection, consisting of an actively stabilized, spinning IR camera and an algorithm that detects whale blows on the basis of their thermal...2 OBJECTIVES This project aims at a cost-efficient test of the thermal limits of the abovementioned IR based automatic whale detection

  5. Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Yazan M. Alomari

    2014-01-01

    Full Text Available Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs and red blood cells (RBCs in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD algorithm to solve the initialization problem, detecting irregular circles (cells, selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.

  6. Automatic QRS complex detection algorithm designed for a novel wearable, wireless electrocardiogram recording device

    DEFF Research Database (Denmark)

    Saadi, Dorthe Bodholt; Egstrup, Kenneth; Branebjerg, Jens

    2012-01-01

    We have designed and optimized an automatic QRS complex detection algorithm for electrocardiogram (ECG) signals recorded with the DELTA ePatch platform. The algorithm is able to automatically switch between single-channel and multi-channel analysis mode. This preliminary study includes data from 11...

  7. Automatic centroid detection and surface measurement with a digital Shack–Hartmann wavefront sensor

    International Nuclear Information System (INIS)

    Yin, Xiaoming; Zhao, Liping; Li, Xiang; Fang, Zhongping

    2010-01-01

    With the breakthrough of manufacturing technologies, the measurement of surface profiles is becoming a big issue. A Shack–Hartmann wavefront sensor (SHWS) provides a promising technology for non-contact surface measurement with a number of advantages over interferometry. The SHWS splits the incident wavefront into many subsections and transfers the distorted wavefront detection into the centroid measurement. So the accuracy of the centroid measurement determines the accuracy of the SHWS. In this paper, we have presented a new centroid measurement algorithm based on an adaptive thresholding and dynamic windowing method by utilizing image-processing techniques. Based on this centroid detection method, we have developed a digital SHWS system which can automatically detect centroids of focal spots, reconstruct the wavefront and measure the 3D profile of the surface. The system has been tested with various simulated and real surfaces such as flat surfaces, spherical and aspherical surfaces as well as deformable surfaces. The experimental results demonstrate that the system has good accuracy, repeatability and immunity to optical misalignment. The system is also suitable for on-line applications of surface measurement

  8. Automatic Detection and Resolution of Lexical Ambiguity in Process Models

    NARCIS (Netherlands)

    Pittke, F.; Leopold, H.; Mendling, J.

    2015-01-01

    System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal

  9. Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

    Energy Technology Data Exchange (ETDEWEB)

    2018-03-25

    A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

  10. Automatic detection of frequency changes depends on auditory stimulus intensity.

    Science.gov (United States)

    Salo, S; Lang, A H; Aaltonen, O; Lertola, K; Kärki, T

    1999-06-01

    A cortical cognitive auditory evoked potential, mismatch negativity (MMN), reflects automatic discrimination and echoic memory functions of the auditory system. For this study, we examined whether this potential is dependent on the stimulus intensity. The MMN potentials were recorded from 10 subjects with normal hearing using a sine tone of 1000 Hz as the standard stimulus and a sine tone of 1141 Hz as the deviant stimulus, with probabilities of 90% and 10%, respectively. The intensities were 40, 50, 60, 70, and 80 dB HL for both standard and deviant stimuli in separate blocks. Stimulus intensity had a statistically significant effect on the mean amplitude, rise time parameter, and onset latency of the MMN. Automatic auditory discrimination seems to be dependent on the sound pressure level of the stimuli.

  11. Automatic pitch detection for a computer game interface

    International Nuclear Information System (INIS)

    Fonseca Solis, Juan M.

    2015-01-01

    A software able to recognize notes played by musical instruments is created through automatic pitch recognition. A pitch recognition algorithm is embedded into a software project, using the C implementation of SWIPEP. A memory game is chosen for project. A sequence of notes is listened and played by user to the computer, using a soprano recorder flute. The basic concepts to understand the acoustic phenomena involved are explained. The paper is aimed for all students with basic programming knowledge and want to incorporate sound processing to their projects. (author) [es

  12. Automatic detection of pleural effusion in chest radiographs

    NARCIS (Netherlands)

    Maduskar, P.; Philipsen, R.H.H.M.; Melendez Rodriguez, J.C.; Scholten, E.T.; Chanda, D.; Ayles, H.; Sanchez, C.I.; Ginneken, B. van

    2016-01-01

    Automated detection of Tuberculosis (TB) using chest radiographs (CXRs) is gaining popularity due to the lack of trained human readers in resource limited countries with a high TB burden. The majority of the computer-aided detection (CAD) systems for TB focus on detection of parenchymal

  13. Automatic Rock Detection and Mapping from HiRISE Imagery

    Science.gov (United States)

    Huertas, Andres; Adams, Douglas S.; Cheng, Yang

    2008-01-01

    This system includes a C-code software program and a set of MATLAB software tools for statistical analysis and rock distribution mapping. The major functions include rock detection and rock detection validation. The rock detection code has been evolved into a production tool that can be used by engineers and geologists with minor training.

  14. An automatized frequency analysis for vine plot detection and delineation in remote sensing

    OpenAIRE

    Delenne , Carole; Rabatel , G.; Deshayes , M.

    2008-01-01

    The availability of an automatic tool for vine plot detection, delineation, and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundary and accurate estimation of interrow width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard ver...

  15. The testing techniques of the automatics fire detection monitoring systems (A receiver and A transmitter)

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Yon Woo; Soong, Woong Sup; Kim, Kee Ha [Korea Atomic Energy Research Institute, Taejon (Korea)

    1999-04-01

    The major function of the automatic fire detection system is to use effectively the fire-fighting equipments and the shelter apparatus detecting immediately the fire and notifying the fire to a person in charge. To perform these functions, the automatic fire detection system is composed of a receiver and a transmitter which indicate the origin of a fire, sound facility, wiring and power supply. And the main purpose using this system is to stop the spread of the fire and minimize the damage of human life and properties of the facility. 12 refs., 17 figs., 11 tabs. (Author)

  16. BgCut: Automatic Ship Detection from UAV Images

    Directory of Open Access Journals (Sweden)

    Chao Xu

    2014-01-01

    foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

  17. Automatic hearing loss detection system based on auditory brainstem response

    International Nuclear Information System (INIS)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R

    2007-01-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory

  18. X-ray based stem detection in an automatic tomato weeding system

    Science.gov (United States)

    A stem detection system was developed for automatic weed control in transplanted tomato fields. A portable x-ray source projected an x-ray beam perpendicular to the crop row and parallel to the soil surface. The plant’s main stem absorbs x-ray energy, decreasing the detected signal and allowing stem...

  19. Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering.

    Science.gov (United States)

    Shi, Lin; Wang, Defeng; Liu, Wen; Fang, Kui; Wang, Yi-Xiang J; Huang, Wenhua; King, Ann D; Heng, Pheng Ann; Ahuja, Anil T

    2014-05-01

    To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37-92.10% on rat dataset and 73.18-90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency. Copyright © 2013 Wiley Periodicals, Inc.

  20. Comparative analysis of automatic approaches to building detection from multi-source aerial data

    NARCIS (Netherlands)

    Frontoni, E.; Khoshelham, K.; Nardinocchi, C.; Nedkov, S.; Zingaretti, P.

    2008-01-01

    Automatic building detection has been a hot topic since the early 1990’s. Early approaches were based on a single aerial image. Detecting buildings is a difficult task so it can be more effective when multiple sources of information are obtained and fused. The objective of this paper is to provide a

  1. Automatic solar feature detection using image processing and pattern recognition techniques

    Science.gov (United States)

    Qu, Ming

    The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Halpha full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves

  2. Automatic snoring sounds detection from sleep sounds via multi-features analysis.

    Science.gov (United States)

    Wang, Can; Peng, Jianxin; Song, Lijuan; Zhang, Xiaowen

    2017-03-01

    Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder. Snoring is the most intuitively characteristic symptom of OSAHS. Recently, many studies have attempted to develop snore analysis technology for diagnosing OSAHS. The preliminary and essential step in such diagnosis is to automatically segment snoring sounds from original sleep sounds. This study presents an automatic snoring detection algorithm that detects potential snoring episodes using an adaptive effective-value threshold method, linear and nonlinear feature extraction using maximum power ratio, sum of positive/negative amplitudes, 500 Hz power ratio, spectral entropy (SE) and sample entropy (SampEn), and automatic snore/nonsnore classification using a support vector machine. The results show that SampEn provides higher classification accuracy than SE. Furthermore, the proposed automatic detection method achieved over 94.0% accuracy when identifying snoring and nonsnoring sounds despite using small training sets. The sensitivity and accuracy of the results demonstrate that the proposed snoring detection method can effectively classify snoring and nonsnoring sounds, thus enabling the automatic detection of snoring.

  3. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Dawei Li

    2017-01-01

    Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

  4. Usage of polarisation features of landmines for improved automatic detection

    NARCIS (Netherlands)

    Jong, W. de; Cremer, F.; Schutte, K.; Storm, J.

    2000-01-01

    In this paper the landmine detection performance of an infrared and a visual light camera both equipped with a polarisation filter are compared with the detection performance of these cameras without polarisation filters. Sequences of images have been recorded with in front of these cameras a

  5. Automatic fog detection for public safety by using camera images

    Science.gov (United States)

    Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

    2017-04-01

    Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

  6. Automatic Gap Detection in Friction Stir Welding Processes (Preprint)

    National Research Council Canada - National Science Library

    Yang, Yu; Kalya, Prabhanjana; Landers, Robert G; Krishnamurthy, K

    2006-01-01

    .... This paper develops a monitoring algorithm to detect gaps in Friction Stir Welding (FSW) processes. Experimental studies are conducted to determine how the process parameters and the gap width affect the welding process...

  7. Automatic Detection of Cyberbullying in Social Media Text

    NARCIS (Netherlands)

    Van Hee, Cynthia; Jacobs, Gilles; Emmery, Chris; Desmet, Bart; Lefever, Els; Verhoeven, Ben; De Pauw, Guy; Daelemans, W.M.P.; Hoste, Veronique

    2018-01-01

    While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of

  8. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

    Full Text Available The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step, is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies.

  9. Automatic Change Detection to Facial Expressions in Adolescents: Evidence from Visual Mismatch Negativity Responses

    Directory of Open Access Journals (Sweden)

    Tongran eLiu

    2016-03-01

    Full Text Available Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group were recruited to complete an emotional oddball task featuring on happy and one fearful condition. The measurement of event-related potential (ERP was carried out via electroencephalography (EEG and electrooculography (EOG recording, to detect visual mismatch negativity (vMMN with regard to the automatic detection of changes in facial expressions between the two age groups. The current findings demonstrated that the adolescent group featured more negative vMMN amplitudes than the adult group in the fronto-central region during the 120-200 ms interval. During the time window of 370-450 ms, only the adult group showed better automatic processing on fearful faces than happy faces. The present study indicated that adolescents posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information.

  10. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  11. Oblique-incidence reflectivity difference application for morphology detection.

    Science.gov (United States)

    Zhan, Honglei; Zhao, Kun; Lü, Huibin; Jin, Kuijuan; Yang, Guozhen; Chen, Xiaohong

    2017-10-20

    Analogous with scanning electron microscopy, we use an oblique-incidence reflectivity difference (OIRD) approach for morphology detection. By scanning the active carbon clusters in a one-dimensional way and the reservoir rocks in a two-dimensional way, the morphology of the samples' surface can be revealed in OIRD signal images. High OIRD signals of active carbon samples refer to the centralized distribution areas of carbon, and the fluctuations are caused by the uneven distribution of carbon pellets. OIRD intensity is proportional to the thickness of materials. In terms of rocks, the trough areas with smaller values refer to the low-lying fields. The areas with relatively large OIRD intensities correspond to the protuberance areas of rocks. Consequently, OIRD is a sensitive yet rapid measure of surface detection in material and petrogeology science.

  12. Automatic detection of regions of interest in mammographic images

    Science.gov (United States)

    Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

    2011-03-01

    This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

  13. Study on the Automatic Detection Method and System of Multifunctional Hydrocephalus Shunt

    Science.gov (United States)

    Sun, Xuan; Wang, Guangzhen; Dong, Quancheng; Li, Yuzhong

    2017-07-01

    Aiming to the difficulty of micro pressure detection and the difficulty of micro flow control in the testing process of hydrocephalus shunt, the principle of the shunt performance detection was analyzed.In this study, the author analyzed the principle of several items of shunt performance detection,and used advanced micro pressure sensor and micro flow peristaltic pump to overcome the micro pressure detection and micro flow control technology.At the same time,This study also puted many common experimental projects integrated, and successfully developed the automatic detection system for a shunt performance detection function, to achieve a test with high precision, high efficiency and automation.

  14. AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

    Directory of Open Access Journals (Sweden)

    Vander Luis de Souza Freitas

    Full Text Available Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.

  15. AUTOMATIC TREE-CROWN DETECTION IN CHALLENGING SCENARIOS

    Directory of Open Access Journals (Sweden)

    D. Bulatov

    2016-06-01

    Full Text Available In this paper, a new procedure for individual tree detection and modeling is presented. The input of this procedure consists of a normalized digital surface model NDSM, and a possibly error-prone classification result. The procedure is modular so that the functionality, the advantages and the disadvantages for every single module will be explained. The most important technical contributions of the paper are: Employing watershed transformation combined with classification results, applying hotspots detectors for identifying treetops in groups of trees, and correcting NDSM by detecting and geometric reconstruction of small anomalies, such as earth walls. Two minor contributions are made up by a detailed literature research on available methods for individual tree detection and estimation of tree-crowns for clearly identified trees in order to reduce arbitrariness by assigning trees to one of the few types in the output model.

  16. THE ALGORITHM FOR THE AUTOMATIC DETECTION OF THE WHISTLERS IN THE REAL-TIME MODE

    Directory of Open Access Journals (Sweden)

    E.A. Malysh

    2015-12-01

    Full Text Available This is the description of the whistlers automatic detection algorithm, based on the nonlinear transformation of the spectrogram VLF signal. In the converted spectrogram the whistler graphic is presented by a straight line, detection of which is algorithmically simple task. The testing of the program implementation of the algorithm showed that a detection can be managed in the real-time mode.

  17. Datasets of Odontocete Sounds Annotated for Developing Automatic Detection Methods, FY09-10

    Science.gov (United States)

    2012-09-01

    automatic call detection and classification; make them publicly available in an archive on the Internet ; continue developing and publishing detection and...out of 85 glider dives. Manual analysis revealed that 7 of these detections were actual beaked whale encounters. During the other 3 glider dives...28 Sept.-1 Oct. 2011. Spatially explicit capture-recapture minke whale density estimation. Proc. XIX Congresso Anual da Sociedade Portuguesa de

  18. Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

    Directory of Open Access Journals (Sweden)

    Maíla de Lima Claro

    2016-08-01

    Full Text Available The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

  19. Automatic detection of REM sleep in subjects without atonia

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Nikolic, Miki

    2012-01-01

    Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored...

  20. Automatic error detection in alignments for speech synthesis

    CSIR Research Space (South Africa)

    Barnard, E

    2006-11-01

    Full Text Available The phonetic segmentation of recorded speech is a crucial factor in the quality of concatenative systems for speech synthesis. The authors describe a likelihood-based error detection process that can be used to flag possible errors in such a...

  1. automatic data collection design for neural networks detection

    African Journals Online (AJOL)

    Dr Obe

    ABSTRACT. Automated data collection is necessary to alleviate problems inherent in data collection for investigation of management frauds. Once we have gathered a realistic data, several methods then exist for proper analysis and detection of anomalous transactions. However, in Nigeria, collecting fraudulent data is ...

  2. Automatic Data Collection Design for Neural Networks Detection of ...

    African Journals Online (AJOL)

    Automated data collection is necessary to alleviate problems inherent in data collection for investigation of management frauds. Once we have gathered a realistic data, several methods then exist for proper analysis and detection of anomalous transactions. However, in Nigeria, collecting fraudulent data is relatively difficult ...

  3. Automatic solar panel recognition and defect detection using infrared imaging

    Science.gov (United States)

    Gao, Xiang; Munson, Eric; Abousleman, Glen P.; Si, Jennie

    2015-05-01

    Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to "count" the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.

  4. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

    Science.gov (United States)

    Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

    2017-06-26

    We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

  5. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David

    2013-12-01

    Nanoimprint Lithography (NIL) is a promising technology for low cost and large scale nanostructure fabrication. This technique is based on a contact molding-demolding process, that can produce number of defects such as incomplete filling, negative patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications are presented. Results are independent on the device which captures the image (optical, confocal or electron microscope). The use of numerical images allows the possibility to automate the detection and to compute a statistical analysis of defects. This method provides a fast analysis of printed gratings and could be used to monitor the production of such structures. © 2013 Elsevier B.V. All rights reserved.

  6. CHLOE: A tool for automatic detection of peculiar galaxies

    Science.gov (United States)

    Shamir, Lior; Manning, Saundra; Wallin, John

    2014-09-01

    CHLOE is an image analysis unsupervised learning algorithm that detects peculiar galaxies in datasets of galaxy images. The algorithm first computes a large set of numerical descriptors reflecting different aspects of the visual content, and then weighs them based on the standard deviation of the values computed from the galaxy images. The weighted Euclidean distance of each galaxy image from the median is measured, and the peculiarity of each galaxy is determined based on that distance.

  7. Automatic detection of coronary arterial branches from X-ray angiograms

    International Nuclear Information System (INIS)

    Lu, Shan; Eiho, Shigeru

    1992-01-01

    This paper describes a method to trace the coronary arterial boundaries automatically from x-ray angiograms. We developed an automatic procedure to detect the edges of an artery with its branches. The edge point is evaluated by a function based on smoothing differential operator on a searching line which is obtained by using the continuous properties of the arterial edges. Thus the boundary points along the artery are detected automatically. If there exists a branch on the boundary, it can be detected automatically. This information about the branch is stored on the stack of the search information and will be used to detect the branch artery. In our edge detection process, the required user interaction is only the manual definition of a starting point for the search, the direction of the search and the range for search. We tested this method on some images generated by a computer with different stenoses and on a coronary angiogram. These results show that this method is useful for analyzing coronary angiograms. (author)

  8. Automatic ultrasonic image analysis method for defect detection

    International Nuclear Information System (INIS)

    Magnin, I.; Perdrix, M.; Corneloup, G.; Cornu, B.

    1987-01-01

    Ultrasonic examination of austenitic steel weld seams raises well known problems of interpreting signals perturbed by this type of material. The JUKEBOX ultrasonic imaging system developed at the Cadarache Nuclear Research Center provides a major improvement in the general area of defect localization and characterization, based on processing overall images obtained by (X, Y) scanning. (X, time) images are formed by juxtaposing input signals. A series of parallel images shifted on the Y-axis is also available. The authors present a novel defect detection method based on analysing the timeline positions of the maxima and minima recorded on (X, time) images. This position is statistically stable when a defect is encountered, and is random enough under spurious noise conditions to constitute a discriminating parameter. The investigation involves calculating the trace variance: this parameters is then taken into account for detection purposes. Correlation with parallel images enhances detection reliability. A significant increase in the signal-to-noise ratio during tests on artificial defects is shown

  9. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

    Tomei-Le-Digarcher, Sandrine

    2009-01-01

    Therapeutic follow-up of patients with cancer is nowadays of main interest in research. Positron Emission Tomography (PET) appears to become a reference exam for monitoring treatment of cancers, particular in lymphoma. This PhD thus deals on the development of a computer aided detection (CAD) tool focused on hardly visible tumors for whole-body 3D PET images. To achieve such a goal, we proposed an approach based on the combination of two classifiers, the Linear Discriminant Analysis (LDA) and the Support Vector Machines, associated with wavelet image features. Each classifier gives a 3D score map quantifying the probability of its voxels to correspond to a tumor. We proposed a 3D evaluation strategy based on the use of simulated images giving the targeted tumor characteristic gold standard. Such database was developed in this PhD from hundred Monte Carlo simulations of the Zuba phantom. It includes hundred images presenting 375 spherical tumors of calibrated contrasts. Results of the CAD obtained from the binary detection maps are promising. They open the perspective of enriching the binary information generally given to the clinician with parametric indices quantifying the pertinence of each detected tumor. (author)

  10. Automatic car driving detection using raw accelerometry data.

    Science.gov (United States)

    Strączkiewicz, M; Urbanek, J K; Fadel, W F; Crainiceanu, C M; Harezlak, J

    2016-09-21

    Measuring physical activity using wearable devices has become increasingly popular. Raw data collected from such devices is usually summarized as 'activity counts', which combine information of human activity with environmental vibrations. Driving is a major sedentary activity that artificially increases the activity counts due to various car and body vibrations that are not connected to human movement. Thus, it has become increasingly important to identify periods of driving and quantify the bias induced by driving in activity counts. To address these problems, we propose a detection algorithm of driving via accelerometry (DADA), designed to detect time periods when an individual is driving a car. DADA is based on detection of vibrations generated by a moving vehicle and recorded by an accelerometer. The methodological approach is based on short-time Fourier transform (STFT) applied to the raw accelerometry data and identifies and focuses on frequency vibration ranges that are specific to car driving. We test the performance of DADA on data collected using wrist-worn ActiGraph devices in a controlled experiment conducted on 24 subjects. The median area under the receiver-operating characteristic curve (AUC) for predicting driving periods was 0.94, indicating an excellent performance of the algorithm. We also quantify the size of the bias induced by driving and obtain that per unit of time the activity counts generated by driving are, on average, 16% of the average activity counts generated during walking.

  11. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  12. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    Science.gov (United States)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  13. Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods

    Directory of Open Access Journals (Sweden)

    Dorothée Coppieters ’t Wallant

    2016-01-01

    Full Text Available Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation and individual characteristics (intellectual quotient. Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.

  14. Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods.

    Science.gov (United States)

    Coppieters 't Wallant, Dorothée; Maquet, Pierre; Phillips, Christophe

    2016-01-01

    Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.

  15. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Washington University in St Louis, Taian, Shandong (China); Yang, D [Washington University School of Medicine, St Louis, MO (United States)

    2015-06-15

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  16. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    International Nuclear Information System (INIS)

    Qiu, J; Yang, D

    2015-01-01

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  17. Automatic Encoding and Language Detection in the GSDL – Part II

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2015-10-01

    Full Text Available The processing of the older MS Word format in the GSDL depends on the correct encoding of the temporary HTML file. The “windows-scripting” fails, but the wvware.exe program is successful. The actual .docx format needs user to change the setting in the Word configuration. A temporary HTML file should be encoded in UTF-8 instead of the Windows-1250 preset in the Czech environment. The automatic conversion from ISO-8859-2 to Windows-1250 for HTML pages is wrong, but the conversion ISO-8859-1 to Windows-1252 is valid. The automatic language detection is sometimes incorrect due to the predomination of a similar language model. The automatic language detection needs further investigation.

  18. Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

    Science.gov (United States)

    Sa, Qila; Wang, Zhihui

    2018-03-01

    At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

  19. Automatic detection of mycobacterium tuberculosis using artificial intelligence.

    Science.gov (United States)

    Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen; Li, Ting

    2018-03-01

    Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified.

  20. Automatic detection of service initiation signals used in bars

    Directory of Open Access Journals (Sweden)

    Sebastian eLoth

    2013-08-01

    Full Text Available Recognising the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognise the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesised candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that a these two easily recognisable actions are sufficient for recognising the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and b the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.

  1. Automatic detection of service initiation signals used in bars.

    Science.gov (United States)

    Loth, Sebastian; Huth, Kerstin; De Ruiter, Jan P

    2013-01-01

    Recognizing the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognize the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesized candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that (a) these two easily recognizable actions are sufficient for recognizing the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and (b) the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.

  2. Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms.

    Science.gov (United States)

    Pedrosa, Joao; Castro, Ana; Vinhoza, Tiago T V

    2014-01-01

    The digital analysis of heart sounds has revealed itself as an evolving field of study. In recent years, numerous approaches to create decision support systems were attempted. This paper proposes two novel algorithms: one for the segmentation of heart sounds into heart cycles and another for detecting heart murmurs. The segmentation algorithm, based on the autocorrelation function to find the periodic components of the PCG signal had a sensitivity and positive predictive value of 89.2% and 98.6%, respectively. The murmur detection algorithm is based on features collected from different domains and was evaluated in two ways: a random division between train and test set and a division according to patients. The first returned sensitivity and specificity of 98.42% and 97.21% respectively for a minimum error of 2.19%. The second division had a far worse performance with a minimum error of 33.65%. The operating point was chosen at sensitivity 69.67% and a specificity 46.91% for a total error of 38.90% by varying the percentage of segments classified as murmurs needed for a positive murmur classification.

  3. Method of automatic detection of tumors in mammogram

    Science.gov (United States)

    Xie, Mei; Ma, Zheng

    2001-09-01

    Prevention and early diagnosis of tumors in mammogram are foremost. Unfortunately, these images are often corrupted by the noise due to the film noise and the background texture of the images, which did not allow isolation of the target information from the background noise, and often results in the suspicious area to be analyzed inaccurately. In order to achieve more accurate detection and segmentation tumors, the quality of the images need to improve, (including to suppressing noise and enhancing the contrast of the image). This paper presents a new adaptive histogram threshold method approach for segmentation of suspicious mass regions in digitized images. The method use multi-scale wavelet decomposition and a threshold selection criterion based on a transformed imageís histogram. This separation can help eliminate background noise and discriminates against objects of different size and shape. The tumors are extracted by used an adaptively bayesian classifier. We demonstrate that the method proposed can greatly improve the accuracy of detection in tumors.

  4. Exploiting ensemble learning for automatic cataract detection and grading.

    Science.gov (United States)

    Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

    2016-02-01

    Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Benabbas Yassine

    2011-01-01

    Full Text Available Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.

  6. Detecting uncertainty in spoken dialogues: an explorative research to the automatic detection of a speakers' uncertainty by using prosodic markers

    NARCIS (Netherlands)

    Dral, J.; Heylen, Dirk K.J.; op den Akker, Hendrikus J.A.; Ahmad, K.

    2008-01-01

    This paper reports results in automatic detection of speakers uncertainty in spoken dialogues by using prosodic markers. For this purpose a substantial part of the AMI corpus (a multi-modal multi-party meeting corpus) has been selected and converted to a suitable format so its data could be analyzed

  7. Detecting Uncertainty in Spoken Dialogues: An explorative research for the automatic detection of speaker uncertainty by using prosodic markers

    NARCIS (Netherlands)

    Dral, Jeroen; Heylen, Dirk K.J.; op den Akker, Hendrikus J.A.; Ahmad, Kurshid

    2011-01-01

    This paper reports results in automatic detection of speaker uncertainty in spoken dialogues by using prosodic markers. For this purpose a substantial part of the AMI corpus (a multi-modal multi-party meeting corpus) has been selected and converted to a suitable format so its data could be analyzed

  8. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

    International Nuclear Information System (INIS)

    Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A.; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Greer, Peter B.

    2016-01-01

    Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

  9. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

    Energy Technology Data Exchange (ETDEWEB)

    Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli [Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106 and CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A. [CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Stanwell, Peter [School of health sciences, The University of Newcastle, Newcastle, NSW 2308 (Australia); Pichler, Peter [Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia); Sun, Jidi; Greer, Peter B. [School of Mathematical and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia and Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia)

    2016-05-15

    Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

  10. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  11. Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

    NARCIS (Netherlands)

    Chen, Junwen; Liu, Zhigang; Wang, H.; Nunez Vicencio, Alfredo; Han, Zhiwei

    2018-01-01

    The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary

  12. Decision-tree induction to detect clinical mastitis with automatic milking

    NARCIS (Netherlands)

    Kamphuis, C.; Mollenhorst, H.; Feelders, A.; Pietersma, D.; Hogeveen, H.

    2010-01-01

    a b s t r a c t This study explored the potential of using decision-tree induction to develop models for the detection of clinical mastitis with automatic milking. Sensor data (including electrical conductivity and colour) of over 711,000 quarter milkings were collected from December 2006 till

  13. Automatic Detection of Childhood Absence Epilepsy Seizures: Toward a Monitoring Device

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Madsen, Rasmus E.; Remvig, Line S.

    2012-01-01

    long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection. We implemented a state-of-the-art algorithm...

  14. ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Crispo, B.; Etalle, Sandro

    2008-01-01

    We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network

  15. Feasibility of automatic marker detection with an a-Si flat-panel imager

    NARCIS (Netherlands)

    Nederveen, A. J.; Lagendijk, J. J.; Hofman, P.

    2001-01-01

    Here we study automatic detection of implanted gold markers relative to the field boundary in portal images for on-line position verification. Portal images containing 1-2 MU were taken with an amorphous silicon flat-panel imager. The images were obtained with lateral field at 18 MV. Both the

  16. Automatic Atrial Fibrillation Detection: A Novel Approach Using Discrete Wavelet Transform and Heart Rate Variabilit

    DEFF Research Database (Denmark)

    Bruun, Iben H.; Hissabu, Semira M. S.; Poulsen, Erik S.

    2017-01-01

    be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed...

  17. Automatic detection of children's engagement using non-verbal features and ordinal learning

    NARCIS (Netherlands)

    Kim, Jaebok; Truong, Khiet Phuong; Evers, Vanessa

    In collaborative play, young children can exhibit different types of engagement. Some children are engaged with other children in the play activity while others are just looking. In this study, we investigated methods to automatically detect the children's levels of engagement in play settings using

  18. Automatic Detection of Retinal Exudates using a Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Nualsawat HIRANSAKOLWONG

    2013-02-01

    Full Text Available Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. Unfortunately, it is a normal situation that retinal images in Thailand are poor quality images. In this paper, we present a series of experiments on feature selection and exudates classification using the support vector machine classifiers. The retinal images are segmented following key preprocessing steps, i.e., color normalization, contrast enhancement, noise removal and color space selection. On data sets of poor quality images, sensitivity, specificity and accuracy is 94.46%, 89.52% and 92.14%, respectively.

  19. Automatic detection and treatment of oscillatory and/or stiff ordinary differential equations

    Energy Technology Data Exchange (ETDEWEB)

    Gear, C. W.

    1980-06-01

    The next generation of ODE software can be expected to detect special problems and to adapt to their needs. The low-cost, automatic detection of oscillatory behavior, the determination of its period, and methods for its subsequent efficient integration are addressed here, along with stiffness detection. In the first phase, the method for oscillatory problems discussed examines the output of any integrator to determine if the output is nearly periodic. At the point this answer is positive, the second phase is entered and an automatic, nonstiff, multirevolutionary method is invoked. This requires the occasional solution of a nearly periodic initial-value problem over one period by a standard method and the re-determination of its period. Because the multirevolutionary method uses a very large step, the problem has a high probability of being stiff in this second phase. Hence, it is important to detect if stiffness is present so that an appropriate stiff, multirevolutionary method can be selected. 6 figures.

  20. Automatic processing of isotopic dilution curves obtained by precordial detection

    International Nuclear Information System (INIS)

    Verite, J.C.

    1973-01-01

    Dilution curves pose two distinct problems: that of their acquisition and that of their processing. A study devoted to the latter aspect only was presented. It was necessary to satisfy two important conditions: the treatment procedure, although applied to a single category of curves (isotopic dilution curves obtained by precordial detection), had to be as general as possible; to allow dissemination of the method the equipment used had to be relatively modest and inexpensive. A simple method, considering the curve processing as a process identification, was developed and should enable the mean heart cavity volume and certain pulmonary circulation parameters to be determined. Considerable difficulties were encountered, limiting the value of the results obtained though not condemning the method itself. The curve processing question raised the problem of their acquisition, i.e. the number of these curves and their meaning. A list of the difficulties encountered is followed by a set of possible solutions, a solution being understood to mean a curve processing combination where the overlapping between the two aspects of the problem is accounted for [fr

  1. Automatic detection of ship tracks in ATSR-2 satellite imagery

    Directory of Open Access Journals (Sweden)

    E. Campmany

    2009-03-01

    Full Text Available Ships modify cloud microphysics by adding cloud condensation nuclei (CCN to a developing or existing cloud. These create lines of larger reflectance in cloud fields that are observed in satellite imagery. An algorithm has been developed to automate the detection of ship tracks in Along Track Scanning Radiometer 2 (ATSR-2 imagery. The scheme has been integrated into the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE processing chain. The algorithm firstly identifies intensity ridgelets in clouds which have the potential to be part of a ship track. This identification is done by comparing each pixel with its surrounding ones. If the intensity of three adjacent pixels is greater than the intensity of their neighbours, then it is classified as a ridgelet. These ridgelets are then connected together, according to a set of connectivity rules, to form tracks which are classed as ship tracks if they are long enough. The algorithm has been applied to two years of ATSR-2 data. Ship tracks are most frequently seen off the west coast of California, and the Atlantic coast of both West Africa and South-Western Europe. The global distribution of ship tracks shows strong seasonality, little inter-annual variability and a similar spatial pattern to the distribution of ship emissions.

  2. Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements

    OpenAIRE

    Alberto Martini; Marco Troncossi; Alessandro Rivola

    2015-01-01

    The implementation of strategies for controlling water leaks is essential in order to reduce losses affecting distribution networks of drinking water. This paper focuses on leak detection by using vibration monitoring techniques. The long-term goal is the development of a system for automatic early detection of burst leaks in service pipes. An experimental campaign was started to measure vibrations transmitted along water pipes by real burst leaks occurring in actual water supply networks. Th...

  3. Hotspot Patterns: The Formal Definition and Automatic Detection of Architecture Smells

    Science.gov (United States)

    2015-01-15

    Hotspot Patterns: The Formal Definition and Automatic Detection of Architecture Smells Ran Mo∗, Yuanfang Cai∗, Rick Kazman†, Lu Xiao∗ ∗ Drexel...they are associated with extremely high error-proneness and/or change-proneness, cannot be characterized by existing notions such as code smells [7...software system. Code Smell Detection: Fowler [7] describes the concept of a “bad smell ” as a heuristic for identifying refactoring opportunities. Code

  4. Automatic detection and tracking of dust particles in a RF plasma sheath

    OpenAIRE

    Zayachuk, Y.; Brochard, F.; Bardin, S.; Briançon, J-L.; Hugon, R.; Bougdira, J.

    2010-01-01

    A method enabling automatic detection and tracking of large amounts of individual dust particles in plasmas is presented. Individual trajectories can be found with a good spatiotemporal resolution, even without applying any external light source to facilitate detection. Main advantages of this method is a high portability and the possibility of making statistical analyses of the trajectories of a large amount of non uniformly size distributed particles, under challenging illumination conditio...

  5. Visual mismatch negativity reveals automatic detection of sequential regularity violation

    Directory of Open Access Journals (Sweden)

    Gábor eStefanics

    2011-05-01

    Full Text Available Sequential regularities are abstract rules based on repeating sequences of environmental events, which are useful to make predictions about future events. As the processes underlying visual mismatch negativity (vMMN are sensitive to complex stimulus changes, this event-related potential component, like its auditory counterpart, may be an index of a primitive system of intelligence. Here we tested whether the visual system is capable to detect abstract sequential regularity in unattended stimulus sequences. In our first experiment we investigated the emergence of vMMN and other change-related activity to stimuli violating abstract rules. Red and green disk patterns were delivered in pairs. When in the majority of pairs the colors were identical within the pairs, deviant pairs with different colors for the second member of the pair elicited vMMN. Spatially more extended vMMN responses with longer latency were observed for deviants with 10% compared to 30% probability. In our second experiment utilizing oddball sequences, we tested the emergence of vMMN to violations of a concrete, feature-based rule of a repetition of a standard color. Deviant colors elicited a vMMN response in the oddball sequences. VMMN was larger for the second member of the pair, i.e. after a shorter stimulus onset asynchrony (SOA. This result corresponds to the expected SOA/(vMMN relationship. Our results show that the system underlying vMMN is sensitive to abstract probability rules and this component can be considered as a correlate of violated predictions about the characteristics of environmental events.

  6. Visual mismatch negativity reveals automatic detection of sequential regularity violation.

    Science.gov (United States)

    Stefanics, Gábor; Kimura, Motohiro; Czigler, István

    2011-01-01

    Sequential regularities are abstract rules based on repeating sequences of environmental events, which are useful to make predictions about future events. Here, we tested whether the visual system is capable to detect sequential regularity in unattended stimulus sequences. The visual mismatch negativity (vMMN) component of the event-related potentials is sensitive to the violation of complex regularities (e.g., object-related characteristics, temporal patterns). We used the vMMN component as an index of violation of conditional (if, then) regularities. In the first experiment, to investigate emergence of vMMN and other change-related activity to the violation of conditional rules, red and green disk patterns were delivered in pairs. The majority of pairs comprised of disk patterns with identical colors, whereas in deviant pairs the colors were different. The probabilities of the two colors were equal. The second member of the deviant pairs elicited a vMMN with longer latency and more extended spatial distribution to deviants with lower probability (10 vs. 30%). In the second (control) experiment the emergence of vMMN to violation of a simple, feature-related rule was studied using oddball sequences of stimulus pairs where deviant colors were presented with 20% probabilities. Deviant colored patterns elicited a vMMN, and this component was larger for the second member of the pair, i.e., after a shorter inter-stimulus interval. This result corresponds to the SOA/(v)MMN relationship, expected on the basis of a memory-mismatch process. Our results show that the system underlying vMMN is sensitive to abstract, conditional rules. Representation of such rules implicates expectation of a subsequent event, therefore vMMN can be considered as a correlate of violated predictions about the characteristics of environmental events.

  7. Automatic construction of a recurrent neural network based classifier for vehicle passage detection

    Science.gov (United States)

    Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

    2017-03-01

    Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

  8. Long Baseline Stereovision for Automatic Detection and Ranging of Moving Objects in the Night Sky

    Directory of Open Access Journals (Sweden)

    Vlad Turcu

    2012-09-01

    Full Text Available As the number of objects in Earth’s atmosphere and in low Earth orbit is continuously increasing; accurate surveillance of these objects has become important. This paper presents a generic, low cost sky surveillance system based on stereovision. Two cameras are placed 37 km apart and synchronized by a GPS-controlled external signal. The intrinsic camera parameters are calibrated before setup in the observation position, the translation vectors are determined from the GPS coordinates and the rotation matrices are continuously estimated using an original automatic calibration methodology based on following known stars. The moving objects in the sky are recognized as line segments in the long exposure images, using an automatic detection and classification algorithm based on image processing. The stereo correspondence is based on the epipolar geometry and is performed automatically using the image detection results. The resulting experimental system is able to automatically detect moving objects such as planes, meteors and Low Earth Orbit satellites, and measure their 3D position in an Earth-bound coordinate system.

  9. Automated Incident Detection Using Real-Time Floating Car Data

    Directory of Open Access Journals (Sweden)

    Maarten Houbraken

    2017-01-01

    Full Text Available The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID system using only Floating Car Data (FCD in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

  10. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    International Nuclear Information System (INIS)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing

    2016-01-01

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  11. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    Energy Technology Data Exchange (ETDEWEB)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing, E-mail: jzeng13@uw.edu [Department of Radiation Oncology, University of Washington Medical Center, 1959 NE Pacific Street, Campus Box 356043, Seattle, Washington 98195 (United States)

    2016-05-15

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  12. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

    Full Text Available This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i automatic camera calibration using both moving objects and a background structure; (ii object depth estimation; and (iii detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

  13. Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT.

    Science.gov (United States)

    Baum, Thomas; Bauer, Jan S; Klinder, Tobias; Dobritz, Martin; Rummeny, Ernst J; Noël, Peter B; Lorenz, Cristian

    2014-04-01

    To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures. Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus. Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p osteoporotic vertebral fractures so that appropriate therapy can be initiated. • This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. • Osteoporotic vertebral fractures could be automatically detected using this prototype algorithm. • The prototype algorithm helps radiologists to report underdiagnosed osteoporotic vertebral fractures.

  14. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

    Science.gov (United States)

    Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

    2018-01-01

    Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  15. Early Detection and Localization of Downhole Incidents in Managed Pressure Drilling

    DEFF Research Database (Denmark)

    Willersrud, Anders; Imsland, Lars; Blanke, Mogens

    2015-01-01

    Downhole incidents such as kick, lost circulation, pack-off, and hole cleaning issues are important contributors to downtime in drilling. In managed pressure drilling (MPD), operations margins are typically narrower, implying more frequent incidents and more severe consequences. Detection...

  16. Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms

    Directory of Open Access Journals (Sweden)

    Woong Bae Yoon

    2016-01-01

    Full Text Available The computer-aided detection (CAD systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.

  17. A method for unsupervised change detection and automatic radiometric normalization in multispectral data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton John

    2011-01-01

    Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North...... Rhine- Westphalia, Germany. A link to an example with ASTER data to detect change with the same method after the 2005 Kashmir earthquake is given. The method is also used to automatically normalize multitemporal, multispectral Landsat ETM+ data radiometrically. IDL/ENVI, Python and Matlab software...

  18. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

    Science.gov (United States)

    Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

  19. Automatic detection of rapid eye movements (REMs): A machine learning approach.

    Science.gov (United States)

    Yetton, Benjamin D; Niknazar, Mohammad; Duggan, Katherine A; McDevitt, Elizabeth A; Whitehurst, Lauren N; Sattari, Negin; Mednick, Sara C

    2016-02-01

    Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. We developed an automatic REM detection algorithm combining a novel set of extracted features and the 'AdaBoost' classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha=0.65) is markedly lower than experts (Cronbach Alpha=0.80). By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. A chest-shape target automatic detection method based on Deformable Part Models

    Science.gov (United States)

    Zhang, Mo; Jin, Weiqi; Li, Li

    2016-10-01

    Automatic weapon platform is one of the important research directions at domestic and overseas, it needs to accomplish fast searching for the object to be shot under complex background. Therefore, fast detection for given target is the foundation of further task. Considering that chest-shape target is common target of shoot practice, this paper treats chestshape target as the target and studies target automatic detection method based on Deformable Part Models. The algorithm computes Histograms of Oriented Gradient(HOG) features of the target and trains a model using Latent variable Support Vector Machine(SVM); In this model, target image is divided into several parts then we can obtain foot filter and part filters; Finally, the algorithm detects the target at the HOG features pyramid with method of sliding window. The running time of extracting HOG pyramid with lookup table can be shorten by 36%. The result indicates that this algorithm can detect the chest-shape target in natural environments indoors or outdoors. The true positive rate of detection reaches 76% with many hard samples, and the false positive rate approaches 0. Running on a PC (Intel(R)Core(TM) i5-4200H CPU) with C++ language, the detection time of images with the resolution of 640 × 480 is 2.093s. According to TI company run library about image pyramid and convolution for DM642 and other hardware, our detection algorithm is expected to be implemented on hardware platform, and it has application prospect in actual system.

  1. Automatic defect detection in video archives: application to Montreux Jazz Festival digital archives

    Science.gov (United States)

    Hanhart, Philippe; Rerabek, Martin; Ivanov, Ivan; Dufaux, Alain; Jones, Caryl; Delidais, Alexandre; Ebrahimi, Touradj

    2013-09-01

    Archival of audio-visual databases has become an important discipline in multimedia. Various defects are typ- ically present in such archives. Among those, one can mention recording related defects such as interference between audio and video signals, optical related artifacts, recording and play out artifacts such as horizontal lines, and dropouts, as well as those due to digitization such as diagonal lines. An automatic or semi-automatic detection to identify such defects is useful, especially for large databases. In this paper, we propose two auto- matic algorithms for detection of horizontal and diagonal lines, as well as dropouts that are among the most typical artifacts encountered. We then evaluate the performance of these algorithms by making use of ground truth scores obtained by human subjects.

  2. An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

    Directory of Open Access Journals (Sweden)

    Hai Guo

    2015-01-01

    Full Text Available An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA. So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine, NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

  3. Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?

    DEFF Research Database (Denmark)

    Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk

    1995-01-01

    curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...... of relevant pressure peaks at the various recording levels. Until now, this selection has been performed entirely by rule-based systems, requiring each pressure deflection to fit within predefined rigid numerical limits in order to be detected. However, due to great variations in the shapes of the pressure...

  4. Automatic Laser Light Detection and Filtering Using a Liquid Crystal Lyot Filter

    Science.gov (United States)

    Rees, S.; Staromlynska, J.

    A device which acts as both a simple cw laser warner and anti-dazzle protection device has been designed and tested. The design is based on a single stage, double element, tunable liquid crystal Lyot filter. Laboratory tests have shown that the detection sensitivity of the device for monochromatic cw radiation is approximately 43×10-9 W/cm2 and that the achievable contrast ratio is greater than 100:1 Automatic detection and filtering of a cw laser source has been demonstrated. Results indicate that the algorithm to automatically filter the laser radiation works well and that the extinction obtained is good. Factors which affect the contrast ratio are discussed and improved device design suggested.

  5. Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks

    OpenAIRE

    Oliveira, Gonçalo; Frazão, Xavier; Pimentel, André; Ribeiro, Bernardete

    2016-01-01

    Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In par...

  6. Automatic collection of the rare-earths with post chromatography column detection

    International Nuclear Information System (INIS)

    David, P.; Metzger, G.; Repellin, M.

    1987-01-01

    The complete separation of rare-earths (in the aim of radio-isotopes measurement) requires High Performance Liquid Chromatography with ternary elution gradient. To automatize their collection with satisfying conditions, we have realized a non polluting, reliable and easy to operate detection method. This one is based on a derivation colorimetric system with arsenazo I (3 -(2 arsophenylazo 4.5) - dihydroxy - 2.7 naphtalene disulfonic acid)

  7. Automatic Detection and Classification of Diabetic Retinopathy Using Retinal Fundus Images

    OpenAIRE

    A. Biran; P. Sobhe Bidari; A. Almazroe V. Lakshminarayanan; K. Raahemifar

    2016-01-01

    Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLA...

  8. Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

    Directory of Open Access Journals (Sweden)

    Kajsa Møllersen

    Full Text Available Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have

  9. Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

    Science.gov (United States)

    Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

    2017-01-01

    Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact

  10. [A wavelet-transform-based method for the automatic detection of late-type stars].

    Science.gov (United States)

    Liu, Zhong-tian; Zhao, Rrui-zhen; Zhao, Yong-heng; Wu, Fu-chao

    2005-07-01

    The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

  11. Forming and detection of digital watermarks in the System for Automatic Identification of VHF Transmissions

    Directory of Open Access Journals (Sweden)

    О. В. Шишкін

    2013-07-01

    Full Text Available Forming and detection algorithms for digital watermarks are designed for automatic identification of VHF radiotelephone transmissions in the maritime and aeronautical mobile services. An audible insensitivity and interference resistance of embedded digital data are provided by means of OFDM technology jointly with normalized distortions distribution and data packet detection by the hash-function. Experiments were carried out on the base of ship’s radio station RT-2048 Sailor and USB ADC-DAC module of type Е14-140M L-CARD in the off-line processing regime in Matlab medium

  12. Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, J; Sørensen, Gertrud Laura; Sorensen, H B D

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG....

  13. AUTOMATIC PEDESTRIAN CROSSING DETECTION AND IMPAIRMENT ANALYSIS BASED ON MOBILE MAPPING SYSTEM

    Directory of Open Access Journals (Sweden)

    X. Liu

    2017-09-01

    Full Text Available Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  14. Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images.

    Science.gov (United States)

    Prokopetc, Kristina; Collins, Toby; Bartoli, Adrien

    2015-01-01

    We present a method for the automatic detection of the uterus and the Fallopian tube/Uterus junctions (FU-junctions) in a monocular laparoscopic image. The main application is to perform automatic registration and fusion between preoperative radiological images of the uterus and laparoscopic images for image-guided surgery. In the broader context of computer assisted intervention, our method is the first that detects an organ and registration landmarks from laparoscopic images without manual input. Our detection problem is challenging because of the large inter-patient anatomical variability and pathologies such as uterine fibroids. We solve the problem using learned contextual geometric constraints that statistically model the positions and orientations of the FU-junctions relative to the uterus' body. We train the uterus detector using a modern part-based approach and the FU-junction detector using junction-specific context-sensitive features. We have trained and tested on a database of 95 uterus images with cross validation, and successfully detected the uterus with Recall = 0.95 and average Number of False Positives per Image (NFPI) = 0.21, and FU-junctions with Recall = 0.80 and NFPI = 0.50. Our experimental results show that the contextual constraints are fundamental to achieve high quality detection.

  15. Automatic Pedestrian Crossing Detection and Impairment Analysis Based on Mobile Mapping System

    Science.gov (United States)

    Liu, X.; Zhang, Y.; Li, Q.

    2017-09-01

    Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  16. Farmers' preferences for automatic lameness-detection systems in dairy cattle.

    Science.gov (United States)

    Van De Gucht, T; Saeys, W; Van Nuffel, A; Pluym, L; Piccart, K; Lauwers, L; Vangeyte, J; Van Weyenberg, S

    2017-07-01

    As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic

  17. Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT

    Energy Technology Data Exchange (ETDEWEB)

    Baum, Thomas; Dobritz, Martin; Rummeny, Ernst J.; Noel, Peter B. [Technische Universitaet Muenchen, Institut fuer Radiologie, Klinikum rechts der Isar, Muenchen (Germany); Bauer, Jan S. [Technische Universitaet Muenchen, Abteilung fuer Neuroradiologie, Klinikum rechts der Isar, Muenchen (Germany); Klinder, Tobias; Lorenz, Cristian [Philips Research Laboratories, Hamburg (Germany)

    2014-04-15

    To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures. Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus. Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p < 0.001). Longitudinal changes in APR and MPR were significantly different between vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p < 0.001). This prototype algorithm may support radiologists in reporting currently underdiagnosed osteoporotic vertebral fractures so that appropriate therapy can be initiated. circle This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. (orig.)

  18. Applications of the automatic change detection for disaster monitoring by the knowledge-based framework

    Science.gov (United States)

    Tadono, T.; Hashimoto, S.; Onosato, M.; Hori, M.

    2012-11-01

    Change detection is a fundamental approach in utilization of satellite remote sensing image, especially in multi-temporal analysis that involves for example extracting damaged areas by a natural disaster. Recently, the amount of data obtained by Earth observation satellites has increased significantly owing to the increasing number and types of observing sensors, the enhancement of their spatial resolution, and improvements in their data processing systems. In applications for disaster monitoring, in particular, fast and accurate analysis of broad geographical areas is required to facilitate efficient rescue efforts. It is expected that robust automatic image interpretation is necessary. Several algorithms have been proposed in the field of automatic change detection in past, however they are still lack of robustness for multi purposes, an instrument independency, and accuracy better than a manual interpretation. We are trying to develop a framework for automatic image interpretation using ontology-based knowledge representation. This framework permits the description, accumulation, and use of knowledge drawn from image interpretation. Local relationships among certain concepts defined in the ontology are described as knowledge modules and are collected in the knowledge base. The knowledge representation uses a Bayesian network as a tool to describe various types of knowledge in a uniform manner. Knowledge modules are synthesized and used for target-specified inference. The results applied to two types of disasters by the framework without any modification and tuning are shown in this paper.

  19. Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

    Science.gov (United States)

    Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

    2018-03-01

    Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  20. A semi-automatic traffic sign detection, classification, and positioning system

    Science.gov (United States)

    Creusen, I. M.; Hazelhoff, L.; de With, P. H. N.

    2012-01-01

    The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental agencies are interested in these inventories for maintenance and safety reasons. This paper introduces a complete semi-automatic traffic sign inventory system. The system consists of several components. First, a detection algorithm locates the 2D position of the traffic signs in the panoramic images. Second, a classification algorithm is used to identify the traffic sign. Third, the 3D position of the traffic sign is calculated using the GPS position of the photographs. Finally, the results are listed in a table for quick inspection and are also visualized in a web browser.

  1. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde [Santa María Tonantzintla, Puebla (Mexico)

    2017-06-15

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

  2. Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

    International Nuclear Information System (INIS)

    Cruz-Vega, Israel; Rangel-Magdaleno, Jose; Ramirez-Cortes, Juan; Peregrina-Barreto, Hayde

    2017-01-01

    There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and auto- correlation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, com- paring the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

  3. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  4. Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements

    Directory of Open Access Journals (Sweden)

    Alberto Martini

    2015-01-01

    Full Text Available The implementation of strategies for controlling water leaks is essential in order to reduce losses affecting distribution networks of drinking water. This paper focuses on leak detection by using vibration monitoring techniques. The long-term goal is the development of a system for automatic early detection of burst leaks in service pipes. An experimental campaign was started to measure vibrations transmitted along water pipes by real burst leaks occurring in actual water supply networks. The first experimental data were used for assessing the leak detection performance of a prototypal algorithm based on the calculation of the standard deviation of acceleration signals. The experimental campaign is here described and discussed. The proposed algorithm, enhanced by means of proper signal filtering techniques, was successfully tested on all monitored leaks, thus proving effective for leak detection purpose.

  5. Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance

    Science.gov (United States)

    Liu, Xiaoquan; Wang, Xinwei; Ren, Pengdao; Cao, Yinan; Zhou, Yan; Liu, Yuliang

    2017-08-01

    An automatic fishing net detection and recognition method for underwater obstacle avoidance is proposed. In the method, optical gated viewing technology is utilized to obtain high-resolution fishing net images and extend detection distance by suppressing water backscattering and background noise. The fishing net recognition is based on the proposed histograms of slope lines (HSLs) descriptors plus a support vector machine classifier. The extraction of HSL descriptors includes five steps of contrast-limited adaptive histogram equalization, the Gaussian low-pass filtering, the Canny detection, the Hough transform, and weighted vote. In the proof experiments, the detection distance of the fishing net reaches 5.7 attenuation length and the recognition accuracy reaches 93.79%.

  6. Automatic concrete cracks detection and mapping of terrestrial laser scan data

    Directory of Open Access Journals (Sweden)

    Mostafa Rabah

    2013-12-01

    The current paper submits a method for automatic concrete cracks detection and mapping from the data that was obtained during laser scanning survey. The method of cracks detection and mapping is achieved by three steps, namely the step of shading correction in the original image, step of crack detection and finally step of crack mapping and processing steps. The detected crack is defined in a pixel coordinate system. To remap the crack into the referred coordinate system, a reverse engineering is used. This is achieved by a hybrid concept of terrestrial laser-scanner point clouds and the corresponding camera image, i.e. a conversion from the pixel coordinate system to the terrestrial laser-scanner or global coordinate system. The results of the experiment show that the mean differences between terrestrial laser scan and the total station are about 30.5, 16.4 and 14.3 mms in x, y and z direction, respectively.

  7. Efficient video-equipped fire detection approach for automatic fire alarm systems

    Science.gov (United States)

    Kang, Myeongsu; Tung, Truong Xuan; Kim, Jong-Myon

    2013-01-01

    This paper proposes an efficient four-stage approach that automatically detects fire using video capabilities. In the first stage, an approximate median method is used to detect video frame regions involving motion. In the second stage, a fuzzy c-means-based clustering algorithm is employed to extract candidate regions of fire from all of the movement-containing regions. In the third stage, a gray level co-occurrence matrix is used to extract texture parameters by tracking red-colored objects in the candidate regions. These texture features are, subsequently, used as inputs of a back-propagation neural network to distinguish between fire and nonfire. Experimental results indicate that the proposed four-stage approach outperforms other fire detection algorithms in terms of consistently increasing the accuracy of fire detection in both indoor and outdoor test videos.

  8. Automatic detection of noisy channels in fNIRS signal based on correlation analysis.

    Science.gov (United States)

    Guerrero-Mosquera, Carlos; Borragán, Guillermo; Peigneux, Philippe

    2016-09-15

    fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

    Directory of Open Access Journals (Sweden)

    Kemal Akyol

    2016-01-01

    Full Text Available With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

  10. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

    Science.gov (United States)

    Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

    2013-11-01

    A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

  11. Automatic Cloud Detection for Chinese High Resolution Remote Sensing Satellite Imagery

    Directory of Open Access Journals (Sweden)

    TAN Kai

    2016-05-01

    Full Text Available Cloud detection is always an arduous problem in satellite imagery processing, especially the thin cloud which has the similar spectral characteristics as ground surfacehas long been the obstacle of the production of imagery product. In this paper, an automatic cloud detection method for Chinese high resolution remote sensing satellite imagery is introduced to overcome this problem.Firstly, the image is transformed from RGB to HIS color space by an improved color transformation model. The basic cloud coverage figure is obtained by using the information of intensity and saturation,followed by getting the modified figure with the information of near-infrared band and hue. Methods of histogram equalization and bilateral filtering, combined with conditioned Otsu thresholding are adopted to generate texture information. Then the cloud seed figureis obtained by using texture information to eliminate the existed errors in the modified figure. Finally, cloud covered areas are accurately extracted by integration of intensity information from the HIS color space and cloud seed figure. Compared to the detection results of other automatic and interactive methods, the overall accuracy of our proposed method achieves nearly 10% improvement, and it is capable of improving the efficiency of cloud detection significantly.

  12. Automatic detection of Martian dark slope streaks by machine learning using HiRISE images

    Science.gov (United States)

    Wang, Yexin; Di, Kaichang; Xin, Xin; Wan, Wenhui

    2017-07-01

    Dark slope streaks (DSSs) on the Martian surface are one of the active geologic features that can be observed on Mars nowadays. The detection of DSS is a prerequisite for studying its appearance, morphology, and distribution to reveal its underlying geological mechanisms. In addition, increasingly massive amounts of Mars high resolution data are now available. Hence, an automatic detection method for locating DSSs is highly desirable. In this research, we present an automatic DSS detection method by combining interest region extraction and machine learning techniques. The interest region extraction combines gradient and regional grayscale information. Moreover, a novel recognition strategy is proposed that takes the normalized minimum bounding rectangles (MBRs) of the extracted regions to calculate the Local Binary Pattern (LBP) feature and train a DSS classifier using the Adaboost machine learning algorithm. Comparative experiments using five different feature descriptors and three different machine learning algorithms show the superiority of the proposed method. Experimental results utilizing 888 extracted region samples from 28 HiRISE images show that the overall detection accuracy of our proposed method is 92.4%, with a true positive rate of 79.1% and false positive rate of 3.7%, which in particular indicates great performance of the method at eliminating non-DSS regions.

  13. Planning an Automatic Fire Detection, Alarm, and Extinguishing System for Research Laboratories

    Directory of Open Access Journals (Sweden)

    Rostam Golmohamadi

    2014-04-01

    Full Text Available Background & Objectives: Educational and research laboratories in universities have a high risk of fire, because they have a variety of materials and equipment. The aim of this study was to provide a technical plan for safety improvement in educational and research laboratories of a university based on the design of automatic detection, alarm, and extinguishing systems . Methods : In this study, fire risk assessment was performed based on the standard of Military Risk Assessment method (MIL-STD-882. For all laboratories, detection and fire alarm systems and optimal fixed fire extinguishing systems were designed. Results : Maximum and minimum risks of fire were in chemical water and wastewater (81.2% and physical agents (62.5% laboratories, respectively. For studied laboratories, we designed fire detection systems based on heat and smoke detectors. Also in these places, fire-extinguishing systems based on CO2 were designed . Conclusion : Due to high risk of fire in studied laboratories, the best control method for fire prevention and protection based on special features of these laboratories is using automatic detection, warning and fire extinguishing systems using CO2 .

  14. Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Yongwha Chung

    2013-09-01

    Full Text Available Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD and the Sparse Representation Classifier (SRC as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used and accurately (94% detection and 91% classification accuracy, either as a standalone solution or to complement known methods to obtain a more accurate solution.

  15. Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis

    Science.gov (United States)

    Spies, Lothar; Tewes, Anja; Suppa, Per; Opfer, Roland; Buchert, Ralph; Winkler, Gerhard; Raji, Alaleh

    2013-12-01

    A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (≥0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.

  16. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification.

    Science.gov (United States)

    Wang, Yuanfa; Li, Zunchao; Feng, Lichen; Zheng, Chuang; Zhang, Wenhao

    2017-01-01

    An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

  17. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhu, Jiang; Kong, Dexing

    2017-05-01

    It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images. Our cascade CNNs are a type of hybrid model, consisting of two different CNNs and a new splitting method. Specifically, it employs a deep CNN to learn the segmentation probability maps from the ground true data. Then, all the segmentation probability maps are split into different connected regions by the splitting method. Finally, another deep CNN is used to automatically detect the thyroid nodules from ultrasound thyroid images. Experiment results illustrate the cascade CNNs are very effective in detection of thyroid nodules. Specially, the value of area under the curve of receiver operating characteristic is 98.51%. The Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses show a significant improvement in the performance of our cascade CNNs compared to that of other methods. The multi-view strategy can improve the performance of cascade CNNs. Moreover, our special splitting method can effectively separate different connected regions so that the second CNN can correctively gain the positive and negative samples according to the automatic labels. The experiment results demonstrate the potential clinical applications of this proposed method. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules. © 2017 American Association of Physicists in Medicine.

  18. Automatic polymerase chain reaction product detection system for food safety monitoring using zinc finger protein fused to luciferase

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Wataru; Kezuka, Aki; Murakami, Yoshiyuki; Lee, Jinhee; Abe, Koichi [Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588 (Japan); Motoki, Hiroaki; Matsuo, Takafumi; Shimura, Nobuaki [System Instruments Co., Ltd., 776-2 Komiya-cho, Hachioji, Tokyo 192-0031 (Japan); Noda, Mamoru; Igimi, Shizunobu [Division of Biomedical Food Research, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501 (Japan); Ikebukuro, Kazunori, E-mail: ikebu@cc.tuat.ac.jp [Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588 (Japan)

    2013-11-01

    Graphical abstract: -- Highlights: •Zif268 fused to luciferase was used for E. coli O157, Salmonella and coliform detection. •Artificial zinc finger protein fused to luciferase was constructed for Norovirus detection. •An analyzer that automatically detects PCR products by zinc finger protein fused to luciferase was developed. •Target pathogens were specifically detected by the automatic analyzer with zinc finger protein fused to luciferase. -- Abstract: An automatic polymerase chain reaction (PCR) product detection system for food safety monitoring using zinc finger (ZF) protein fused to luciferase was developed. ZF protein fused to luciferase specifically binds to target double stranded DNA sequence and has luciferase enzymatic activity. Therefore, PCR products that comprise ZF protein recognition sequence can be detected by measuring the luciferase activity of the fusion protein. We previously reported that PCR products from Legionella pneumophila and Escherichia coli (E. coli) O157 genomic DNA were detected by Zif268, a natural ZF protein, fused to luciferase. In this study, Zif268–luciferase was applied to detect the presence of Salmonella and coliforms. Moreover, an artificial zinc finger protein (B2) fused to luciferase was constructed for a Norovirus detection system. In the luciferase activity detection assay, several bound/free separation process is required. Therefore, an analyzer that automatically performed the bound/free separation process was developed to detect PCR products using the ZF–luciferase fusion protein. By means of the automatic analyzer with ZF–luciferase fusion protein, target pathogenic genomes were specifically detected in the presence of other pathogenic genomes. Moreover, we succeeded in the detection of 10 copies of E. coli BL21 without extraction of genomic DNA by the automatic analyzer and E. coli was detected with a logarithmic dependency in the range of 1.0 × 10 to 1.0 × 10{sup 6} copies.

  19. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs.

    Science.gov (United States)

    Niemeijer, Meindert; van Ginneken, Bram; Cree, Michael J; Mizutani, Atsushi; Quellec, Gwénolé; Sanchez, Clara I; Zhang, Bob; Hornero, Roberto; Lamard, Mathieu; Muramatsu, Chisako; Wu, Xiangqian; Cazuguel, Guy; You, Jane; Mayo, Agustín; Li, Qin; Hatanaka, Yuji; Cochener, Béatrice; Roux, Christian; Karray, Fakhri; Garcia, María; Fujita, Hiroshi; Abramoff, Michael D

    2010-01-01

    The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. Abràmoff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.

  20. Automatic detection, segmentation and assessment of snoring from ambient acoustic data.

    Science.gov (United States)

    Duckitt, W D; Tuomi, S K; Niesler, T R

    2006-10-01

    Snoring is a prevalent condition with a variety of negative social effects and associated health problems. Treatments, both surgical and therapeutic, have been developed, but the objective non-invasive monitoring of their success remains problematic. We present a method which allows the automatic monitoring of snoring characteristics, such as intensity and frequency, from audio data captured via a freestanding microphone. This represents a simple and portable diagnostic alternative to polysomnography. Our system is based on methods that have proved effective in the field of speech recognition. Hidden Markov models (HMMs) were employed as basic elements with which to model different types of sound by means of spectrally based features. This allows periods of snoring to be identified, while rejecting silence, breathing and other sounds. Training and test data were gathered from six subjects, and annotated appropriately. The system was tested by requiring it to automatically classify snoring sounds in new audio recordings and then comparing the result with manually obtained annotations. We found that our system was able to correctly identify snores with 82-89% accuracy, despite the small size of the training set. We could further demonstrate how this segmentation can be used to measure the snoring intensity, snoring frequency and snoring index. We conclude that a system based on hidden Markov models and spectrally based features is effective in the automatic detection and monitoring of snoring from audio data.

  1. Detection of fiducial gold markers for automatic on-line megavoltage position verification using a marker extraction kernel (MEK)

    NARCIS (Netherlands)

    Nederveen, A.; Lagendijk, J.; Hofman, P.

    2000-01-01

    PURPOSE: In this study automatic detection of implanted gold markers in megavoltage portal images for on-line position verification was investigated. METHODS AND MATERIALS: A detection method for fiducial gold markers, consisting of a marker extraction kernel (MEK), was developed. The detection

  2. Automatic Lumen Detection on Longitudinal Ultrasound B-Mode Images of the Carotid Using Phase Symmetry

    Directory of Open Access Journals (Sweden)

    José Rouco

    2016-03-01

    Full Text Available This article describes a method that improves the performance of previous approaches for the automatic detection of the common carotid artery (CCA lumen centerline on longitudinal B-mode ultrasound images. We propose to detect several lumen centerline candidates using local symmetry analysis based on local phase information of dark structures at an appropriate scale. These candidates are analyzed with selection mechanisms that use symmetry, contrast or intensity features in combination with position-based heuristics. Several experimental results are provided to evaluate the robustness and performance of the proposed method in comparison with previous approaches. These results lead to the conclusion that our proposal is robust to noise, lumen artifacts, contrast variations and that is able to deal with the presence of CCA-like structures, significantly improving the performance of our previous approach, from 87.5% ± 0.7% of correct detections to 98.3% ± 0.3% in a set of 200 images.

  3. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    Science.gov (United States)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  4. Automatic Detection of Microcalcifications in a Digital Mammography Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Carlos A. Madrigal-González

    2013-11-01

    Full Text Available Breast cancer is one of the cancers that has a higher mortality rate among women and early detection increases the possibilities of cure, so its early detection is one of the best treatments for this serious disease. Microcalcifications are a type of lesion in the breast and its presence is highly correlated with the presence of cancer. In this paper we present a method for automatic detection of microcalcifications using digital image processing using a Gaussian filtering approach, which can enhance the contrast between microcalcifications and normal tissue present in a mammography, then apply a local thresholding algorithm witch allow the identification of suspicious microcalcifications. The classifier used to determine the degree of benign or malignant microcalcifications is the K-Nearest Neighbours (KNN and the validation of the results was done using ROC curves.

  5. The Impact of the Implementation of Edge Detection Methods on the Accuracy of Automatic Voltage Reading

    Science.gov (United States)

    Sidor, Kamil; Szlachta, Anna

    2017-04-01

    The article presents the impact of the edge detection method in the image analysis on the reading accuracy of the measured value. In order to ensure the automatic reading of the measured value by an analog meter, a standard webcam and the LabVIEW programme were applied. NI Vision Development tools were used. The Hough transform was used to detect the indicator. The programme output was compared during the application of several methods of edge detection. Those included: the Prewitt operator, the Roberts cross, the Sobel operator and the Canny edge detector. The image analysis was made for an analog meter indicator with the above-mentioned methods, and the results of that analysis were compared with each other and presented.

  6. Using Acceleration Data to Automatically Detect the Onset of Farrowing in Sows

    Directory of Open Access Journals (Sweden)

    Imke Traulsen

    2018-01-01

    Full Text Available The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1 Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2 Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3 Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4 Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates.

  7. Automatic vs. Human Detection of Bipolar Magnetic Regions: Using the Best of Both Worlds

    Science.gov (United States)

    Munoz-Jaramillo, A.; DeLuca, M. D.; Windmueller, J. C.; Longcope, D. W.

    2014-12-01

    The solar cycle can be understood as a process that alternates the large-scale magnetic field of the Sun between poloidal and toroidal configurations. Although the process that transitions the solar cycle between toroidal and poloidal phases is still not fully understood, theoretical studies, and observational evidence, suggest that this process is driven by the emergence and decay of bipolar magnetic regions (BMRs) at the photosphere. Furthermore, the emergence of BMRs at the photosphere is the main driver behind solar variability and solar activity in general; making the study of their properties doubly important for heliospheric physics. However, in spite of their critical role, there is still no unified catalog of BMRs spanning multiple instruments and covering the entire period of systematic measurement of the solar magnetic field (i.e. 1975 to present).One of the interesting aspects of the detection of BMRs is that, due to the time and spatial scales of interest, it is tractable for both human observers and automatic detection algorithms. This makes it ideal for comparative studies of the advantages and failing of both approaches. In this presentation we will compare three different BMR catalogs, reduced from magnetograms taken by SOHO/MDI, using human, automatic, and hybrid methods of detection. The focus will be the comparative performance between the three methods, their merits, and disadvantages, and the lessons that can be applied to other imaging data sets.

  8. Automatic sex detection of individuals of Ceratitis capitata by means of computer vision in a biofactory.

    Science.gov (United States)

    Blasco, Jose; Gómez-Sanchís, Juan; Gutierrez, Abelardo; Chueca, Patricia; Argilés, Rafael; Moltó, Enrique

    2009-01-01

    The sterile insect technique (SIT) is acknowledged around the world as an effective method for biological pest control of Ceratitis capitata (Wiedemann). Sterile insects are produced in biofactories where one key issue is the selection of the progenitors that have to transmit specific genetic characteristics. Recombinant individuals must be removed as this colony is renewed. Nowadays, this task is performed manually, in a process that is extremely slow, painstaking and labour intensive, in which the sex of individuals must be identified. The paper explores the possibility of using vision sensors and pattern recognition algorithms for automated detection of recombinants. An automatic system is proposed and tested to inspect individual specimens of C. capitata using machine vision. It includes a backlighting system and image processing algorithms for determining the sex of live flies in five high-resolution images of each insect. The system is capable of identifying the sex of the flies by means of a program that analyses the contour of the abdomen, using fast Fourier transform features, to detect the presence of the ovipositor. Moreover, it can find the characteristic spatulate setae of males. Simulation tests with 1000 insects (5000 images) had 100% success in identifying male flies, with an error rate of 0.6% for female flies. This work establishes the basis for building a machine for the automatic detection and removal of recombinant individuals in the selection of progenitors for biofactories, which would have huge benefits for SIT around the globe.

  9. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

    Directory of Open Access Journals (Sweden)

    Vahid Faghih Dinevari

    2016-01-01

    Full Text Available Wireless capsule endoscopy (WCE is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT and singular value decomposition (SVD algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

  10. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images.

    Science.gov (United States)

    Faghih Dinevari, Vahid; Karimian Khosroshahi, Ghader; Zolfy Lighvan, Mina

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

  11. On-line automatic detection of wood pellets in pneumatically conveyed wood dust flow

    Science.gov (United States)

    Sun, Duo; Yan, Yong; Carter, Robert M.; Gao, Lingjun; Qian, Xiangchen; Lu, Gang

    2014-04-01

    This paper presents a piezoelectric transducer based system for on-line automatic detection of wood pellets in wood dust flow in pneumatic conveying pipelines. The piezoelectric transducer senses non-intrusively the collisions between wood pellets and the pipe wall. Wavelet-based denoising is adopted to eliminate environmental noise and recover the collision events. Then the wood pellets are identified by sliding a time window through the denoised signal with a suitable threshold. Experiments were carried out on a laboratory test rig and on an industrial pneumatic conveying pipeline to assess the effectiveness and operability of the system.

  12. Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method

    OpenAIRE

    Hamid Reza Pourreza; Mohammad Hossein Bahreyni Toossi; Alireza Mehdizadeh; Reza Pourreza; Meysam Tavakoli

    2009-01-01

    Introduction: Diabetic retinopathy (DR) is one of the most serious and most frequent eye diseases in the world and the most common cause of blindness in adults between 20 and 60 years of age. Following 15 years of diabetes, about 2% of the diabetic patients are blind and 10% suffer from vision impairment due to DR complications. This paper addresses the automatic detection of microaneurysms (MA) in color fundus images, which plays a key role in computer-assisted early diagnosis of diabetic re...

  13. MAPPING OF PLANETARY SURFACE AGE BASED ON CRATER STATISTICS OBTAINED BY AN AUTOMATIC DETECTION ALGORITHM

    Directory of Open Access Journals (Sweden)

    A. L. Salih

    2016-06-01

    Full Text Available The analysis of the impact crater size-frequency distribution (CSFD is a well-established approach to the determination of the age of planetary surfaces. Classically, estimation of the CSFD is achieved by manual crater counting and size determination in spacecraft images, which, however, becomes very time-consuming for large surface areas and/or high image resolution. With increasing availability of high-resolution (nearly global image mosaics of planetary surfaces, a variety of automated methods for the detection of craters based on image data and/or topographic data have been developed. In this contribution a template-based crater detection algorithm is used which analyses image data acquired under known illumination conditions. Its results are used to establish the CSFD for the examined area, which is then used to estimate the absolute model age of the surface. The detection threshold of the automatic crater detection algorithm is calibrated based on a region with available manually determined CSFD such that the age inferred from the manual crater counts corresponds to the age inferred from the automatic crater detection results. With this detection threshold, the automatic crater detection algorithm can be applied to a much larger surface region around the calibration area. The proposed age estimation method is demonstrated for a Kaguya Terrain Camera image mosaic of 7.4 m per pixel resolution of the floor region of the lunar crater Tsiolkovsky, which consists of dark and flat mare basalt and has an area of nearly 10,000 km2. The region used for calibration, for which manual crater counts are available, has an area of 100 km2. In order to obtain a spatially resolved age map, CSFDs and surface ages are computed for overlapping quadratic regions of about 4.4 x 4.4 km2 size offset by a step width of 74 m. Our constructed surface age map of the floor of Tsiolkovsky shows age values of typically 3.2-3.3 Ga, while for small regions lower (down to

  14. Automatic seizure detection in SEEG using high frequency activities in wavelet domain.

    Science.gov (United States)

    Ayoubian, L; Lacoma, H; Gotman, J

    2013-03-01

    Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  15. Semi-automatic detection of scoliotic rib borders using chest radiographs.

    Science.gov (United States)

    Plourde, F; Cheriet, F; Dansereau, J

    2006-01-01

    Stereoradiography is a well known technique to obtain 3D reconstructions of the rib cage. However, clinical applications are limited by the associated 2D rib detection method. Either this detection is widely supervised and time-consuming for the user, or it is fully automatic and not accurate enough for proper 3D reconstruction or clinical indices extraction. To address these issues, we propose a novel, semi-automated technique for detecting scoliotic rib borders in PA-0 degrees and PA-20 degrees chest X-ray images, using a modified edge-following approach. The novelty consists in following multiple promising edges simultaneously. Detections are initiated from starting points (input by the user) along the upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among the detected edges. Promising results show the superiority of this approach over classical rib detection in terms of accuracy. Moreover, the proposed method is of great relevancy in the scoliotic context since scoliotic ribs present very few shape priors, due to their irregularities, and hence, standard rib detection techniques become unsuitable.

  16. Automatic vehicle detection based on automatic histogram-based fuzzy C-means algorithm and perceptual grouping using very high-resolution aerial imagery and road vector data

    Science.gov (United States)

    Ghaffarian, Saman; Gökaşar, Ilgın

    2016-01-01

    This study presents an approach for the automatic detection of vehicles using very high-resolution images and road vector data. Initially, road vector data and aerial images are integrated to extract road regions. Then, the extracted road/street region is clustered using an automatic histogram-based fuzzy C-means algorithm, and edge pixels are detected using the Canny edge detector. In order to automatically detect vehicles, we developed a local perceptual grouping approach based on fusion of edge detection and clustering outputs. To provide the locality, an ellipse is generated using characteristics of the candidate clusters individually. Then, ratio of edge pixels to nonedge pixels in the corresponding ellipse is computed to distinguish the vehicles. Finally, a point-merging rule is conducted to merge the points that satisfy a predefined threshold and are supposed to denote the same vehicles. The experimental validation of the proposed method was carried out on six very high-resolution aerial images that illustrate two highways, two shadowed roads, a crowded narrow street, and a street in a dense urban area with crowded parked vehicles. The evaluation of the results shows that our proposed method performed 86% and 83% in overall correctness and completeness, respectively.

  17. Automatic detection of subglacial lakes in radar sounder data acquired in Antarctica

    Science.gov (United States)

    Ilisei, Ana-Maria; Khodadadzadeh, Mahdi; Dalsasso, Emanuele; Bruzzone, Lorenzo

    2017-10-01

    Subglacial lakes decouple the ice sheet from the underlying bedrock, thus facilitating the sliding of the ice masses towards the borders of the continents, consequently raising the sea level. This motivated increasing attention in the detection of subglacial lakes. So far, about 70% of the total number of subglacial lakes in Antarctica have been detected by analysing radargrams acquired by radar sounder (RS) instruments. Although the amount of radargrams is expected to drastically increase, from both airborne and possible future Earth observation RS missions, currently the main approach to the detection of subglacial lakes in radargrams is by visual interpretation. This approach is subjective and extremely time consuming, thus difficult to apply to a large amount of radargrams. In order to address the limitations of the visual interpretation and to assist glaciologists in better understanding the relationship between the subglacial environment and the climate system, in this paper, we propose a technique for the automatic detection of subglacial lakes. The main contribution of the proposed technique is the extraction of features for discriminating between lake and non-lake basal interfaces. In particular, we propose the extraction of features that locally capture the topography of the basal interface, the shape and the correlation of the basal waveforms. Then, the extracted features are given as input to a supervised binary classifier based on Support Vector Machine to perform the automatic subglacial lake detection. The effectiveness of the proposed method is proven both quantitatively and qualitatively by applying it to a large dataset acquired in East Antarctica by the MultiChannel Coherent Radar Depth Sounder.

  18. Automatic detection of referral patients due to retinal pathologies through data mining.

    Science.gov (United States)

    Quellec, Gwenolé; Lamard, Mathieu; Erginay, Ali; Chabouis, Agnès; Massin, Pascale; Cochener, Béatrice; Cazuguel, Guy

    2016-04-01

    With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully

  19. Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy

    Science.gov (United States)

    Mattonen, Sarah A.; Palma, David A.; Haasbeek, Cornelis J. A.; Senan, Suresh; Ward, Aaron D.

    2015-03-01

    Stereotactic ablative radiotherapy (SABR) is a treatment for early-stage lung cancer with local control rates comparable to surgery. After SABR, benign radiation induced lung injury (RILI) results in tumour-mimicking changes on computed tomography (CT) imaging. Distinguishing recurrence from RILI is a critical clinical decision determining the need for potentially life-saving salvage therapies whose high risks in this population dictate their use only for true recurrences. Current approaches do not reliably detect recurrence within a year post-SABR. We measured the detection accuracy of texture features within automatically determined regions of interest, with the only operator input being the single line segment measuring tumour diameter, normally taken during the clinical workflow. Our leave-one-out cross validation on images taken 2-5 months post-SABR showed robustness of the entropy measure, with classification error of 26% and area under the receiver operating characteristic curve (AUC) of 0.77 using automatic segmentation; the results using manual segmentation were 24% and 0.75, respectively. AUCs for this feature increased to 0.82 and 0.93 at 8-14 months and 14-20 months post SABR, respectively, suggesting even better performance nearer to the date of clinical diagnosis of recurrence; thus this system could also be used to support and reinforce the physician's decision at that time. Based on our ongoing validation of this automatic approach on a larger sample, we aim to develop a computer-aided diagnosis system which will support the physician's decision to apply timely salvage therapies and prevent patients with RILI from undergoing invasive and risky procedures.

  20. An automatic system for the detection of dairy cows lying behaviour in free-stall barns

    Directory of Open Access Journals (Sweden)

    Simona M.C. Porto

    2013-09-01

    Full Text Available In this paper, a method for the automatic detection of dairy cow lying behaviour in free-stall barns is proposed. A computer visionbased system (CVBS composed of a video-recording system and a cow lying behaviour detector based on the Viola Jones algorithm was developed. The CVBS performance was tested in a head-to-head free stall barn. Two classifiers were implemented in the software component of the CVBS to obtain the cow lying behaviour detector. The CVBS was validated by comparing its detection results with those generated from visual recognition. This comparison allowed the following accuracy indices to be calculated: the branching factor (BF, the miss factor (MF, the sensitivity, and the quality percentage (QP. The MF value of approximately 0.09 showed that the CVBS missed one cow every 11 well detected cows. Conversely, the BF value of approximately 0.08 indicated that one false positive was detected every 13 well detected cows. The high value of approximately 0.92 obtained for the sensitivity index and that obtained for QP of about 0.85 revealed the ability of the proposed system to detect cows lying in the stalls.

  1. DESIGN AND DEVELOP A COMPUTER AIDED DESIGN FOR AUTOMATIC EXUDATES DETECTION FOR DIABETIC RETINOPATHY SCREENING

    Directory of Open Access Journals (Sweden)

    C. A. SATHIYAMOORTHY

    2016-04-01

    Full Text Available Diabetic Retinopathy is a severe and widely spread eye disease which can lead to blindness. One of the main symptoms for vision loss is Exudates and it could be prevented by applying an early screening process. In the Existing systems, a Fuzzy C-Means Clustering technique is used for detecting the exudates for analyzation. The main objective of this paper is, to improve the efficiency of the Exudates detection in diabetic retinopathy images. To do this, a three Stage – [TS] approach is introduced for detecting and extracting the exudates automatically from the retinal images for screening the Diabetic retinopathy. TS functions on the image in three levels such as Pre-processing the image, enhancing the image and detecting the Exudates accurately. After successful detection, the detected exudates are classified using GLCM method for finding the accuracy. The TS approach is experimented using MATLAB software and the performance evaluation can be proved by comparing the results with the existing approach’s result and with the hand-drawn ground truths images from the expert ophthalmologist.

  2. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  3. Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.

    Science.gov (United States)

    Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A; Zhu, Wei-Ping; Ahmad, M Omair

    2018-03-01

    Wireless capsule endoscopy (WCE) is capable of demonstrating the entire gastrointestinal tract at an expense of exhaustive reviewing process for detecting bleeding disorders. The main objective is to develop an automatic method for identifying the bleeding frames and zones from WCE video. Different statistical features are extracted from the overlapping spatial blocks of the preprocessed WCE image in a transformed color plane containing green to red pixel ratio. The unique idea of the proposed method is to first perform unsupervised clustering of different blocks for obtaining two clusters and then extract cluster based features (CBFs). Finally, a global feature consisting of the CBFs and differential CBF is used to detect bleeding frame via supervised classification. In order to handle continuous WCE video, a post-processing scheme is introduced utilizing the feature trends in neighboring frames. The CBF along with some morphological operations is employed to identify bleeding zones. Based on extensive experimentation on several WCE videos, it is found that the proposed method offers significantly better performance in comparison to some existing methods in terms of bleeding detection accuracy, sensitivity, specificity and precision in bleeding zone detection. It is found that the bleeding detection performance obtained by using the proposed CBF based global feature is better than the feature extracted from the non-clustered image. The proposed method can reduce the burden of physicians in investigating WCE video to detect bleeding frame and zone with a high level of accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Computer program for analysis of impedance cardiography signals enabling manual correction of points detected automatically

    Science.gov (United States)

    Oleksiak, Justyna; Cybulski, Gerard

    2014-11-01

    The aim of this work was to create a computer program, written in LabVIEW, which enables the visualization and analysis of hemodynamic parameters. It allows the user to import data collected using ReoMonitor, an ambulatory monitoring impedance cardiography (AICG) device. The data include one channel of the ECG and one channel of the first derivative of the impedance signal (dz/dt) sampled at 200Hz and the base impedance signal (Z0) sampled every 8s. The program consist of two parts: a bioscope allowing the presentation of traces (ECG, AICG, Z0) and an analytical portion enabling the detection of characteristic points on the signals and automatic calculation of hemodynamic parameters. The detection of characteristic points in both signals is done automatically, with the option to make manual corrections, which may be necessary to avoid "false positive" recognitions. This application is used to determine the values of basic hemodynamic variables: pre-ejection period (PEP), left ventricular ejection time (LVET), stroke volume (SV), cardiac output (CO), and heart rate (HR). It leaves room for further development of additional features, for both the analysis panel and the data acquisition function.

  5. A FUZZY AUTOMATIC CAR DETECTION METHOD BASED ON HIGH RESOLUTION SATELLITE IMAGERY AND GEODESIC MORPHOLOGY

    Directory of Open Access Journals (Sweden)

    N. Zarrinpanjeh

    2017-09-01

    Full Text Available Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  6. a Fuzzy Automatic CAR Detection Method Based on High Resolution Satellite Imagery and Geodesic Morphology

    Science.gov (United States)

    Zarrinpanjeh, N.; Dadrassjavan, F.

    2017-09-01

    Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  7. Automatic multiresolution age-related macular degeneration detection from fundus images

    Science.gov (United States)

    Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

    2014-03-01

    Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

  8. Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

    Science.gov (United States)

    Rahnemoonfar, Maryam; Foster, Jamie; Starek, Michael J.

    2017-05-01

    Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

  9. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Richard Washington

    2008-11-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T- intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  10. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  11. Automatic REM Sleep Detection Associated with Idiopathic REM Sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sørensen, Gertrud Laura; Sørensen, Helge Bjarup Dissing

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG....... Method: Ten normal controls and ten age matched patients diagnosed with RBD were enrolled. All subjects underwent one polysomnographic (PSG) recording, which was manual scored according to the new sleep-scoring standard from the American Academy of Sleep Medicine. Based on the manual scoring...

  12. A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Bohui Zhu

    2013-01-01

    Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

  13. NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER’S DISEASE: A REVIEW

    Directory of Open Access Journals (Sweden)

    Rupali Kamathe

    2017-08-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

  14. Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve.

    Science.gov (United States)

    Xu, Lili; Luo, Shuqian

    2010-01-01

    Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

  15. Automatic optical detection and classification of marine animals around MHK converters using machine vision

    Energy Technology Data Exchange (ETDEWEB)

    Brunton, Steven [Univ. of Washington, Seattle, WA (United States)

    2018-01-15

    Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

  16. Automatic Detection of P and S Phases by Support Vector Machine

    Science.gov (United States)

    Jiang, Y.; Ning, J.; Bao, T.

    2017-12-01

    Many methods in seismology rely on accurately picked phases. A well performed program on automatically phase picking will assure the application of these methods. Related researches before mostly focus on finding different characteristics between noise and phases, which are all not enough successful. We have developed a new method which mainly based on support vector machine to detect P and S phases. In it, we first input some waveform pieces into the support vector machine, then employ it to work out a hyper plane which can divide the space into two parts: respectively noise and phase. We further use the same method to find a hyper plane which can separate the phase space into P and S parts based on the three components' cross-correlation matrix. In order to further improve the ability of phase detection, we also employ array data. At last, we show that the overall effect of our method is robust by employing both synthetic and real data.

  17. Automatic decision support system based on SAR data for oil spill detection

    Science.gov (United States)

    Mera, David; Cotos, José M.; Varela-Pet, José; Rodríguez, Pablo G.; Caro, Andrés

    2014-11-01

    Global trade is mainly supported by maritime transport, which generates important pollution problems. Thus, effective surveillance and intervention means are necessary to ensure proper response to environmental emergencies. Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillages on the oceans surface. Several decision support systems have been based on this technology. This paper presents an automatic oil spill detection system based on SAR data which was developed on the basis of confirmed spillages and it was adapted to an important international shipping route off the Galician coast (northwest Iberian Peninsula). The system was supported by an adaptive segmentation process based on wind data as well as a shape oriented characterization algorithm. Moreover, two classifiers were developed and compared. Thus, image testing revealed up to 95.1% candidate labeling accuracy. Shared-memory parallel programming techniques were used to develop algorithms in order to improve above 25% of the system processing time.

  18. Automatic Channel Fault Detection on a Small Animal APD-Based Digital PET Scanner

    Science.gov (United States)

    Charest, Jonathan; Beaudoin, Jean-François; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

    2014-10-01

    Avalanche photodiode (APD) based positron emission tomography (PET) scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure the maximal performance, these PET scanners require proper calibration by qualified scanner operators, which can become a cumbersome task because of the huge number of channels they are made of. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main activities of the IS consists in analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available data on individual channels into parameters that are subsequently used by the fault detection block to generate the channel health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of PET scanner performance: sensitivity, timing, crystal identification and energy. Some experiments on a 8 cm axial length LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 10% (with a 95% confidence interval (CI) of [9, 11]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 96% (CI: [95, 97]), which proves that many faults can be detected automatically. Increased fault detection efficiency would be

  19. CRISPR Recognition Tool (CRT): a tool for automatic detection ofclustered regularly interspaced palindromic repeats

    Energy Technology Data Exchange (ETDEWEB)

    Bland, Charles; Ramsey, Teresa L.; Sabree, Fareedah; Lowe,Micheal; Brown, Kyndall; Kyrpides, Nikos C.; Hugenholtz, Philip

    2007-05-01

    Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes. CRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT also demonstrated superior performance, especially for genomes containing large numbers of repeats. In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, was shown to be a significant improvement over the current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(n) in space and O(nm/l) in time.

  20. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data.

    Science.gov (United States)

    Brunner, Gerd; Chittajallu, Deepak R; Kurkure, Uday; Kakadiaris, Ioannis A

    2010-10-01

    Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (± 15.80)%, 93.54 (± 1.98)%, and 85.27 (± 14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (± 7.78)%, 96.57 (± 4.90)%, and 73.58 (± 8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.

  1. Automatic detection of non-convulsive seizures: A reduced complexity approach

    Directory of Open Access Journals (Sweden)

    Tazeem Fatma

    2016-10-01

    Full Text Available Detection of non-convulsive seizures (NCSz is a challenging task because they lack convulsions, meaning no physical visible symptoms are there to detect the presence of a seizure activity. Hence their diagnosis is not easy, also continuous observation of full length EEG for the detection of non-convulsive seizures (NCSz by an expert or a technician is a very exhaustive, time consuming job. A technique for the automatic detection of NCSz is proposed in this paper. The database used in this research was recorded at the All India Institute of Medical Sciences (AIIMS, New Delhi. 13 EEG recordings of 9 subjects consisting of a total 23 seizures of 29.42 min duration were used for analysis. Normalized modified Wilson amplitude is used as a key feature to classify between normal and seizure activity. The main advantage of this study lies in the fact that no classifier is used here and hence algorithm is very simple and computationally fast. With the use of only one feature, all of the seizures under test were detected correctly, and hence the median sensitivity and specificity of 100% and 99.21% were achieved respectively.

  2. Deep Learning-Based Data Forgery Detection in Automatic Generation Control

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Fengli [Univ. of Arkansas, Fayetteville, AR (United States); Li, Qinghua [Univ. of Arkansas, Fayetteville, AR (United States)

    2017-10-09

    Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

  3. A transfer learning approach to goodness of pronunciation based automatic mispronunciation detection.

    Science.gov (United States)

    Huang, Hao; Xu, Haihua; Hu, Ying; Zhou, Gang

    2017-11-01

    Goodness of pronunciation (GOP) is the most widely used method for automatic mispronunciation detection. In this paper, a transfer learning approach to GOP based mispronunciation detection when applying maximum F1-score criterion (MFC) training to deep neural network (DNN)-hidden Markov model based acoustic models is proposed. Rather than train the whole network using MFC, a DNN is used, whose hidden layers are borrowed from native speech recognition with only the softmax layer trained according to the MFC objective function. As a result, significant mispronunciation detection improvement is obtained. In light of this, the two-stage transfer learning based GOP is investigated in depth. The first stage exploits the hidden layer(s) to extract phonetic-discriminating features. The second stage uses a trainable softmax layer to learn the human standard for judgment. The validation is carried out by experimenting with different mispronunciation detection architectures using acoustic models trained by different criteria. It is found that it is preferable to use frame-level cross-entropy to train the hidden layer parameters. Classifier based mispronunciation detection is further experimented with using features computed by transfer learning based GOP and it is shown that it also helps to achieve better results.

  4. Early Detection and Localization of Downhole Incidents in Managed Pressure Drilling

    DEFF Research Database (Denmark)

    Willersrud, Anders; Imsland, Lars; Blanke, Mogens

    2015-01-01

    Downhole incidents such as kick, lost circulation, pack-off, and hole cleaning issues are important contributors to downtime in drilling. In managed pressure drilling (MPD), operations margins are typically narrower, implying more frequent incidents and more severe consequences. Detection and han...

  5. Towards an automatic lab-on-valve-ion mobility spectrometric system for detection of cocaine abuse.

    Science.gov (United States)

    Cocovi-Solberg, David J; Esteve-Turrillas, Francesc A; Armenta, Sergio; de la Guardia, Miguel; Miró, Manuel

    2017-08-25

    A lab-on-valve miniaturized system integrating on-line disposable micro-solid phase extraction has been interfaced with ion mobility spectrometry for the accurate and sensitive determination of cocaine and ecgonine methyl ester in oral fluids. The method is based on the automatic loading of 500μL of oral fluid along with the retention of target analytes and matrix clean-up by mixed-mode cationic/reversed-phase solid phase beads, followed by elution with 100μL of 2-propanol containing (3% v/v) ammonia, which are online injected into the IMS. The sorptive particles are automatically discarded after every individual assay inasmuch as the sorptive capacity of the sorbent material is proven to be dramatically deteriorated with reuse. The method provided a limit of detection of 0.3 and 0.14μgL -1 for cocaine and ecgonine methyl ester, respectively, with relative standard deviation values from 8 till 14% with a total analysis time per sample of 7.5min. Method trueness was evaluated by analyzing oral fluid samples spiked with cocaine at different concentration levels (1, 5 and 25μgL -1 ) affording relative recoveries within the range of 85±24%. Fifteen saliva samples were collected from volunteers and analysed following the proposed automatic procedure, showing a 40% cocaine occurrence with concentrations ranging from 1.3 to 97μgL -1 . Field saliva samples were also analysed by reference methods based on lateral flow immunoassay and gas chromatography-mass spectrometry. The application of this procedure to the control of oral fluids of cocaine consumers represents a step forward towards the development of a point-of-care cocaine abuse sensing system. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Development and testing of incident detection algorithms. Vol. 2, research methodology and detailed results.

    Science.gov (United States)

    1976-04-01

    The development and testing of incident detection algorithms was based on Los Angeles and Minneapolis freeway surveillance data. Algorithms considered were based on times series and pattern recognition techniques. Attention was given to the effects o...

  7. Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

    Science.gov (United States)

    Kage, Andreas; Legal, Wolfgang; Kelm, Peter; Simon, Jörg; Bergen, Tobias; Münzenmayer, Christian; Benz, Michaela

    2012-03-01

    Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

  8. Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

    Science.gov (United States)

    Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

    2017-06-01

    The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

  9. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    Science.gov (United States)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  10. Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method

    Directory of Open Access Journals (Sweden)

    Hamid Reza Pourreza

    2009-03-01

    Full Text Available Introduction: Diabetic retinopathy (DR is one of the most serious and most frequent eye diseases in the world and the most common cause of blindness in adults between 20 and 60 years of age. Following 15 years of diabetes, about 2% of the diabetic patients are blind and 10% suffer from vision impairment due to DR complications. This paper addresses the automatic detection of microaneurysms (MA in color fundus images, which plays a key role in computer-assisted early diagnosis of diabetic retinopathy. Materials and Methods: The algorithm can be divided into three main steps. The purpose of the first step or pre-processing is background normalization and contrast enhancement of the images. The second step aims to detect candidates, i.e., all patterns possibly corresponding to MA, which is achieved using a local radon transform, Then, features are extracted, which are used in the last step to automatically classify the candidates into real MA or other objects using the SVM method. A database of 100 annotated images was used to test the algorithm. The algorithm was compared to manually obtained gradings of these images. Results: The sensitivity of diagnosis for DR was 100%, with specificity of 90% and the sensitivity of precise MA localization was 97%, at an average number of 5 false positives per image. Discussion and Conclusion: Sensitivity and specificity of this algorithm make it one of the best methods in this field. Using the local radon transform in this algorithm eliminates the noise sensitivity for MA detection in retinal image analysis.

  11. Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images.

    Science.gov (United States)

    Protonotarios, Nicholas E; Spyrou, George M; Kastis, George A

    2017-06-01

    The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Section based traffic detection on motorways for incident management

    NARCIS (Netherlands)

    Noort, M. van; Klunder, G.

    2007-01-01

    Current vehicle detection on motorways is based generally on either inductive loop systems or various alternatives such as video cameras. Recently, we encountered two new developments that take a different approach: one from The Netherlands using microwave sensors, and the other from Sweden using

  13. Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

    Directory of Open Access Journals (Sweden)

    Jie Su

    2016-01-01

    Full Text Available We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500, lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression classifier was used individually; while the recognition rate was significantly higher (95.642% when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44% of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review.

  14. Automatic detection and measurement of viral replication compartments by ellipse adjustment

    Science.gov (United States)

    Garcés, Yasel; Guerrero, Adán; Hidalgo, Paloma; López, Raul Eduardo; Wood, Christopher D.; Gonzalez, Ramón A.; Rendón-Mancha, Juan Manuel

    2016-11-01

    Viruses employ a variety of strategies to hijack cellular activities through the orchestrated recruitment of macromolecules to specific virus-induced cellular micro-environments. Adenoviruses (Ad) and other DNA viruses induce extensive reorganization of the cell nucleus and formation of nuclear Replication Compartments (RCs), where the viral genome is replicated and expressed. In this work an automatic algorithm designed for detection and segmentation of RCs using ellipses is presented. Unlike algorithms available in the literature, this approach is deterministic, automatic, and can adjust multiple RCs using ellipses. The proposed algorithm is non iterative, computationally efficient and is invariant to affine transformations. The method was validated over both synthetic images and more than 400 real images of Ad-infected cells at various timepoints of the viral replication cycle obtaining relevant information about the biogenesis of adenoviral RCs. As proof of concept the algorithm was then used to quantitatively compare RCs in cells infected with the adenovirus wild type or an adenovirus mutant that is null for expression of a viral protein that is known to affect activities associated with RCs that result in deficient viral progeny production.

  15. Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

  16. Automatic detection of optic disc based on PCA and mathematical morphology.

    Science.gov (United States)

    Morales, Sandra; Naranjo, Valery; Angulo, Us; Alcaniz, Mariano

    2013-04-01

    The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.

  17. Automatic detection of measurement points for non-contact vibrometer-based diagnosis of cardiac arrhythmias

    Science.gov (United States)

    Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter

    2017-03-01

    Monitoring of the heart rhythm is the cornerstone of the diagnosis of cardiac arrhythmias. It is done by means of electrocardiography which relies on electrodes attached to the skin of the patient. We present a new system approach based on the so-called vibrocardiogram that allows an automatic non-contact registration of the heart rhythm. Because of the contactless principle, the technique offers potential application advantages in medical fields like emergency medicine (burn patient) or premature baby care where adhesive electrodes are not easily applicable. A laser-based, mobile, contactless vibrometer for on-site diagnostics that works with the principle of laser Doppler vibrometry allows the acquisition of vital functions in form of a vibrocardiogram. Preliminary clinical studies at the Klinikum Karlsruhe have shown that the region around the carotid artery and the chest region are appropriate therefore. However, the challenge is to find a suitable measurement point in these parts of the body that differs from person to person due to e. g. physiological properties of the skin. Therefore, we propose a new Microsoft Kinect-based approach. When a suitable measurement area on the appropriate parts of the body are detected by processing the Kinect data, the vibrometer is automatically aligned on an initial location within this area. Then, vibrocardiograms on different locations within this area are successively acquired until a sufficient measuring quality is achieved. This optimal location is found by exploiting the autocorrelation function.

  18. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  19. CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

    Science.gov (United States)

    Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

    2018-01-01

    Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

  20. Automatic Change Detection for Real-Time Monitoring of EEG Signals

    Directory of Open Access Journals (Sweden)

    Zhen Gao

    2018-04-01

    Full Text Available In recent years, automatic change detection for real-time monitoring of electroencephalogram (EEG signals has attracted widespread interest with a large number of clinical applications. However, it is still a challenging problem. This paper presents a novel framework for this task where joint time-domain features are firstly computed to extract temporal fluctuations of a given EEG data stream; and then, an auto-regressive (AR linear model is adopted to model the data and temporal anomalies are subsequently calculated from that model to reflect the possibilities that a change occurs; a non-parametric statistical test based on Randomized Power Martingale (RPM is last performed for making change decision from the resulting anomaly scores. We conducted experiments on the publicly-available Bern-Barcelona EEG database where promising results for terms of detection precision (96.97%, detection recall (97.66% as well as computational efficiency have been achieved. Meanwhile, we also evaluated the proposed method for real detection of seizures occurrence for a monitoring epilepsy patient. The results of experiments by using both the testing database and real application demonstrated the effectiveness and feasibility of the method for the purpose of change detection in EEG signals. The proposed framework has two additional properties: (1 it uses a pre-defined AR model for modeling of the past observed data so that it can be operated in an unsupervised manner, and (2 it uses an adjustable threshold to achieve a scalable decision making so that a coarse-to-fine detection strategy can be developed for quick detection or further analysis purposes.

  1. Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images.

    Science.gov (United States)

    Dos Santos, Alessio Moreira; Mitja, Danielle; Delaître, Eric; Demagistri, Laurent; de Souza Miranda, Izildinha; Libourel, Thérèse; Petit, Michel

    2017-05-15

    High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the "Compt-palm" algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale. Copyright

  2. Automatic detection of the macula in retinal fundus images using seeded mode tracking approach.

    Science.gov (United States)

    Wong, Damon W K; Liu, Jiang; Tan, Ngan-Meng; Yin, Fengshou; Cheng, Xiangang; Cheng, Ching-Yu; Cheung, Gemmy C M; Wong, Tien Yin

    2012-01-01

    The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

  3. Detection and incidence of Pernospora variabilis in quinoa seeds

    OpenAIRE

    Testen, A.M.; Backman, Paul A.

    2012-01-01

    This poster describes the research undertaken to determine the level of imported quinoa contamination with quinoa downy mildew, caused by Pernospora variabilis, as well as to develop a rapid method of detection by DNA primers. The majority of lots coming from a wide variety of sources were found to have been contaminated with the pathogen, indicating it is more widespread than anticipated. Additionally, DNA primers for P. variabilis were shown to be effective in identifying most cases of cont...

  4. Statistical Analysis of Automatic Seed Word Acquisition to Improve Harmful Expression Extraction in Cyberbullying Detection

    Directory of Open Access Journals (Sweden)

    Suzuha Hatakeyama

    2016-04-01

    Full Text Available We study the social problem of cyberbullying, defined as a new form of bullying that takes place in the Internet space. This paper proposes a method for automatic acquisition of seed words to improve performance of the original method for the cyberbullying detection by Nitta et al. [1]. We conduct an experiment exactly in the same settings to find out that the method based on a Web mining technique, lost over 30% points of its performance since being proposed in 2013. Thus, we hypothesize on the reasons for the decrease in the performance and propose a number of improvements, from which we experimentally choose the best one. Furthermore, we collect several seed word sets using different approaches, evaluate and their precision. We found out that the influential factor in extraction of harmful expressions is not the number of seed words, but the way the seed words were collected and filtered.

  5. AUTOMATIC DETECTION ALGORITHM OF DYNAMIC PRESSURE PULSES IN THE SOLAR WIND

    International Nuclear Information System (INIS)

    Zuo, Pingbing; Feng, Xueshang; Wang, Yi; Xie, Yanqiong; Li, Huijun; Xu, Xiaojun

    2015-01-01

    Dynamic pressure pulses (DPPs) in the solar wind are a significant phenomenon closely related to the solar-terrestrial connection and physical processes of solar wind dynamics. In order to automatically identify DPPs from solar wind measurements, we develop a procedure with a three-step detection algorithm that is able to rapidly select DPPs from the plasma data stream and simultaneously define the transition region where large dynamic pressure variations occur and demarcate the upstream and downstream region by selecting the relatively quiet status before and after the abrupt change in dynamic pressure. To demonstrate the usefulness, efficiency, and accuracy of this procedure, we have applied it to the Wind observations from 1996 to 2008 by successfully obtaining the DPPs. The procedure can also be applied to other solar wind spacecraft observation data sets with different time resolutions

  6. Automatic detection of photoresist residual layer in lithography using a neural classification approach

    KAUST Repository

    Gereige, Issam

    2012-09-01

    Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

  7. Comparison of algorithms for automatic border detection of melanoma in dermoscopy images

    Science.gov (United States)

    Srinivasa Raghavan, Sowmya; Kaur, Ravneet; LeAnder, Robert

    2016-09-01

    Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.

  8. Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

    Science.gov (United States)

    Máttyus, G.

    2013-05-01

    Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

  9. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

    Science.gov (United States)

    Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

    2014-03-01

    This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative

  10. AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    Elaheh Aghabalaei Khordehchi

    2017-06-01

    Full Text Available Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs are extracted from the smoothed images. The Statistical Region Merging (SRM algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

  11. A temporal constraint for automatic deviance detection and object formation: A mismatch negativity study.

    Science.gov (United States)

    Weise, Annekathrin; Grimm, Sabine; Müller, Dagmar; Schröger, Erich

    2010-05-17

    The automatic detection of deviations within a constant sine wave tone is confined to the initial part of approximately 350 ms. When a deviation occurs beyond this critical limit, the mismatch negativity (MMN) - a deviance-related component of the event-related potential - is largely attenuated or even absent. However, for time-variant acoustic stimuli such as speech sounds or tonal patterns, MMN is also obtained for deviations beyond the initial 350 ms. We consider two hypotheses that can explain the MMN to time-variant sounds. One is that the terminal part of those sounds is represented as the spectral information varies over time (spectral-variation hypothesis). The other is that transients, occurring in time-variant signals, help to segment the long sounds into smaller units, each being not larger than the critical 350 ms (segmentation hypothesis). We measured MMN to duration shortenings (deviants) embedded in a sequence of 1000 ms long standard tones of increasing frequency (sweeps). The sweeps did or did not contain a noise burst. Results reveal a lack of MMN to the duration deviant in the sweep without a noise burst, which rules out the spectral-variation hypothesis. The presence of MMN to the duration deviant in the sweep with a noise burst supports the segmentation hypothesis. Thus, the results suggest a temporal constraint inherent to the processing of unstructured/unsegmented long tones. We argue that transients within a sound act as segmentation cues providing an automatic sound representation for which deviations can be detected. Copyright 2010 Elsevier B.V. All rights reserved.

  12. Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

    Science.gov (United States)

    Sarker, Abeed; Gonzalez, Graciela

    2015-02-01

    Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments

  13. Rheticus Displacement: an Automatic Geo-Information Service Platform for Ground Instabilities Detection and Monitoring

    Science.gov (United States)

    Chiaradia, M. T.; Samarelli, S.; Agrimano, L.; Lorusso, A. P.; Nutricato, R.; Nitti, D. O.; Morea, A.; Tijani, K.

    2016-12-01

    Rheticus® is an innovative cloud-based data and services hub able to deliver Earth Observation added-value products through automatic complex processes and a minimum interaction with human operators. This target is achieved by means of programmable components working as different software layers in a modern enterprise system which relies on SOA (service-oriented-architecture) model. Due to its architecture, where every functionality is well defined and encapsulated in a standalone component, Rheticus is potentially highly scalable and distributable allowing different configurations depending on the user needs. Rheticus offers a portfolio of services, ranging from the detection and monitoring of geohazards and infrastructural instabilities, to marine water quality monitoring, wildfires detection or land cover monitoring. In this work, we outline the overall cloud-based platform and focus on the "Rheticus Displacement" service, aimed at providing accurate information to monitor movements occurring across landslide features or structural instabilities that could affect buildings or infrastructures. Using Sentinel-1 (S1) open data images and Multi-Temporal SAR Interferometry techniques (i.e., SPINUA), the service is complementary to traditional survey methods, providing a long-term solution to slope instability monitoring. Rheticus automatically browses and accesses (on a weekly basis) the products of the rolling archive of ESA S1 Scientific Data Hub; S1 data are then handled by a mature running processing chain, which is responsible of producing displacement maps immediately usable to measure with sub-centimetric precision movements of coherent points. Examples are provided, concerning the automatic displacement map generation process, as well as the integration of point and distributed scatterers, the integration of multi-sensors displacement maps (e.g., Sentinel-1 IW and COSMO-SkyMed HIMAGE), the combination of displacement rate maps acquired along both ascending

  14. Automatic Detection and Visualization of Qualitative Hemodynamic Characteristics in Cerebral Aneurysms.

    Science.gov (United States)

    Gasteiger, R; Lehmann, D J; van Pelt, R; Janiga, G; Beuing, O; Vilanova, A; Theisel, H; Preim, B

    2012-12-01

    Cerebral aneurysms are a pathological vessel dilatation that bear a high risk of rupture. For the understanding and evaluation of the risk of rupture, the analysis of hemodynamic information plays an important role. Besides quantitative hemodynamic information, also qualitative flow characteristics, e.g., the inflow jet and impingement zone are correlated with the risk of rupture. However, the assessment of these two characteristics is currently based on an interactive visual investigation of the flow field, obtained by computational fluid dynamics (CFD) or blood flow measurements. We present an automatic and robust detection as well as an expressive visualization of these characteristics. The detection can be used to support a comparison, e.g., of simulation results reflecting different treatment options. Our approach utilizes local streamline properties to formalize the inflow jet and impingement zone. We extract a characteristic seeding curve on the ostium, on which an inflow jet boundary contour is constructed. Based on this boundary contour we identify the impingement zone. Furthermore, we present several visualization techniques to depict both characteristics expressively. Thereby, we consider accuracy and robustness of the extracted characteristics, minimal visual clutter and occlusions. An evaluation with six domain experts confirms that our approach detects both hemodynamic characteristics reasonably.

  15. Automatic Polyp Detection in Pillcam Colon 2 Capsule Images and Videos: Preliminary Feasibility Report

    Directory of Open Access Journals (Sweden)

    Pedro N. Figueiredo

    2011-01-01

    Full Text Available Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.

  16. Infrared machine vision system for the automatic detection of olive fruit quality.

    Science.gov (United States)

    Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

    2013-11-15

    External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

  17. Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images

    Directory of Open Access Journals (Sweden)

    Ezhil E. Nithila

    2017-06-01

    Full Text Available Lung Cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis will be the only remedy. Computer-Aided Diagnosis (CAD plays a key role in the early detection and diagnosis of lung cancer. This paper develops a CAD system that focus on new heuristic search algorithm to optimize the Back Propagation Neural Network (BPNN in characterizing nodule from non-nodules. The proposed CAD system consists of four main stages: (i image acquisition (ii lesion detection, (iii texture feature extraction and (iv tumor characterization using a classifier. The optimization mechanism employs Particle Swarm Optimization (PSO with new inertia weight for NN in order to investigate the classification rate of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The experiments were conducted on CT images to classify into nodule and non-nodule from the tumor region of interest. The performance of the CAD system was evaluated for the texture characterized images taken from LIDC-IDRI and SPIE-AAPM databases. Due to improved inertia weight used in Particle Swarm (PS the CAD achieves highest classification accuracy of 98% for solid nodules, 99.5% for part solid nodules and 97.2% for non solid nodules respectively. The experimental results suggest that the developed CAD system has great potential and promise in the automatic diagnosis of tumors of lung.

  18. Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery

    Science.gov (United States)

    Ononye, Ambrose E.; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert L.; Cleveland, Thomas E.

    2009-05-01

    Aflatoxin is a mycotoxin predominantly produced by Aspergillus flavus and Aspergillus parasitiucus fungi that grow naturally in corn, peanuts and in a wide variety of other grain products. Corn, like other grains is used as food for human and feed for animal consumption. It is known that aflatoxin is carcinogenic; therefore, ingestion of corn infected with the toxin can lead to very serious health problems such as liver damage if the level of the contamination is high. The US Food and Drug Administration (FDA) has strict guidelines for permissible levels in the grain products for both humans and animals. The conventional approach used to determine these contamination levels is one of the destructive and invasive methods that require corn kernels to be ground and then chemically analyzed. Unfortunately, each of the analytical methods can take several hours depending on the quantity, to yield a result. The development of high spectral and spatial resolution imaging sensors has created an opportunity for hyperspectral image analysis to be employed for aflatoxin detection. However, this brings about a high dimensionality problem as a setback. In this paper, we propose a technique that automatically detects aflatoxin contaminated corn kernels by using dual-band imagery. The method exploits the fluorescence emission spectra from corn kernels captured under 365 nm ultra-violet light excitation. Our approach could lead to a non-destructive and non-invasive way of quantifying the levels of aflatoxin contamination. The preliminary results shown here, demonstrate the potential of our technique for aflatoxin detection.

  19. Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2014-03-01

    Full Text Available Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index that is combined with three other decision indices using the Dempster–Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM supervised classification.

  20. Results of automatic system implementation for Cofrentes power plant detection system LPRM inspection execution

    Energy Technology Data Exchange (ETDEWEB)

    Palomo, M., E-mail: mpalomo@iqn.upv.es [Departamento de Ingenieria Quimica y Nuclear, Universidad Politecnica de Valencia (Spain); Urrea, M., E-mail: matias.urrea@iberdrola.es [C.N.Cofrentes - Iberdrola Generacion S.A., Valencia (Spain); Curiel, M., E-mail: m.curiel@lainsa.com [LAINSA, Grupo Dominguis, Valencia (Brazil); Arnaldos, A., E-mail: a.arnaldos@titaniast.com [TITANIA Servicios Teconologicos, Valencia (Spain)

    2011-07-01

    During this presentation we are going to introduce the results of Cofrentes nuclear power plant automation of the detection system LPRM (Local Power Range Monitor) inspection procedure. An LPRM's test system has been developed and it consists in a software application and data acquisition hardware that performs automatically the complete detection system process: refueling, storage and operation inspection: Ramp voltage generation, measured voltage Plateaux evaluation, qualification report emission; historical analysis to scan burn evolution. The inspections differentiations are developed by the different specifications that it has to fulfil: operation inspection: it is made to check the fission bolt wearing, the detection system functioning and to analyse malfunctioning. From technical specifications and curves analyses it can be determined each LPRM's substitution. Storage inspection: it is made to check the correct functioning and isolation losses before being installed in the core during refueling. Refueling inspection: it is checked that storage LPRM's installation is correct and that they are ready for new fuel cycle. The software application LPRM's Test has been developed by National Instruments LabVIEW, and it performs the following actions: Protocol IEEE-488 (GPIB) control of the source KEITHLEY 237. This source generates the ramp voltage and measure voltage; information acquisition of storage, process and source, identifying LPRM and realization conditions of the same; data analysis and conditions report, historical comparative analysis. (author)

  1. A Robust Vision-based Runway Detection and Tracking Algorithm for Automatic UAV Landing

    KAUST Repository

    Abu Jbara, Khaled F.

    2015-05-01

    This work presents a novel real-time algorithm for runway detection and tracking applied to the automatic takeoff and landing of Unmanned Aerial Vehicles (UAVs). The algorithm is based on a combination of segmentation based region competition and the minimization of a specific energy function to detect and identify the runway edges from streaming video data. The resulting video-based runway position estimates are updated using a Kalman Filter, which can integrate other sensory information such as position and attitude angle estimates to allow a more robust tracking of the runway under turbulence. We illustrate the performance of the proposed lane detection and tracking scheme on various experimental UAV flights conducted by the Saudi Aerospace Research Center. Results show an accurate tracking of the runway edges during the landing phase under various lighting conditions. Also, it suggests that such positional estimates would greatly improve the positional accuracy of the UAV during takeoff and landing phases. The robustness of the proposed algorithm is further validated using Hardware in the Loop simulations with diverse takeoff and landing videos generated using a commercial flight simulator.

  2. Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.

    Science.gov (United States)

    Abd El Aziz, Mohamed; Selim, I M; Xiong, Shengwu

    2017-06-30

    This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

  3. Efficient and automatic image reduction framework for space debris detection based on GPU technology

    Science.gov (United States)

    Diprima, Francesco; Santoni, Fabio; Piergentili, Fabrizio; Fortunato, Vito; Abbattista, Cristoforo; Amoruso, Leonardo

    2018-04-01

    In the last years, the increasing number of space debris has triggered the need of a distributed monitoring system for the prevention of possible space collisions. Space surveillance based on ground telescope allows the monitoring of the traffic of the Resident Space Objects (RSOs) in the Earth orbit. This space debris surveillance has several applications such as orbit prediction and conjunction assessment. In this paper is proposed an optimized and performance-oriented pipeline for sources extraction intended to the automatic detection of space debris in optical data. The detection method is based on the morphological operations and Hough Transform for lines. Near real-time detection is obtained using General Purpose computing on Graphics Processing Units (GPGPU). The high degree of processing parallelism provided by GPGPU allows to split data analysis over thousands of threads in order to process big datasets with a limited computational time. The implementation has been tested on a large and heterogeneous images data set, containing both imaging satellites from different orbit ranges and multiple observation modes (i.e. sidereal and object tracking). These images were taken during an observation campaign performed from the EQUO (EQUatorial Observatory) observatory settled at the Broglio Space Center (BSC) in Kenya, which is part of the ASI-Sapienza Agreement.

  4. Automatic Detection of Changes on Mars Surface from High-Resolution Orbital Images

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2017-04-01

    Over the last 40 years Mars has been extensively mapped by several NASA and ESA orbital missions, generating a large image dataset comprised of approximately 500,000 high-resolution images (of human resources, which is very difficult to achieve when dealing with a rapidly increasing volume of data. Although citizen science can be employed for training and verification it is unsuitable for planetwide systematic change detection. In this work, we introduce a novel approach in planetary image change detection, which involves a batch-mode automatic change detection pipeline that identifies regions that have changed. This is tested in anger, on tens of thousands of high-resolution images over the MC11 quadrangle [5], acquired by CTX, HRSC, THEMIS-VIS and MOC-NA instruments [1]. We will present results which indicate a substantial level of activity in this region of Mars, including instances of dynamic natural phenomena that haven't been cataloged in the planetary science literature before. We will demonstrate the potential and usefulness of such an automatic approach in planetary science change detection. Acknowledgments: The research leading to these results has received funding from the STFC "MSSL Consolidated Grant" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n° 607379. References: [1] P. Sidiropoulos and J. - P. Muller (2015) On the status of orbital high-resolution repeat imaging of Mars for the observation of dynamic surface processes. Planetary and Space Science, 117: 207-222. [2] O. Aharonson, et al. (2003) Slope streak formation and dust deposition rates on Mars. Journal of Geophysical Research: Planets, 108(E12):5138 [3] A. McEwen, et al. (2011) Seasonal flows on warm martian slopes. Science, 333 (6043): 740-743. [4] S. Byrne, et al. (2009) Distribution of mid-latitude ground ice on mars from new impact craters. Science, 325(5948):1674-1676. [5] K. Gwinner, et al (2016) The

  5. Fire detection and incidents localization based on public information channels and social media

    Science.gov (United States)

    Thanos, Konstantinos-Georgios; Skroumpelou, Katerina; Rizogiannis, Konstantinos; Kyriazanos, Dimitris M.; Astyakopoulos, Alkiviadis; Thomopoulos, Stelios C. A.

    2017-05-01

    In this paper a solution is presented aiming to assist the early detection and localization of a fire incident by exploiting crowdsourcing and unofficial civilian online reports. It consists of two components: (a) the potential fire incident detection and (b) the visualization component. The first component comprises two modules that run in parallel and aim to collect reports posted on public platforms and conclude to potential fire incident locations. It collects the public reports, distinguishes reports that refer to a potential fire incident and store the corresponding information in a structured way. The second module aggregates all these stored reports and conclude to a probable fire location, based on the amount of reports per area, the time and location of these reports. In further the result is entered to a fusion module which combines it with information collected by sensors if available in order to provide a more accurate fire event detection capability. The visualization component is a fully - operational public information channel which provides accurate and up-to-date information about active and past fires, raises awareness about forest fires and the relevant hazards among citizens. The channel has visualization capabilities for presenting in an efficient way information regarding detected fire incidents fire expansion areas, and relevant information such as detecting sensors and reporting origin. The paper concludes with insight to current CONOPS end user with regards to the inclusion of the proposed solution to the current CONOPS of fire detection.

  6. EMS Response to Mass Casualty Incidents: The Critical Importance of Automatic Statewide Mutual Aid and MCI Training

    Science.gov (United States)

    2008-09-01

    rescue dummies and live individuals) that were placed at the Amphitheatre.61 Upon explosion of the bomb and radiological dispersal device from concert...automatic mutual aid organization serving the state of Illinois and singed on jurisdictions within Wisconsin, Indiana, and Missouri and several cities

  7. Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images.

    Science.gov (United States)

    Yang Li; Wei Liang; Yinlong Zhang; Haibo An; Jindong Tan

    2016-08-01

    Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity. Furthermore, a feature fusion deep learning (FFDL) model is introduced to combine two types of features of lumbar vertebrae X-ray images, which uses sobel kernel and Gabor kernel to obtain the contour and texture of lumbar vertebrae, respectively. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model performs more accurate in abnormal cases with pathologies and surgical implants in multi-angle views.

  8. Usefulness of Cone-Beam Computed Tomography and Automatic Vessel Detection Software in Emergency Transarterial Embolization

    Energy Technology Data Exchange (ETDEWEB)

    Carrafiello, Gianpaolo, E-mail: gcarraf@gmail.com; Ierardi, Anna Maria, E-mail: amierardi@yahoo.it; Duka, Ejona, E-mail: ejonaduka@hotmail.com [Insubria University, Department of Radiology, Interventional Radiology (Italy); Radaelli, Alessandro, E-mail: alessandro.radaelli@philips.com [Philips Healthcare (Netherlands); Floridi, Chiara, E-mail: chiara.floridi@gmail.com [Insubria University, Department of Radiology, Interventional Radiology (Italy); Bacuzzi, Alessandro, E-mail: alessandro.bacuzzi@ospedale.varese.it [University of Insubria, Anaesthesia and Palliative Care (Italy); Bucourt, Maximilian de, E-mail: maximilian.de-bucourt@charite.de [Charité - University Medicine Berlin, Department of Radiology (Germany); Marchi, Giuseppe De, E-mail: giuseppedemarchi@email.it [Insubria University, Department of Radiology, Interventional Radiology (Italy)

    2016-04-15

    BackgroundThis study was designed to evaluate the utility of dual phase cone beam computed tomography (DP-CBCT) and automatic vessel detection (AVD) software to guide transarterial embolization (TAE) of angiographically challenging arterial bleedings in emergency settings.MethodsTwenty patients with an arterial bleeding at computed tomography angiography and an inconclusive identification of the bleeding vessel at the initial 2D angiographic series were included. Accuracy of DP-CBCT and AVD software were defined as the ability to detect the bleeding site and the culprit arterial bleeder, respectively. Technical success was defined as the correct positioning of the microcatheter using AVD software. Clinical success was defined as the successful embolization. Total volume of iodinated contrast medium and overall procedure time were registered.ResultsThe bleeding site was not detected by initial angiogram in 20 % of cases, while impossibility to identify the bleeding vessel was the reason for inclusion in the remaining cases. The bleeding site was detected by DP-CBCT in 19 of 20 (95 %) patients; in one case CBCT-CT fusion was required. AVD software identified the culprit arterial branch in 18 of 20 (90 %) cases. In two cases, vessel tracking required manual marking of the candidate arterial bleeder. Technical success was 95 %. Successful embolization was achieved in all patients. Mean contrast volume injected for each patient was 77.5 ml, and mean overall procedural time was 50 min.ConclusionsC-arm CBCT and AVD software during TAE of angiographically challenging arterial bleedings is feasible and may facilitate successful embolization. Staff training in CBCT imaging and software manipulation is necessary.

  9. Usefulness of Cone-Beam Computed Tomography and Automatic Vessel Detection Software in Emergency Transarterial Embolization

    International Nuclear Information System (INIS)

    Carrafiello, Gianpaolo; Ierardi, Anna Maria; Duka, Ejona; Radaelli, Alessandro; Floridi, Chiara; Bacuzzi, Alessandro; Bucourt, Maximilian de; Marchi, Giuseppe De

    2016-01-01

    BackgroundThis study was designed to evaluate the utility of dual phase cone beam computed tomography (DP-CBCT) and automatic vessel detection (AVD) software to guide transarterial embolization (TAE) of angiographically challenging arterial bleedings in emergency settings.MethodsTwenty patients with an arterial bleeding at computed tomography angiography and an inconclusive identification of the bleeding vessel at the initial 2D angiographic series were included. Accuracy of DP-CBCT and AVD software were defined as the ability to detect the bleeding site and the culprit arterial bleeder, respectively. Technical success was defined as the correct positioning of the microcatheter using AVD software. Clinical success was defined as the successful embolization. Total volume of iodinated contrast medium and overall procedure time were registered.ResultsThe bleeding site was not detected by initial angiogram in 20 % of cases, while impossibility to identify the bleeding vessel was the reason for inclusion in the remaining cases. The bleeding site was detected by DP-CBCT in 19 of 20 (95 %) patients; in one case CBCT-CT fusion was required. AVD software identified the culprit arterial branch in 18 of 20 (90 %) cases. In two cases, vessel tracking required manual marking of the candidate arterial bleeder. Technical success was 95 %. Successful embolization was achieved in all patients. Mean contrast volume injected for each patient was 77.5 ml, and mean overall procedural time was 50 min.ConclusionsC-arm CBCT and AVD software during TAE of angiographically challenging arterial bleedings is feasible and may facilitate successful embolization. Staff training in CBCT imaging and software manipulation is necessary.

  10. Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models

    Science.gov (United States)

    Dammeier, Franziska; Moore, Jeffrey R.; Hammer, Conny; Haslinger, Florian; Loew, Simon

    2016-02-01

    Data from continuously recording permanent seismic networks can contain information about rockslide occurrence and timing complementary to eyewitness observations and thus aid in construction of robust event catalogs. However, detecting infrequent rockslide signals within large volumes of continuous seismic waveform data remains challenging and often requires demanding manual intervention. We adapted an automatic classification method using hidden Markov models to detect rockslide signals in seismic data from two stations in central Switzerland. We first processed 21 known rockslides, with event volumes spanning 3 orders of magnitude and station event distances varying by 1 order of magnitude, which resulted in 13 and 19 successfully classified events at the two stations. Retraining the models to incorporate seismic noise from the day of the event improved the respective results to 16 and 19 successful classifications. The missed events generally had low signal-to-noise ratio and small to medium volumes. We then processed nearly 14 years of continuous seismic data from the same two stations to detect previously unknown events. After postprocessing, we classified 30 new events as rockslides, of which we could verify three through independent observation. In particular, the largest new event, with estimated volume of 500,000 m3, was not generally known within the Swiss landslide community, highlighting the importance of regional seismic data analysis even in densely populated mountainous regions. Our method can be easily implemented as part of existing earthquake monitoring systems, and with an average event detection rate of about two per month, manual verification would not significantly increase operational workload.

  11. Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Till F. Sonnemann

    2017-12-01

    Full Text Available Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.

  12. Automatic facial pore analysis system using multi-scale pore detection.

    Science.gov (United States)

    Sun, J Y; Kim, S W; Lee, S H; Choi, J E; Ko, S J

    2017-08-01

    As facial pore widening and its treatments have become common concerns in the beauty care field, the necessity for an objective pore-analyzing system has been increased. Conventional apparatuses lack in usability requiring strong light sources and a cumbersome photographing process, and they often yield unsatisfactory analysis results. This study was conducted to develop an image processing technique for automatic facial pore analysis. The proposed method detects facial pores using multi-scale detection and optimal scale selection scheme and then extracts pore-related features such as total area, average size, depth, and the number of pores. Facial photographs of 50 subjects were graded by two expert dermatologists, and correlation analyses between the features and clinical grading were conducted. We also compared our analysis result with those of conventional pore-analyzing devices. The number of large pores and the average pore size were highly correlated with the severity of pore enlargement. In comparison with the conventional devices, the proposed analysis system achieved better performance showing stronger correlation with the clinical grading. The proposed system is highly accurate and reliable for measuring the severity of skin pore enlargement. It can be suitably used for objective assessment of the pore tightening treatments. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  13. Detecting lithography's variations: new types of defects for automatic inspection machines

    Science.gov (United States)

    Gudeczauskas, Paul; Ravid, Erez

    1997-07-01

    Photolithography for silicon semiconductor device manufacturing is a crucial technology in the race to denser and more highly integrated circuits. To achieve an acceptable wafer throughput, most steppers use a combination of global and site-to-site alignment. Focus and exposure are controlled based on a limited number of fields. Post develop evaluation of the pattern quality is typically limited to a few fields on a few wafers. Focus and exposure shifts cause small variations in CDs that rapidly become critical yield limiters. Trends toward larger stepper fields and wafers render very small variations in magnification, distortion, rotation and translation of the patten. Rapid closed loop feedback of a photolithography problem prior to etch is critical for measuring and controlling stepper performance, reducing wafer scrap and yield loss. In this article we will demonstrate how sub-micron variations can be quickly detected with laser scanning tool, combined with pixel-to- pixel image processing. The WF-720 automatic defect inspection tool, utilizing a unique PDI configuration, enables detection of minor changes in pattern shapes based on the global pixel population behavior of the distorted patterns on the wafer.

  14. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  15. Automatic crack detection method for loaded coal in vibration failure process.

    Directory of Open Access Journals (Sweden)

    Chengwu Li

    Full Text Available In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM. A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.

  16. Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

    Science.gov (United States)

    LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

    2017-12-01

    With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

  17. An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs.

    Science.gov (United States)

    Yu, Peichun; Xu, Hao; Zhu, Ying; Yang, Chao; Sun, Xiwen; Zhao, Jun

    2011-06-01

    This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest-based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The results show that the classification performances are high. Compared with the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic.

  18. Long term Suboxone™ emotional reactivity as measured by automatic detection in speech.

    Directory of Open Access Journals (Sweden)

    Edward Hill

    Full Text Available Addictions to illicit drugs are among the nation's most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (Suboxone®; SUBX as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states ("true ground emotionality" in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of "true" emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP and 33 members of Alcoholics Anonymous (AA. Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate "true ground" emotionality in long-term buprenorphine/naloxone combination (Suboxone™. We found in long-term SUBX patients a significantly flat affect (p<0.01, and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction.

  19. Automatic tremor detection with a combined cross-correlation and neural network approach

    Science.gov (United States)

    Horstmann, T.; Harrington, R. M.; Cochran, E. S.

    2011-12-01

    Low-amplitude, long-duration, and ambiguous phase arrivals associated with crustal tremor make automatic detection difficult. We present a new detection method that combines cross-correlation with a neural network clustering algorithm. The approach is independent of any a priori assumptions regarding tremor event duration; instead, it examines frequency content, amplitude, and motion products of continuous data to distinguish tremor from earthquakes and background noise in an automated fashion. Because no assumptions regarding event duration are required, the clustering algorithm is therefore able to detect short, burst-like events which may be missed by many current methods. We detect roughly 130 seismic events occurring over 100 minutes, including earthquakes and tremor, in a three-week long test data set of waveforms recorded near Cholame, California. The detection has a success rate of over 90% when compared to visually selected events. We use continuous broadband data from 13 STS-2 seismometers deployed from May 2010 to July 2011 along the Cholame segment of the San Andreas Fault, as well as stations from the HRSN network. The large volume of waveforms requires first reducing the amount of data before applying the neural network algorithm. First, we filter the data between 2 Hz and 8 Hz, calculate envelopes, and decimate them to 0.2 Hz. We cross-correlate signals at each station with two master stations using a moving 520-second time window with a 5-sec time step. We calculate a mean cross-correlation coefficient value between all station pairs for each time window and each master station, and select the master station with the highest mean value. Time windows with mean coefficients exceeding 0.3 are used in the neural network approach, and windows separated by less than 300 seconds are grouped together. In the second step, we apply the neural network algorithm, i.e., Self Organized Map (SOM), to classify the reduced data set. We first calculate feature

  20. Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2012-01-01

    The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy...

  1. Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO

    Directory of Open Access Journals (Sweden)

    Lersviriyanantakul Chaiwat

    2016-01-01

    Full Text Available For using surface electromyography (sEMG in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner.

  2. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Min-Yin Liu

    2017-05-01

    Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

  3. Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark.

    Science.gov (United States)

    Singha, Suman; Vespe, Michele; Trieschmann, Olaf

    2013-08-15

    Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and 'look-alike' classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Automatic Detection and Reproduction of Natural Head Position in Stereo-Photogrammetry.

    Science.gov (United States)

    Hsung, Tai-Chiu; Lo, John; Li, Tik-Shun; Cheung, Lim-Kwong

    2015-01-01

    The aim of this study was to develop an automatic orientation calibration and reproduction method for recording the natural head position (NHP) in stereo-photogrammetry (SP). A board was used as the physical reference carrier for true verticals and NHP alignment mirror orientation. Orientation axes were detected and saved from the digital mesh model of the board. They were used for correcting the pitch, roll and yaw angles of the subsequent captures of patients' facial surfaces, which were obtained without any markings or sensors attached onto the patient. We tested the proposed method on two commercial active (3dMD) and passive (DI3D) SP devices. The reliability of the pitch, roll and yaw for the board placement were within ±0.039904°, ±0.081623°, and ±0.062320°; where standard deviations were 0.020234°, 0.045645° and 0.027211° respectively. Orientation-calibrated stereo-photogrammetry is the most accurate method (angulation deviation within ±0.1°) reported for complete NHP recording with insignificant clinical error.

  5. Automatic post-picking using MAPPOS improves particle image detection from cryo-EM micrographs.

    Science.gov (United States)

    Norousi, Ramin; Wickles, Stephan; Leidig, Christoph; Becker, Thomas; Schmid, Volker J; Beckmann, Roland; Tresch, Achim

    2013-05-01

    Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully- or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual post-picking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance. MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of non-particle images. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

    International Nuclear Information System (INIS)

    Fukuda, T; Fukunaga, S; Ishida, H; Matsuo, T; Ogawa, S; Shibuya, H; Sudo, J; Kodama, K; Mikado, S

    2013-01-01

    Nuclear emulsion, a tracking detector with sub-micron position resolution, has played a successful role in the field of particle physics and the analysis speed has been substantially improved by the development of automated scanning systems. This paper describes a newly developed automated scanning system and its application to the analysis of nuclear fragments emitted almost isotropically in nuclear evaporation. This system is able to recognize tracks of nuclear fragments up to |tan θ| < 3.0 (where θ is the track angle with respect to the perpendicular to the emulsion film), while existing systems have an angular acceptance limited to |tan θ| < 0.6. The automatic scanning for such a large angle track in nuclear emulsion is the first trial. Furthermore the track recognition algorithm is performed by a powerful Graphics Processing Unit (GPU) for the first time. This GPU has a sufficient computing power to process large area scanning data with a wide angular acceptance and enough flexibility to allow the tuning of the recognition algorithm. This new system will in particular be applied in the framework of the OPERA experiment: the background in the sample of τ decay candidates due to hadronic interactions will be reduced by a better detection of the emitted nuclear fragments.

  7. An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

    Science.gov (United States)

    Hajimani, Elmira; Ruano, M G; Ruano, A E

    2017-07-01

    This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

  8. Automatic detection of the hippocampal region associated with Alzheimer's disease from microscopic images of mice brain

    Science.gov (United States)

    Albaidhani, Tahseen; Hawkes, Cheryl; Jassim, Sabah; Al-Assam, Hisham

    2016-05-01

    The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer's disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.

  9. Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results

    Directory of Open Access Journals (Sweden)

    Alexandru Florin Badea

    2013-01-01

    Full Text Available The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD. It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of “real-time” elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology.

  10. Video-based respiration monitoring with automatic region of interest detection.

    Science.gov (United States)

    Janssen, Rik; Wang, Wenjin; Moço, Andreia; de Haan, Gerard

    2016-01-01

    Vital signs monitoring is ubiquitous in clinical environments and emerging in home-based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video-based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization. We created a benchmark dataset containing 148 video sequences obtained on adults under challenging conditions and also neonates in the neonatal intensive care unit (NICU). The measurements obtained by the proposed video respiration monitoring (VRM) method are not significantly different from the reference methods (guided breathing or contact-based ECG; p-value  =  0.6), and explain more than 99% of the variance of the reference values with low limits of agreement (-2.67 to 2.81 bpm). VRM seems to provide a valid solution to ECG in confined motion scenarios, though precision may be reduced for neonates. More studies are needed to validate VRM under challenging recording conditions, including upper-body motion types.

  11. 3D convolutional neural network for automatic detection of lung nodules in chest CT

    Science.gov (United States)

    Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

    2017-03-01

    Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

  12. AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ

    Directory of Open Access Journals (Sweden)

    Diane Fitria

    2014-08-01

    Full Text Available Abstract An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach accuracy of 94.07%, 92.54%, 88.09% , 86.55% , 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78% respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.

  13. Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

    Science.gov (United States)

    Ebert, Lars C; Heimer, Jakob; Schweitzer, Wolf; Sieberth, Till; Leipner, Anja; Thali, Michael; Ampanozi, Garyfalia

    2017-12-01

    Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT. For deep learning image analysis software, we used the ViDi Suite 2.0. We retrospectively selected 28 cases with, and 24 cases without, hemopericardium. Based on these data, we trained two separate deep learning networks. The first one classified images into hemopericardium/not hemopericardium, and the second one segmented the blood content. We randomly selected 50% of the data for training and 50% for validation. This process was repeated 20 times. The best performing classification network classified all cases of hemopericardium from the validation images correctly with only a few false positives. The best performing segmentation network would tend to underestimate the amount of blood in the pericardium, which is the case for most networks. This is the first study that shows that deep learning has potential for automated image analysis of radiological images in forensic medicine.

  14. A remotely controlled, semi-automatic target system for Rutherford backscattering spectrometry and elastic recoil detection analyses of polymeric membrane samples

    International Nuclear Information System (INIS)

    Attayek, P.J.; Meyer, E.S.; Lin, L.; Rich, G.C.; Clegg, T.B.; Coronell, O.

    2012-01-01

    A new target system for Rutherford backscattering spectrometry and elastic recoil detection analysis is described which enables remotely controlled, semi-automatic analysis of multiple organic polymer samples without exceeding damaging incident beam fluences. Control of fluence at a given beam current is achieved using two stepper motors to move a thin aluminum disk loaded with polymer samples both radially and azimuthally across the beam. Flexible beam spot locations and sample irradiation times are remotely controlled in two steps via two custom LabVIEW™ programs. In the first step, a digital image of the target disk is converted into precise radial and azimuthal coordinates for each mounted polymer sample. In the second step, the motors implement the user-directed sample irradiation and fluence. Schematics of the target system hardware, a block diagram of interactions between the target system components, a description of routine procedures, and illustrative data taken with a 2 MeV 4 He 2+ analysis beam are provided.

  15. Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images

    Science.gov (United States)

    Paz, Abel; Plaza, Antonio; Plaza, Javier

    2009-08-01

    Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.

  16. Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

    Science.gov (United States)

    Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

    2018-02-01

    Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

  17. Improving Security Incidents Detection for Networked Multilevel Intelligent Control Systems in Railway Transport

    Directory of Open Access Journals (Sweden)

    A. V. Chernov

    2016-06-01

    Full Text Available Security monitoring and incident management systems have become the main research focus in the area of intelligent railway control systems. In this work, we discuss a system architecture of multilevel intelligent control system in Russian Railway transport and security incident classification and the handling of theprocess. We make a detailed explanation of problems and tasks of security information and event management system as an important part of a multilevel intelligent control system. We use a rough sets theory to detect an abnormal activity in the considered system. Our main result consists in the development of simple and fast detection techniques that are based on rough sets theory and allow investigating a new type of incidents.

  18. Computerized Diagnostic Assistant for the Automatic Detection of Pneumothorax on Ultrasound: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Shane M. Summers, MD, RDMS

    2016-03-01

    Full Text Available Introduction: Bedside thoracic ultrasound (US can rapidly diagnose pneumothorax (PTX with improved accuracy over the physical examination and without the need for chest radiography (CXR; however, US is highly operator dependent. A computerized diagnostic assistant was developed by the United States Army Institute of Surgical Research to detect PTX on standard thoracic US images. This computer algorithm is designed to automatically detect sonographic signs of PTX by systematically analyzing B-mode US video clips for pleural sliding and M-mode still images for the seashore sign. This was a pilot study to estimate the diagnostic accuracy of the PTX detection computer algorithm when compared to an expert panel of US trained physicians. Methods: This was a retrospective study using archived thoracic US obtained on adult patients presenting to the emergency department (ED between 5/23/2011 and 8/6/2014. Emergency medicine residents, fellows, attending physicians, physician assistants, and medical students performed the US examinations and stored the images in the picture archive and communications system (PACS. The PACS was queried for all ED bedside US examinations with reported positive PTX during the study period along with a random sample of negatives. The computer algorithm then interpreted the images, and we compared the results to an independent, blinded expert panel of three physicians, each with experience reviewing over 10,000 US examinations. Results: Query of the PACS system revealed 146 bedside thoracic US examinations for analysis. Thirteen examinations were indeterminate and were excluded. There were 79 true negatives, 33 true positives, 9 false negatives, and 12 false positives. The test characteristics of the algorithm when compared to the expert panel were sensitivity 79% (95 % CI [63-89] and specificity 87% (95% CI [77-93]. For the 20 images scored as highest quality by the expert panel, the algorithm demonstrated 100% sensitivity

  19. A radial basis classifier for the automatic detection of aspiration in children with dysphagia

    Science.gov (United States)

    Lee, Joon; Blain, Stefanie; Casas, Mike; Kenny, Dave; Berall, Glenn; Chau, Tom

    2006-01-01

    Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion The proposed aspiration

  20. A radial basis classifier for the automatic detection of aspiration in children with dysphagia

    Directory of Open Access Journals (Sweden)

    Blain Stefanie

    2006-07-01

    Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion

  1. Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

    NARCIS (Netherlands)

    Liu, C.; van Netten, Jaap J.; van Baal, Jeff G.; Bus, Sicco A.; van der Heijden, Ferdinand

    2015-01-01

    Early identification of diabetic foot complications and their precursors is essential in preventing their devastating consequences, such as foot infection and amputation. Frequent, automatic risk assessment by an intelligent telemedicine system might be feasible and cost effective. Infrared

  2. Detection and segmentation of virus plaque using HOG and SVM: toward automatic plaque assay.

    Science.gov (United States)

    Mao, Yihao; Liu, Hong; Ye, Rong; Shi, Yonghong; Song, Zhijian

    2014-01-01

    Plaque assaying, measurement of the number, diameter, and area of plaques in a Petri dish image, is a standard procedure gauging the concentration of phage in biology. This paper presented a novel and effective method for implementing automatic plaque assaying. The method was mainly comprised of the following steps: In the training stage, after pre-processing the images for noise suppression, an initial training set was readied by sampling positive (with a plaque at the center) and negative (plaque-free) patches from the training images, and extracting the HOG features from each patch. The linear SVM classifier was trained in a self-learnt supervised learning strategy to avoid possible missing detection. Specifically, the training set which contained positive and negative patches sampled manually from training images was used to train the preliminary classifier which exhaustively searched the training images to predict the label for the unlabeled patches. The mislabeled patches were evaluated by experts and relabeled. And all the newly labeled patches and their corresponding HOG features were added to the initial training set to train the final classifier. In the testing stage, a sliding-window technique was first applied to the unseen image for obtaining HOG features, which were inputted into the classifier to predict whether the patch was positive. Second, a locally adaptive Otsu method was performed on the positive patches to segment the plaques. Finally, after removing the outliers, the parameters of the plaques were measured in the segmented plaques. The experimental results demonstrated that the accuracy of the proposed method was similar to the one measured manually by experts, but it took less than 30 seconds.

  3. Automatic Detection and Recognition of Craters Based on the Spectral Features of Lunar Rocks and Minerals

    Science.gov (United States)

    Ye, L.; Xu, X.; Luan, D.; Jiang, W.; Kang, Z.

    2017-07-01

    Crater-detection approaches can be divided into four categories: manual recognition, shape-profile fitting algorithms, machine-learning methods and geological information-based analysis using terrain and spectral data. The mainstream method is Shape-profile fitting algorithms. Many scholars throughout the world use the illumination gradient information to fit standard circles by least square method. Although this method has achieved good results, it is difficult to identify the craters with poor "visibility", complex structure and composition. Moreover, the accuracy of recognition is difficult to be improved due to the multiple solutions and noise interference. Aiming at the problem, we propose a method for the automatic extraction of impact craters based on spectral characteristics of the moon rocks and minerals: 1) Under the condition of sunlight, the impact craters are extracted from MI by condition matching and the positions as well as diameters of the craters are obtained. 2) Regolith is spilled while lunar is impacted and one of the elements of lunar regolith is iron. Therefore, incorrectly extracted impact craters can be removed by judging whether the crater contains "non iron" element. 3) Craters which are extracted correctly, are divided into two types: simple type and complex type according to their diameters. 4) Get the information of titanium and match the titanium distribution of the complex craters with normal distribution curve, then calculate the goodness of fit and set the threshold. The complex craters can be divided into two types: normal distribution curve type of titanium and non normal distribution curve type of titanium. We validated our proposed method with MI acquired by SELENE. Experimental results demonstrate that the proposed method has good performance in the test area.

  4. Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data

    Science.gov (United States)

    Caruso, T.; Rühl, J.; Sciortino, R.; Marra, F. P.; La Scalia, G.

    2014-10-01

    The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs. It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as "marginal". Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited. Agronomists, landscape planners, policy decision-makers and other professionals have a growing need for accurate and cost-effective information on land use in general and agronomic parameters in the particular. The availability of high spatial resolution imagery has enabled researchers to propose analysis tools on agricultural parcel and tree level. In our study, we test the performance of WorldView-2 imagery relative to the detection of olive groves and the delineation of olive tree crowns, using an object-oriented approach of image classification in combined use with LIDAR data. We selected two sites, which differ in their environmental conditions and in their agronomic parameters of olive grove cultivation. The main advantage of the proposed methodology is the low necessary quantity of data input and its automatibility. However, it should be applied in other study areas to test if the good results of accuracy assessment can be confirmed. Data extracted by the proposed methodology can be used as input data for decision-making support systems for olive grove management.

  5. Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

    Science.gov (United States)

    Buteneers, Pieter; Verstraeten, David; van Mierlo, Pieter; Wyckhuys, Tine; Stroobandt, Dirk; Raedt, Robrecht; Hallez, Hans; Schrauwen, Benjamin

    2011-11-01

    In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG. Copyright © 2011 Elsevier B.V. All rights reserved.

  6. AUTOMATIC DETECTION AND RECOGNITION OF CRATERS BASED ON THE SPECTRAL FEATURES OF LUNAR ROCKS AND MINERALS

    Directory of Open Access Journals (Sweden)

    L. Ye

    2017-07-01

    Full Text Available Crater-detection approaches can be divided into four categories: manual recognition, shape-profile fitting algorithms, machine-learning methods and geological information-based analysis using terrain and spectral data. The mainstream method is Shape-profile fitting algorithms. Many scholars throughout the world use the illumination gradient information to fit standard circles by least square method. Although this method has achieved good results, it is difficult to identify the craters with poor "visibility", complex structure and composition. Moreover, the accuracy of recognition is difficult to be improved due to the multiple solutions and noise interference. Aiming at the problem, we propose a method for the automatic extraction of impact craters based on spectral characteristics of the moon rocks and minerals: 1 Under the condition of sunlight, the impact craters are extracted from MI by condition matching and the positions as well as diameters of the craters are obtained. 2 Regolith is spilled while lunar is impacted and one of the elements of lunar regolith is iron. Therefore, incorrectly extracted impact craters can be removed by judging whether the crater contains "non iron" element. 3 Craters which are extracted correctly, are divided into two types: simple type and complex type according to their diameters. 4 Get the information of titanium and match the titanium distribution of the complex craters with normal distribution curve, then calculate the goodness of fit and set the threshold. The complex craters can be divided into two types: normal distribution curve type of titanium and non normal distribution curve type of titanium. We validated our proposed method with MI acquired by SELENE. Experimental results demonstrate that the proposed method has good performance in the test area.

  7. Conversation analysis at work: detection of conflict in competitive discussions through semi-automatic turn-organization analysis.

    Science.gov (United States)

    Pesarin, Anna; Cristani, Marco; Murino, Vittorio; Vinciarelli, Alessandro

    2012-10-01

    This study proposes a semi-automatic approach aimed at detecting conflict in conversations. The approach is based on statistical techniques capable of identifying turn-organization regularities associated with conflict. The only manual step of the process is the segmentation of the conversations into turns (time intervals during which only one person talks) and overlapping speech segments (time intervals during which several persons talk at the same time). The rest of the process takes place automatically and the results show that conflictual exchanges can be detected with Precision and Recall around 70% (the experiments have been performed over 6 h of political debates). The approach brings two main benefits: the first is the possibility of analyzing potentially large amounts of conversational data with a limited effort, the second is that the model parameters provide indications on what turn-regularities are most likely to account for the presence of conflict.

  8. Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

    Science.gov (United States)

    Chen, Po-Hsiung; Shimada, Rintaro; Yabumoto, Sohshi; Okajima, Hajime; Ando, Masahiro; Chang, Chiou-Tzu; Lee, Li-Tzu; Wong, Yong-Kie; Chiou, Arthur; Hamaguchi, Hiro-O.

    2016-01-01

    We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin.

  9. PCR-Based Method for the Detection of Toxic Mushrooms Causing Food-Poisoning Incidents.

    Science.gov (United States)

    Nomura, Chie; Masayama, Atsushi; Yamaguchi, Mizuka; Sakuma, Daisuke; Kajimura, Keiji

    2017-01-01

    In this study, species-specific identification of five toxic mushrooms, Chlorophyllum molybdites, Gymnopilus junonius, Hypholoma fasciculare, Pleurocybella porrigens, and Tricholoma ustale, which have been involved in food-poisoning incidents in Japan, was investigated. Specific primer pairs targeting internal transcribed spacer (ITS) regions were designed for PCR detection. The specific amplicons were obtained from fresh, cooked, and simulated gastric fluid (SGF)-treated samples. No amplicons were detected from other mushrooms with similar morphology. Our method using one-step extraction of mushrooms allows rapid detection within 2.5 hr. It could be utilized for rapid identification or screening of toxic mushrooms.

  10. Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform.

    Science.gov (United States)

    Meng, Yingchao; Zhang, Zhongping; Yin, Huaqiang; Ma, Tao

    2018-03-01

    To obtain size distribution of nanoparticles, scanning electron microscope (SEM) and transmission electron microscopy (TEM) have been widely adopted, but manual measurement of statistical size distributions from the SEM or TEM images is time-consuming and labor-intensive. Therefore, automatic detection methods are desirable. This paper proposes an automatic image processing algorithm which is mainly based on local adaptive Canny edge detection and modified circular Hough transform. The proposed algorithm can utilize the local thresholds to detect particles from the images with different degrees of complexity. Compared with the results produced by applying global thresholds, our algorithm performs much better. The robustness and reliability of this method have been verified by comparing its results with manual measurement, and an excellent agreement has been found. The proposed method can accurately recognize the particles with high efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Robustness and precision of an automatic marker detection algorithm for online prostate daily targeting using a standard V-EPID.

    Science.gov (United States)

    Aubin, S; Beaulieu, L; Pouliot, S; Pouliot, J; Roy, R; Girouard, L M; Martel-Brisson, N; Vigneault, E; Laverdière, J

    2003-07-01

    An algorithm for the daily localization of the prostate using implanted markers and a standard video-based electronic portal imaging device (V-EPID) has been tested. Prior to planning, three gold markers were implanted in the prostate of seven patients. The clinical images were acquired with a BeamViewPlus 2.1 V-EPID for each field during the normal course radiotherapy treatment and are used off-line to determine the ability of the automatic marker detection algorithm to adequately and consistently detect the markers. Clinical images were obtained with various dose levels from ranging 2.5 to 75 MU. The algorithm is based on marker attenuation characterization in the portal image and spatial distribution. A total of 1182 clinical images were taken. The results show an average efficiency of 93% for the markers detected individually and 85% for the group of markers. This algorithm accomplishes the detection and validation in 0.20-0.40 s. When the center of mass of the group of implanted markers is used, then all displacements can be corrected to within 1.0 mm in 84% of the cases and within 1.5 mm in 97% of cases. The standard video-based EPID tested provides excellent marker detection capability even with low dose levels. The V-EPID can be used successfully with radiopaque markers and the automatic detection algorithm to track and correct the daily setup deviations due to organ motions.

  12. Automatic detection of ''bore slug'' in tubes; Detection automatique des manques de metal internes sur tubes

    Energy Technology Data Exchange (ETDEWEB)

    Bisiaux, B.; Deutsch, S.; Tailleux, O.; Mette, F. [CEV Vallourec, Aulnoye (France)

    2001-07-01

    During the tube manufacturing for the petroleum industry, the lacks of internal metal (called Bore Slug) can be created during the hot rolling. These large defects are not good detected by the classic UT and by the wall thickness measurement. That's why VALLOUREC developed an automatic UT device which works by transmission. Nevertheless, this system is too little selective and can cause no doubtful pipes (tubes good detected bad). We adapted a Bore Slug control system on the VMOG UK RP20 at the end of August. The results are rather good and showed a good detection of the Bore Slug and very little no doubtful pipes. (authors)

  13. A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

    Science.gov (United States)

    Panicker, Rani Oomman; Soman, Biju; Saini, Gagan; Rajan, Jeny

    2016-01-01

    Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

  14. Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis.

    Science.gov (United States)

    Gong, Jing; Liu, Ji-Yu; Wang, Li-Jia; Sun, Xi-Wen; Zheng, Bin; Nie, Sheng-Dong

    2018-02-01

    Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  15. Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

    Science.gov (United States)

    Mencarelli, A.; van Beek, S.; Zijp, L. J.; Rasch, C.; van Herk, M.; Sonke, J.-J.

    2014-04-01

    Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland-Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (-1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically implemented in

  16. Automatic detection of spiculation of pulmonary nodules in computed tomography images

    DEFF Research Database (Denmark)

    Ciompi, F; Jacobs, C; Scholten, E.T.

    2015-01-01

    We present a fully automatic method for the assessment of spiculation of pulmonary nodules in low-dose Computed Tomography (CT) images. Spiculation is considered as one of the indicators of nodule malignancy and an important feature to assess in order to decide on a patient-tailored follow......-up procedure. For this reason, lung cancer screening scenario would benefit from the presence of a fully automatic system for the assessment of spiculation. The presented framework relies on the fact that spiculated nodules mainly differ from non-spiculated ones in their morphology. In order to discriminate...... to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation. © (2015...

  17. Accuracy of coronary plaque detection and assessment of interobserver agreement for plaque quantification using automatic coronary plaque analysis software on coronary CT angiography

    Energy Technology Data Exchange (ETDEWEB)

    Laqmani, A.; Quitzke, M.; Creder, D.D.; Adam, G.; Lund, G. [University Medical Center Hamburg-Eppendorf, Hamburg (Germany). Dept. of Diagnostic and Interventional Radiology and Nuclearmedicine; Klink, T. [Wuerzburg Univ. (Germany). Inst. of Diagnostic and Interventional Radiology

    2016-10-15

    To evaluate the accuracy of automatic plaque detection and the interobserver agreement of automatic versus manually adjusted quantification of coronary plaques on coronary CT angiography (cCTA) using commercially available software. 10 cCTA datasets were evaluated using plaque software. First, the automatically detected plaques were verified. Second, two observers independently performed plaque quantification without revising the automatically constructed plaque contours (automatic approach). Then, each observer adjusted the plaque contours according to plaque delineation (adjusted approach). The interobserver agreement of both approaches was analyzed. 32 of 114 automatically identified findings were true-positive plaques, while 82 (72 %) were false-positive. 20 of 52 plaques (38 %) were missed by the software (false-negative). The automatic approach provided good interobserver agreement with relative differences of 0.9 ± 16.0 % for plaque area and -3.3 ± 33.8 % for plaque volume. Both observers independently adjusted all contours because they did not represent the plaque delineation. Interobserver agreement decreased for the adjusted approach with relative differences of 25.0 ± 24.8 % for plaque area and 20.0 ± 40.4 % for plaque volume. The automatic plaque analysis software is of limited value due to high numbers of false-positive and false-negative plaque findings. The automatic approach was reproducible but it necessitated adjustment of all constructed plaque contours resulting in deterioration of the interobserver agreement.

  18. Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

    Science.gov (United States)

    Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

    2016-01-01

    This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

  19. Automatic landmark detection and face recognition for side-view face images

    NARCIS (Netherlands)

    Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Broemme, Arslan; Busch, Christoph

    2013-01-01

    In real-life scenarios where pose variation is up to side-view positions, face recognition becomes a challenging task. In this paper we propose an automatic side-view face recognition system designed for home-safety applications. Our goal is to recognize people as they pass through doors in order to

  20. An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters.

    Science.gov (United States)

    Behrens, F; Mackeben, M; Schröder-Preikschat, W

    2010-08-01

    This analysis of time series of eye movements is a saccade-detection algorithm that is based on an earlier algorithm. It achieves substantial improvements by using an adaptive-threshold model instead of fixed thresholds and using the eye-movement acceleration signal. This has four advantages: (1) Adaptive thresholds are calculated automatically from the preceding acceleration data for detecting the beginning of a saccade, and thresholds are modified during the saccade. (2) The monotonicity of the position signal during the saccade, together with the acceleration with respect to the thresholds, is used to reliably determine the end of the saccade. (3) This allows differentiation between saccades following the main-sequence and non-main-sequence saccades. (4) Artifacts of various kinds can be detected and eliminated. The algorithm is demonstrated by applying it to human eye movement data (obtained by EOG) recorded during driving a car. A second demonstration of the algorithm detects microsleep episodes in eye movement data.

  1. A new algorithm for automatic Outlier Detection in GPS Time Series

    Science.gov (United States)

    Cannavo', Flavio; Mattia, Mario; Rossi, Massimo; Palano, Mimmo; Bruno, Valentina

    2010-05-01

    Nowadays continuous GPS time series are considered a crucial product of GPS permanent networks, useful in many geo-science fields, such as active tectonics, seismology, crustal deformation and volcano monitoring (Altamimi et al. 2002, Elósegui et al. 2006, Aloisi et al. 2009). Although the GPS data elaboration software has increased in reliability, the time series are still affected by different kind of noise, from the intrinsic noise (e.g. thropospheric delay) to the un-modeled noise (e.g. cycle slips, satellite faults, parameters changing). Typically GPS Time Series present characteristic noise that is a linear combination of white noise and correlated colored noise, and this characteristic is fractal in the sense that is evident for every considered time scale or sampling rate. The un-modeled noise sources result in spikes, outliers and steps. These kind of errors can appreciably influence the estimation of velocities of the monitored sites. The outlier detection in generic time series is a widely treated problem in literature (Wei, 2005), while is not fully developed for the specific kind of GPS series. We propose a robust automatic procedure for cleaning the GPS time series from the outliers and, especially for long daily series, steps due to strong seismic or volcanic events or merely instrumentation changing such as antenna and receiver upgrades. The procedure is basically divided in two steps: a first step for the colored noise reduction and a second step for outlier detection through adaptive series segmentation. Both algorithms present novel ideas and are nearly unsupervised. In particular, we propose an algorithm to estimate an autoregressive model for colored noise in GPS time series in order to subtract the effect of non Gaussian noise on the series. This step is useful for the subsequent step (i.e. adaptive segmentation) which requires the hypothesis of Gaussian noise. The proposed algorithms are tested in a benchmark case study and the results

  2. DESIGN A FILTER TO DETECT AND REMOVE VEGETATION FROM ULTRA-CAM-X AERIAL IMAGES’ POINT CLOUD TO PRODUCE AUTOMATICALLY DIGITAL ELEVATION MODEL

    Directory of Open Access Journals (Sweden)

    H. Enayati

    2015-12-01

    segmented image is added to raster of elevation and vegetation elevation is detected. Results is showing that point clouds’ texture is a good data for filtering vegetation and generating DEM automatically.

  3. Automatic Detection of Mitosis and Nuclei From Cytogenetic Images by CellProfiler Software for Mitotic Index Estimation.

    Science.gov (United States)

    González, Jorge Ernesto; Radl, Analía; Romero, Ivonne; Barquinero, Joan Francesc; García, Omar; Di Giorgio, Marina

    2016-12-01

    Mitotic Index (MI) estimation expressed as percentage of mitosis plays an important role as quality control endpoint. To this end, MI is applied to check the lot of media and reagents to be used throughout the assay and also to check cellular viability after blood sample shipping, indicating satisfactory/unsatisfactory conditions for the progression of cell culture. The objective of this paper was to apply the CellProfiler open-source software for automatic detection of mitotic and nuclei figures from digitized images of cultured human lymphocytes for MI assessment, and to compare its performance to that performed through semi-automatic and visual detection. Lymphocytes were irradiated and cultured for mitosis detection. Sets of images from cultures were analyzed visually and findings were compared with those using CellProfiler software. The CellProfiler pipeline includes the detection of nuclei and mitosis with 80% sensitivity and more than 99% specificity. We conclude that CellProfiler is a reliable tool for counting mitosis and nuclei from cytogenetic images, saves considerable time compared to manual operation and reduces the variability derived from the scoring criteria of different scorers. The CellProfiler automated pipeline achieves good agreement with visual counting workflow, i.e. it allows fully automated mitotic and nuclei scoring in cytogenetic images yielding reliable information with minimal user intervention. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  4. Automatic Detection of Mitosis and Nuclei from Cytogenetic Images by CellProfiler Software for Mitotic Index Estimation

    International Nuclear Information System (INIS)

    Gonzalez, Jorge Ernesto; Romero, Ivonne; Garcia, Omar; Radl, Analia; Di Giorgio, Marina; Barquinero, Joan Francesc

    2016-01-01

    Mitotic Index (MI) estimation expressed as percentage of mitosis plays an important role as quality control endpoint. To this end, MI is applied to check the lot of media and reagents to be used throughout the assay and also to check cellular viability after blood sample shipping, indicating satisfactory/unsatisfactory conditions for the progression of cell culture. The objective of this paper was to apply the CellProfiler open-source software for automatic detection of mitotic and nuclei figures from digitized images of cultured human lymphocytes for MI assessment, and to compare its performance to that performed through semi-automatic and visual detection. Lymphocytes were irradiated and cultured for mitosis detection. Sets of images from cultures were analyzed visually and findings were compared with those using CellProfiler software. The CellProfiler pipeline includes the detection of nuclei and mitosis with 80% sensitivity and more than 99% specificity. We conclude that CellProfiler is a reliable tool for counting mitosis and nuclei from cytogenetic images, saves considerable time compared to manual operation and reduces the variability derived from the scoring criteria of different scorers. The CellProfiler automated pipeline achieves good agreement with visual counting workflow, i.e. it allows fully automated mitotic and nuclei scoring in cytogenetic images yielding reliable information with minimal user intervention. (authors)

  5. Safer healthcare at home: Detecting, correcting and learning from incidents involving infusion devices.

    Science.gov (United States)

    Lyons, Imogen; Blandford, Ann

    2018-02-01

    Complex medical devices such as infusion pumps are increasingly being used in patients' homes with little known about the impact on patient safety. Our aim was to better understand the risks to patient safety in this situation and how these risks might be minimised, by reference to incident reports. We identified 606 records of incidents associated with infusion devices that had occurred in a private home and were reported to the UK National Reporting and Learning Service (2005-2015 inclusive). We used thematic analysis to identify key themes. In this paper we focus on two emergent themes: detecting and diagnosing incidents; and locating the patient, lay caregivers and their family in incident reports. The majority of incidents were attributed to device malfunction, and resulted in the patient being under-dosed. Delays in recognising and responding to problems were identified, alongside challenges in identifying the cause. We propose a process model for fault diagnosis and correction. Patients and caregivers did not feature strongly in reports; we highlight how the device is in the home but of the care system, and propose an agent model to describe this; we also identify ways of mitigating this disjoint. Devices need to be appropriately tailored to the setting in which they are employed, and within a system of care that ensures they are used optimally and safely. Suggested features to improve patient safety include devices that can provide better feedback to identify problems and support resolution, alongside greater monitoring and technical support by care providers for both patients and frontline professionals. The proposed process and agent models provide a structure for reviewing safety and learning from incidents in home health care. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. A Portable Automatic Endpoint Detection System for Amplicons of Loop Mediated Isothermal Amplification on Microfluidic Compact Disk Platform

    Directory of Open Access Journals (Sweden)

    Shah Mukim Uddin

    2015-03-01

    Full Text Available In recent years, many improvements have been made in foodborne pathogen detection methods to reduce the impact of food contamination. Several rapid methods have been developed with biosensor devices to improve the way of performing pathogen detection. This paper presents an automated endpoint detection system for amplicons generated by loop mediated isothermal amplification (LAMP on a microfluidic compact disk platform. The developed detection system utilizes a monochromatic ultraviolet (UV emitter for excitation of fluorescent labeled LAMP amplicons and a color sensor to detect the emitted florescence from target. Then it processes the sensor output and displays the detection results on liquid crystal display (LCD. The sensitivity test has been performed with detection limit up to 2.5 × 10−3 ng/µL with different DNA concentrations of Salmonella bacteria. This system allows a rapid and automatic endpoint detection which could lead to the development of a point-of-care diagnosis device for foodborne pathogens detection in a resource-limited environment.

  7. Control method and device for automatic drift stabilization in radiation detection

    International Nuclear Information System (INIS)

    Berthold, F.; Kubisiak, H.

    1979-01-01

    In the automatic control circuit individual electron peaks in the detectors, e.g. NaI crystals or proportional counters, are used. These peaks exhibit no drift dependence; they may be produced in the detectors in different ways. The control circuit may be applied in nuclear radiation measurement techniques, photometry, gamma cameras and for measuring the X-ray fine structure with proportional counters. (DG) [de

  8. Automatic detection of spiculation of pulmonary nodules in computed tomography images

    Science.gov (United States)

    Ciompi, F.; Jacobs, C.; Scholten, E. T.; van Riel, S. J.; W. Wille, M. M.; Prokop, M.; van Ginneken, B.

    2015-03-01

    We present a fully automatic method for the assessment of spiculation of pulmonary nodules in low-dose Computed Tomography (CT) images. Spiculation is considered as one of the indicators of nodule malignancy and an important feature to assess in order to decide on a patient-tailored follow-up procedure. For this reason, lung cancer screening scenario would benefit from the presence of a fully automatic system for the assessment of spiculation. The presented framework relies on the fact that spiculated nodules mainly differ from non-spiculated ones in their morphology. In order to discriminate the two categories, information on morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a spectrum. A library of spectra is created by clustering data via unsupervised learning. The centroids of the clusters are used to label back each spectrum in the sampling pattern. A compact descriptor encoding the nodule morphology is obtained as the histogram of labels along all the spherical surfaces and used to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation.

  9. Colour transformations and K-means segmentation for automatic cloud detection

    Directory of Open Access Journals (Sweden)

    Martin Blazek

    2015-08-01

    Full Text Available The main aim of this work is to find simple criteria for automatic recognition of several meteorological phenomena using optical digital sensors (e.g., Wide-Field cameras, automatic DSLR cameras or robotic telescopes. The output of those sensors is commonly represented in RGB channels containing information about both colour and luminosity even when normalised. Transformation into other colour spaces (e.g., CIE 1931 xyz, CIE L*a*b*, YCbCr can separate colour from luminosity, which is especially useful in the image processing of automatic cloud boundary recognition. Different colour transformations provide different sectorization of cloudy images. Hence, the analysed meteorological phenomena (cloud types, clear sky project differently into the colour diagrams of each international colour systems. In such diagrams, statistical tools can be applied in search of criteria which could determine clear sky from a covered one and possibly even perform a meteorological classification of cloud types. For the purpose of this work, a database of sky images (both clear and cloudy, with emphasis on a variety of different observation conditions (e.g., time, altitude, solar angle, etc. was acquired. The effectiveness of several colour transformations for meteorological application is discussed and the representation of different clouds (or clear sky in those colour systems is analysed. Utilisation of this algorithm would be useful in all-sky surveys, supplementary meteorological observations, solar cell effectiveness predictions or daytime astronomical solar observations.

  10. Development and evaluation of an automatic acne lesion detection program using digital image processing.

    Science.gov (United States)

    Min, Seonguk; Kong, Hyoun-joong; Yoon, Chiyul; Kim, Hee Chan; Suh, Dae Hun

    2013-02-01

    Existing acne grading methods, which depend on overall impression, require a long training period and there is a high degree of variability among raters, including trained dermatologists. The use of lesion count provides fair reproducibility but the method is time consuming. New technologies in photographic equipment and software allow solutions to the problem of acne evaluation. This study was conducted to develop the automatic acne lesion program and evaluation of its usefulness. We made the conditions to optimize characterization of acne lesions and developed the counting program. Twenty-five volunteers with acne lesions were enrolled. Automated lesion counting for five subtypes of acne (papule, nodule, pustule, whitehead comedone, and blackhead comedone) was performed with image processing. The usefulness of the automatic lesion count program was assessed by a comparison with manual counting performed by an expert dermatologist. In a comparison with manual counting performed by an expert dermatologist, the sensitivity and positive predictive value of the lesion-counting program was greater than 70% for papules, nodules, pustules, and whitehead comedo. In a comparison with manual counting, findings with the use of the lesion-counting program were well correlated for papules, nodules, pustules, and whitehead comedo (r > 0.9). Automatic lesion-counting program can be a useful tool for acne severity evaluation. © 2012 John Wiley & Sons A/S.

  11. Fully automatic detection and visualization of patient specific coronary supply regions

    Science.gov (United States)

    Fritz, Dominik; Wiedemann, Alexander; Dillmann, Ruediger; Scheuering, Michael

    2008-03-01

    Coronary territory maps, which associate myocardial regions with the corresponding coronary artery that supply them, are a common visualization technique to assist the physician in the diagnosis of coronary artery disease. However, the commonly used visualization is based on the AHA-17-segment model, which is an empirical population based model. Therefore, it does not necessarily cope with the often highly individual coronary anatomy of a specific patient. In this paper we introduce a novel fully automatic approach to compute the patient individual coronary supply regions in CTA datasets. This approach is divided in three consecutive steps. First, the aorta is fully automatically located in the dataset with a combination of a Hough transform and a cylindrical model matching approach. Having the location of the aorta, a segmentation and skeletonization of the coronary tree is triggered. In the next step, the three main branches (LAD, LCX and RCX) are automatically labeled, based on the knowledge of the pose of the aorta and the left ventricle. In the last step the labeled coronary tree is projected on the left ventricular surface, which can afterward be subdivided into the coronary supply regions, based on a Voronoi transform. The resulting supply regions can be either shown in 3D on the epicardiac surface of the left ventricle, or as a subdivision of a polarmap.

  12. Automatic Ki-67 counting using robust cell detection and online dictionary learning.

    Science.gov (United States)

    Xing, Fuyong; Su, Hai; Neltner, Janna; Yang, Lin

    2014-03-01

    Ki-67 proliferation index is a valid and important biomarker to gauge neuroendocrine tumor (NET) cell progression within the gastrointestinal tract and pancreas. Automatic Ki-67 assessment is very challenging due to complex variations of cell characteristics. In this paper, we propose an integrated learning-based framework for accurate automatic Ki-67 counting for NET. The main contributions of our method are: 1) A robust cell counting and boundary delineation algorithm that is designed to localize both tumor and nontumor cells. 2) A novel online sparse dictionary learning method to select a set of representative training samples. 3) An automated framework that is used to differentiate tumor from nontumor cells (such as lymphocytes) and immunopositive from immunonegative tumor cells for the assessment of Ki-67 proliferation index. The proposed method has been extensively tested using 46 NET cases. The performance is compared with pathologists' manual annotations. The automatic Ki-67 counting is quite accurate compared with pathologists' manual annotations. This is much more accurate than existing methods.

  13. Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach.

    Science.gov (United States)

    Lopez-de-Ipina, Karmele; Martinez-de-Lizarduy, Unai; Calvo, Pilar M; Mekyska, Jiri; Beitia, Blanca; Barroso, Nora; Estanga, Ainara; Tainta, Milkel; Ecay-Torres, Mirian

    2018-01-01

    Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  14. Novel automatic detection of pleura and B-lines (comet-tail artifacts) on in vivo lung ultrasound scans

    Science.gov (United States)

    Moshavegh, Ramin; Hansen, Kristoffer Lindskov; Møller Sørensen, Hasse; Hemmsen, Martin Christian; Ewertsen, Caroline; Nielsen, Michael Bachmann; Jensen, Jørgen Arendt

    2016-04-01

    This paper presents a novel automatic method for detection of B-lines (comet-tail artifacts) in lung ultrasound scans. B-lines are the most commonly used artifacts for analyzing the pulmonary edema. They appear as laser-like vertical beams, which arise from the pleural line and spread down without fading to the edge of the screen. An increase in their number is associated with presence of edema. All the scans used in this study were acquired using a BK3000 ultrasound scanner (BK Ultrasound, Denmark) driving a 192-element 5:5 MHz wide linear transducer (10L2W, BK Ultrasound). The dynamic received focus technique was employed to generate the sequences. Six subjects, among those three patients after major surgery and three normal subjects, were scanned once and Six ultrasound sequences each containing 50 frames were acquired. The proposed algorithm was applied to all 300 in-vivo lung ultrasound images. The pleural line is first segmented on each image and then the B-line artifacts spreading down from the pleural line are detected and overlayed on the image. The resulting 300 images showed that the mean lateral distance between B-lines detected on images acquired from patients decreased by 20% in compare with that of normal subjects. Therefore, the method can be used as the basis of a method of automatically and qualitatively characterizing the distribution of B-lines.

  15. Automatic Event Detection in Search for Inter-Moss Loops in IRIS Si IV Slit-Jaw Images

    Science.gov (United States)

    Fayock, Brian; Winebarger, Amy R.; De Pontieu, Bart

    2015-01-01

    The high-resolution capabilities of the Interface Region Imaging Spectrometer (IRIS) mission have allowed the exploration of the finer details of the solar magnetic structure from the chromosphere to the lower corona that have previously been unresolved. Of particular interest to us are the relatively short-lived, low-lying magnetic loops that have foot points in neighboring moss regions. These inter-moss loops have also appeared in several AIA pass bands, which are generally associated with temperatures that are at least an order of magnitude higher than that of the Si IV emission seen in the 1400 angstrom pass band of IRIS. While the emission lines seen in these pass bands can be associated with a range of temperatures, the simultaneous appearance of these loops in IRIS 1400 and AIA 171, 193, and 211 suggest that they are not in ionization equilibrium. To study these structures in detail, we have developed a series of algorithms to automatically detect signal brightening or events on a pixel-by-pixel basis and group them together as structures for each of the above data sets. These algorithms have successfully picked out all activity fitting certain adjustable criteria. The resulting groups of events are then statistically analyzed to determine which characteristics can be used to distinguish the inter-moss loops from all other structures. While a few characteristic histograms reveal that manually selected inter-moss loops lie outside the norm, a combination of several characteristics will need to be used to determine the statistical likelihood that a group of events be categorized automatically as a loop of interest. The goal of this project is to be able to automatically pick out inter-moss loops from an entire data set and calculate the characteristics that have previously been determined manually, such as length, intensity, and lifetime. We will discuss the algorithms, preliminary results, and current progress of automatic characterization.

  16. The incidence and risk factors of asymptomatic primary spontaneous pneumothorax detected during health check-ups.

    Science.gov (United States)

    Mitani, Akihisa; Hakamata, Yukichika; Hosoi, Megumi; Horie, Masafumi; Murano, Yoko; Saito, Akira; Yanagimoto, Shintaro; Tsuji, Shoji; Yamamoto, Kazuhiko; Nagase, Takahide

    2017-12-07

    Patients with primary spontaneous pneumothorax (PSP) usually complain of sudden-onset dyspnea and pleuritic chest pain. However, asymptomatic PSP has been incidentally detected on chest X-rays. In this study, we analyzed the incidence, characteristics, risk factors, and prognosis of asymptomatic PSP detected during regular medical check-ups in university students. In this study, 101,709 chest X-rays were performed during medical check-ups for students at the University of Tokyo between April 2011 and March 2016. Among them, 43 cases of asymptomatic PSP (0.042%) were detected. We calculated the lung collapse rate of pneumothorax using Kircher's method. We also analyzed risk factors associated with asymptomatic PSP using characteristics inspected in medical check-ups. The incidence of asymptomatic PSP was significantly higher in men than in women (0.050% vs 0.018%). Multivariate analysis revealed an association of younger age, greater height, lower body mass index, and greater height growth per year with an increased risk of asymptomatic PSP in male students. Mild lung collapse (up is very important because a considerable number of patients with mild lung collapse eventually require an invasive medical procedure.

  17. Automated surveillance of 911 call data for detection of possible water contamination incidents

    Directory of Open Access Journals (Sweden)

    Dangel Chrissy

    2011-03-01

    Full Text Available Abstract Background Drinking water contamination, with the capability to affect large populations, poses a significant risk to public health. In recent water contamination events, the impact of contamination on public health appeared in data streams monitoring health-seeking behavior. While public health surveillance has traditionally focused on the detection of pathogens, developing methods for detection of illness from fast-acting chemicals has not been an emphasis. Methods An automated surveillance system was implemented for Cincinnati's drinking water contamination warning system to monitor health-related 911 calls in the city of Cincinnati. Incident codes indicative of possible water contamination were filtered from all 911 calls for analysis. The 911 surveillance system uses a space-time scan statistic to detect potential water contamination incidents. The frequency and characteristics of the 911 alarms over a 2.5 year period were studied. Results During the evaluation, 85 alarms occurred, although most occurred prior to the implementation of an additional alerting constraint in May 2009. Data were available for analysis approximately 48 minutes after calls indicating alarms may be generated 1-2 hours after a rapid increase in call volume. Most alerts occurred in areas of high population density. The average alarm area was 9.22 square kilometers. The average number of cases in an alarm was nine calls. Conclusions The 911 surveillance system provides timely notification of possible public health events, but did have limitations. While the alarms contained incident codes and location of the caller, additional information such as medical status was not available to assist validating the cause of the alarm. Furthermore, users indicated that a better understanding of 911 system functionality is necessary to understand how it would behave in an actual water contamination event.

  18. Automatic change detection: does the auditory system use representations of individual stimulus features or gestalts?

    Science.gov (United States)

    Deacon, D; Nousak, J M; Pilotti, M; Ritter, W; Yang, C M

    1998-07-01

    The effects of global and feature-specific probabilities of auditory stimuli were manipulated to determine their effects on the mismatch negativity (MMN) of the human event-related potential. The question of interest was whether the automatic comparison of stimuli indexed by the MMN was performed on representations of individual stimulus features or on gestalt representations of their combined attributes. The design of the study was such that both feature and gestalt representations could have been available to the comparator mechanism generating the MMN. The data were consistent with the interpretation that the MMN was generated following an analysis of stimulus features.

  19. Automatic detection of slow-wave sleep and REM-sleep stages using polysomnographic ECG signals

    International Nuclear Information System (INIS)

    Khemiri, S.; Aloui, K.; Naceur, M. S.

    2011-01-01

    We describe in this paper a new approach of classifying the different sleep stages only by focusing on the polysomnographic ECG signals. We show the pre-processing technique of the ECG signals. At the same time the identifcation and elimination of the different types of artifacts which contain the signal and its reconstruction are shown. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM-sleep consists in extracting physiological indicators that characterize these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a night's sleep.

  20. DETECTING DIGITAL IMAGE FORGERIES USING RE-SAMPLING BY AUTOMATIC REGION OF INTEREST (ROI

    Directory of Open Access Journals (Sweden)

    P. Subathra

    2012-05-01

    Full Text Available Nowadays, digital images can be easily altered by using high-performance computers, sophisticated photo-editing, computer graphics software, etc. It will affect the authenticity of images in law, politics, the media, and business. In this paper, we proposed a Resampling technique using automatic selection of Region of Interest (ROI method for finding the authenticity of digitally altered image. The proposed technique provides better results beneath scaling, rotation, skewing transformations, and any of their arbitrary combinations in image. It surmounts the protracted complexity in manual ROI selection.

  1. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    International Nuclear Information System (INIS)

    Althuwaynee, Omar F; Pradhan, Biswajeet; Ahmad, Noordin

    2014-01-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

  2. Precise 3D Lug Pose Detection Sensor for Automatic Robot Welding Using a Structured-Light Vision System.

    Science.gov (United States)

    Park, Jae Byung; Lee, Seung Hun; Lee, Il Jae

    2009-01-01

    In this study, we propose a precise 3D lug pose detection sensor for automatic robot welding of a lug to a huge steel plate used in shipbuilding, where the lug is a handle to carry the huge steel plate. The proposed sensor consists of a camera and four laser line diodes, and its design parameters are determined by analyzing its detectable range and resolution. For the lug pose acquisition, four laser lines are projected on both lug and plate, and the projected lines are detected by the camera. For robust detection of the projected lines against the illumination change, the vertical threshold, thinning, Hough transform and separated Hough transform algorithms are successively applied to the camera image. The lug pose acquisition is carried out by two stages: the top view alignment and the side view alignment. The top view alignment is to detect the coarse lug pose relatively far from the lug, and the side view alignment is to detect the fine lug pose close to the lug. After the top view alignment, the robot is controlled to move close to the side of the lug for the side view alignment. By this way, the precise 3D lug pose can be obtained. Finally, experiments with the sensor prototype are carried out to verify the feasibility and effectiveness of the proposed sensor.

  3. Precise 3D Lug Pose Detection Sensor for Automatic Robot Welding Using a Structured-Light Vision System

    Directory of Open Access Journals (Sweden)

    Il Jae Lee

    2009-09-01

    Full Text Available In this study, we propose a precise 3D lug pose detection sensor for automatic robot welding of a lug to a huge steel plate used in shipbuilding, where the lug is a handle to carry the huge steel plate. The proposed sensor consists of a camera and four laser line diodes, and its design parameters are determined by analyzing its detectable range and resolution. For the lug pose acquisition, four laser lines are projected on both lug and plate, and the projected lines are detected by the camera. For robust detection of the projected lines against the illumination change, the vertical threshold, thinning, Hough transform and separated Hough transform algorithms are successively applied to the camera image. The lug pose acquisition is carried out by two stages: the top view alignment and the side view alignment. The top view alignment is to detect the coarse lug pose relatively far from the lug, and the side view alignment is to detect the fine lug pose close to the lug. After the top view alignment, the robot is controlled to move close to the side of the lug for the side view alignment. By this way, the precise 3D lug pose can be obtained. Finally, experiments with the sensor prototype are carried out to verify the feasibility and effectiveness of the proposed sensor.

  4. MRI detection of unsuspected vertebral injury in acute spinal trauma: incidence and significance

    International Nuclear Information System (INIS)

    Qaiyum, M.; Tyrrell, P.N.M.; McCall, I.W.; Cassar-Pullicino, V.N.

    2001-01-01

    Objective. Multilevel spinal injury is well recognised. Previous studies reviewing the radiographs of spinal injury patients have shown an incidence of 15.2% of unsuspected spinal injury. It is recognised that magnetic resonance imaging (MRI) can identify injuries that are not demonstrated on radiographs. The objective of this study was to determine the incidence and significance of spinal injuries using MRI in comparison with radiographs.Design and patients. The radiographs and MR images of 110 acute spinal injury patients were reviewed independently of each other and the findings were then correlated to determine any unsuspected injury.Results. MRI detected vertebral body bone bruises (microtrabecular bone injury) in 41.8% of spinal injury patients which were not seen on radiographs. These bone bruises were best appreciated on sagittal short tau inversion recovery MR sequences and seen at contiguous and non-contiguous levels in relation to the primary injury.Conclusion. This level of incidence of bone bruises has not previously been appreciated. We recommend that patients undergoing MRI for an injured segment of the spine are better assessed by MRI of the entire spine at the same time to exclude further injury. (orig.)

  5. Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

    Science.gov (United States)

    Shiffman, Smadar

    2004-01-01

    Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

  6. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

    Full Text Available Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS or terrestrial laser scanner (TLS point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

  7. Behavioral and electrophysiological evidence for early and automatic detection of phonological equivalence in variable speech inputs.

    Science.gov (United States)

    Kharlamov, Viktor; Campbell, Kenneth; Kazanina, Nina

    2011-11-01

    Speech sounds are not always perceived in accordance with their acoustic-phonetic content. For example, an early and automatic process of perceptual repair, which ensures conformity of speech inputs to the listener's native language phonology, applies to individual input segments that do not exist in the native inventory or to sound sequences that are illicit according to the native phonotactic restrictions on sound co-occurrences. The present study with Russian and Canadian English speakers shows that listeners may perceive phonetically distinct and licit sound sequences as equivalent when the native language system provides robust evidence for mapping multiple phonetic forms onto a single phonological representation. In Russian, due to an optional but productive t-deletion process that affects /stn/ clusters, the surface forms [sn] and [stn] may be phonologically equivalent and map to a single phonological form /stn/. In contrast, [sn] and [stn] clusters are usually phonologically distinct in (Canadian) English. Behavioral data from identification and discrimination tasks indicated that [sn] and [stn] clusters were more confusable for Russian than for English speakers. The EEG experiment employed an oddball paradigm with nonwords [asna] and [astna] used as the standard and deviant stimuli. A reliable mismatch negativity response was elicited approximately 100 msec postchange in the English group but not in the Russian group. These findings point to a perceptual repair mechanism that is engaged automatically at a prelexical level to ensure immediate encoding of speech inputs in phonological terms, which in turn enables efficient access to the meaning of a spoken utterance.

  8. Image processing applied to automatic detection of defects during ultrasonic examination

    International Nuclear Information System (INIS)

    Moysan, J.

    1992-10-01

    This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

  9. Automatic real-time detection of endoscopic procedures using temporal features.

    Science.gov (United States)

    Stanek, Sean R; Tavanapong, Wallapak; Wong, Johnny; Oh, Jung Hwan; de Groen, Piet C

    2012-11-01

    Endoscopy is used for inspection of the inner surface of organs such as the colon. During endoscopic inspection of the colon or colonoscopy, a tiny video camera generates a video signal, which is displayed on a monitor for interpretation in real-time by physicians. In practice, these images are not typically captured, which may be attributed by lack of fully automated tools for capturing, analysis of important contents, and quick and easy retrieval of these contents. This paper presents the description and evaluation results of our novel software that uses new metrics based on image color and motion over time to automatically record all images of an individual endoscopic procedure into a single digitized video file. The software automatically discards out-patient video frames between different endoscopic procedures. We validated our software system on 2464 h of live video (over 265 million frames) from endoscopy units where colonoscopy and upper endoscopy were performed. Our previous classification method achieved a frame-based sensitivity of 100.00%, but only a specificity of 89.22%. Our new method achieved a frame-based sensitivity and specificity of 99.90% and 99.97%, a significant improvement. Our system is robust for day-to-day use in medical practice. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  10. The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

    Science.gov (United States)

    Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

    2017-12-01

    In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long

  11. TU-E-BRA-04: Real-Time Automatic Fiducial Marker Detection in Low Contrast Cine-MV Images.

    Science.gov (United States)

    Liu, W; Lin, W; Ahmad, M; Nath, R

    2012-06-01

    Intrafraction motion tracking using beam-line MV images have gained much attention because no additional imaging dose is introduced. Since MV images have much lower contrast than kV images, a robust marker detection algorithm is a pre-requisite. In this work, we develop a novel, fast, and robust method to detect implanted markers in low-contrast cine-MV patient images. Several marker detection methods have been proposed in the recent years. These methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover the different situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the ROI. We solve this problem by synergetic use of modern but well-tested computer vision and AI techniques - detect implanted markers utilizing discriminant analysis for initialization and mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated using 1149 cine-MV images from 2 prostate IMRT patients and compared with manual marker detection results from 6 researchers. The average of the manual detection results is considered as the ground truth. The average RMS errors of the automatic tracking from the ground truth are 1.9 and 2.1 pixels for the 2 patients (0.26mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed method can achieve similar marker detection accuracy to manual detection in low-contract cine-MV images. © 2012 American Association of

  12. A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech.

    Science.gov (United States)

    Toth, Laszlo; Hoffmann, Ildiko; Gosztolya, Gabor; Vincze, Veronika; Szatloczki, Greta; Banreti, Zoltan; Pakaski, Magdolna; Kalman, Janos

    2018-01-01

    Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. The temporal analysis of spontaneous speech

  13. Automatic seismic event detection using migration and stacking: a performance and parameter study in Hengill, southwest Iceland

    Science.gov (United States)

    Wagner, F.; Tryggvason, A.; Roberts, R.; Lund, B.; Gudmundsson, Ó.

    2017-06-01

    We investigate the performance of a seismic event detection algorithm using migration and stacking of seismic traces. The focus lies on determining optimal data dependent detection parameters for a data set from a temporary network in the volcanically active Hengill area, southwest Iceland. We test variations of the short-term average to long-term average and Kurtosis functions, calculated from filtered seismic traces, as input data. With optimal detection parameters, our algorithm identified 94 per cent (219 events) of the events detected by the South Iceland Lowlands (SIL) system, that is, the automatic system routinely used on Iceland, as well as a further 209 events, previously missed. The assessed number of incorrect (false) detections was 25 per cent for our algorithm, which was considerably better than that from SIL (40 per cent). Empirical tests show that well-functioning processing parameters can be effectively selected based on analysis of small, representative subsections of data. Our migration approach is more computationally expensive than some alternatives, but not prohibitively so, and it appears well suited to analysis of large swarms of low magnitude events with interevent times on the order of seconds. It is, therefore, an attractive, practical tool for monitoring of natural or anthropogenic seismicity related to, for example, volcanoes, drilling or fluid injection.

  14. Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior

    Directory of Open Access Journals (Sweden)

    Annelies Van Nuffel

    2015-08-01

    Full Text Available Despite the research on opportunities to automatically measure lameness in cattle, lameness detection systems are not widely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat them properly and in a timely fashion. Many papers have focused on the automated measurement of gait or behavioral cow characteristics related to lameness. In order for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cows need to be developed and validated. Few studies have reached this latter stage of the development process. Also, comparison between the different approaches is impeded by the wide range of practical settings used to measure the gait or behavioral characteristic (e.g., measurements during normal farming routine or during experiments; cows guided or walking at their own speed and by the different definitions of lame cows. In the majority of the publications, mildly lame cows are included in the non-lame cow group, which limits the possibility of also detecting early lameness cases. In this review, studies that used sensor technology to measure changes in gait or behavior of cows related to lameness are discussed together with practical considerations when conducting lameness research. In addition, other prerequisites for any lameness detection system on farms (e.g., need for early detection, real-time measurements are discussed.

  15. A novel automatic molecular test for detection of multidrug resistance tuberculosis in sputum specimen: A case control study.

    Science.gov (United States)

    Li, Qiang; Ou, Xi C; Pang, Yu; Xia, Hui; Huang, Hai R; Zhao, Bing; Wang, Sheng F; Zhao, Yan L

    2017-07-01

    MiniLab tuberculosis (ML TB) assay is a new automatic diagnostic tool for diagnosis of multidrug resistance tuberculosis (MDR-TB). This study was conducted with aims to know the performance of this assay. Sputum sample from 224 TB suspects was collected from tuberculosis suspects seeking medical care at Beijing Chest hospital. The sputum samples were directly used for smear and ML TB test. The left sputum sample was used to conduct Xpert MTB/RIF, Bactec MGIT culture and drug susceptibility test (DST). All discrepancies between the results from DST, molecular and phenotypic methods were confirmed by DNA Sequencing. The sensitivity and specificity of ML TB test for detecting MTBC from TB suspects were 95.1% and 88.9%, respectively. The sensitivity for smear negative TB suspects was 64.3%. For detection of RIF resistance, the sensitivity and specificity of ML TB test were 89.2% and 95.7%, respectively. For detection of INH resistance, the sensitivity and specificity of ML TB test were 78.3% and 98.1%, respectively. ML TB test showed similar performance to Xpert MTB/RIF for detection of MTBC and RIF resistance. In addition, ML TB also had good performance for INH resistance detection. Copyright © 2017. Published by Elsevier Ltd.

  16. Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

    DEFF Research Database (Denmark)

    Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.

    2013-01-01

    In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...

  17. An Automatic Optic Disk Detection and Segmentation System using Multi-level Thresholding

    Directory of Open Access Journals (Sweden)

    KARASULU, B.

    2014-05-01

    Full Text Available Optic disk (OD boundary localization is a substantial problem in ophthalmic image processing research area. In order to segment the region of OD, we developed an automatic system which involves a multi-level thresholding. The OD segmentation results of the system in terms of average precision, recall and accuracy for DRIVE database are 98.88%, 99.91%, 98.83%, for STARE database are 98.62%, 97.38%, 96.11%, and for DIARETDB1 database are 99.29%, 99.90%, 99.20%, respectively. The experimental results show that our system works properly on retinal image databases with diseased retinas, diabetic signs, and a large degree of quality variability.

  18. Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

    Science.gov (United States)

    Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

    2018-02-01

    Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

  19. 3D Face Model Dataset: Automatic Detection of Facial Expressions and Emotions for Educational Environments

    Science.gov (United States)

    Chickerur, Satyadhyan; Joshi, Kartik

    2015-01-01

    Emotion detection using facial images is a technique that researchers have been using for the last two decades to try to analyze a person's emotional state given his/her image. Detection of various kinds of emotion using facial expressions of students in educational environment is useful in providing insight into the effectiveness of tutoring…

  20. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs.

    NARCIS (Netherlands)

    Niemeijer, M.; Ginneken, B. van; Cree, M.J.; Mizutani, A.; Quellec, G.; Sanchez, C.I.; Zhang, B.; Hornero, R.; Lamard, M.; Muramatsu, C.; Wu, X.; Cazuguel, G.; You, J.; Mayo, A.; Li, Q.; Hatanaka, Y.; Cochener, B.; Roux, C.; Karray, F.; Garcia, M.; Fujita, H.; Abramoff, M.D.

    2010-01-01

    The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each

  1. Automatic Detection of Cow’s Oestrus in Audio Surveillance System

    Directory of Open Access Journals (Sweden)

    Y. Chung

    2013-07-01

    Full Text Available Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea. In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone and accurately (over 94% accuracy, either as a standalone solution or to complement known methods.

  2. Automatic detection of recoil proton tracks and background rejection in liquid scintillator-micro-capillary-array fast neutron spectrometer

    Science.gov (United States)

    Mor, I.; Vartsky, D.; Dangendorf, V.; Tittelmeier, K.; Goldberg, M. B.; Bar, D.; Brandis, M.

    2017-12-01

    We describe results on automatic detection of fast-neutron induced recoil-proton tracks in micro-capillary bundles filled with organic liquid scintillator, viewed by an intensified CCD camera. This imaging neutron spectrometer was developed for high position-resolution (few tens of μ m) imaging and medium-quality energy spectroscopy of neutrons in the energy range 2–20 MeV. In addition to recoil proton events which display a continuous extended track structure, CCD images include also a multitude of isolated spots of varying intensity ("blobs") that originate from several different sources, such as fragmented proton tracks, electrons from gamma-rays, short heavy-ion tracks, as well as events and noise that occur in the image intensifier and CCD. In order to identify the extended-track recoil proton events and separate them from these background events, a computerized, rapid and automatic track-recognition procedure was developed. Based on the analysis of track parameters such as: track area, light intensity, track length and width, the method is capable of distinguishing few recoil protons from the surrounding numerous background events typically found in each CCD frame.

  3. Detection of viable myocardium by dobutamine stress tagging magnetic resonance imaging with three-dimensional analysis by automatic trace method

    Energy Technology Data Exchange (ETDEWEB)

    Saito, Isao [Yatsu Hoken Hospital, Narashino, Chiba (Japan); Watanabe, Shigeru; Masuda, Yoshiaki

    2000-07-01

    The present study attempted to detect the viability of myocardium by quantitative automatic 3-dimensional analysis of the improvement of regional wall motion using an magnetic resonance imaging (MRI) tagging method. Twenty-two subjects with ischemic heart disease who had abnormal wall motion on echocardiography at rest were enrolled. All patients underwent dobutamine stress echocardiography (DSE), coronary arteriography and left ventriculography. The results were compared with those of 7 normal volunteers. MRI studies were done with myocardial tagging using the spatial modulation of magnetization technique. Automatic tracing with an original program was performed, and wall motion was compared before and during dobutamine infusion. The evaluation of myocardial viability with MRI and echocardiography had similar results in 19 (86.4%) of the 22 patients; 20 were studied by positron emission tomography or thallium-201 single photon emission computed tomography for myocardial viability, or studied for improvement of wall motion following coronary intervention. The sensitivity of dobutamine stress MRI (DSMRI) with tagging was 75.9% whereas that of DSE was 65.5%. The specificity of DSMRI was 85.7% (6/7) and that of DSE was 100% (7/7). The accuracy of DSMRI was 77.8% (28/36) and that of DSE 72.2% (26/36). DSMRI was shown to be superior to DSE in terms of evaluation of myocardial viability. (author)

  4. Automatic Reacquisition of Satellite Positions by Detecting Their Expected Streaks in Astronomical Images

    Science.gov (United States)

    Levesque, M.

    Artificial satellites, and particularly space junk, drift continuously from their known orbits. In the surveillance-of-space context, they must be observed frequently to ensure that the corresponding orbital parameter database entries are up-to-date. Autonomous ground-based optical systems are periodically tasked to observe these objects, calculate the difference between their predicted and real positions and update object orbital parameters. The real satellite positions are provided by the detection of the satellite streaks in the astronomical images specifically acquired for this purpose. This paper presents the image processing techniques used to detect and extract the satellite positions. The methodology includes several processing steps including: image background estimation and removal, star detection and removal, an iterative matched filter for streak detection, and finally false alarm rejection algorithms. This detection methodology is able to detect very faint objects. Simulated data were used to evaluate the methodology's performance and determine the sensitivity limits where the algorithm can perform detection without false alarm, which is essential to avoid corruption of the orbital parameter database.

  5. An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

    Directory of Open Access Journals (Sweden)

    Mustain Billah

    2017-01-01

    Full Text Available Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW features and convolutional neural network (CNN features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM. Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.

  6. Numerized image treatment process for stenoses automatic detection; Procede de traitement d`images numerisees pour la detection automatique de stenoses

    Energy Technology Data Exchange (ETDEWEB)

    Makram-Ebeid Sherif

    1995-01-27

    Numerical image treatment process in angiography X-ray imaging for the automatic stenose detection, including at least pixel identification stage. Those last are situated on the vessel central lines, as it is called ``tracking`` step. This one includes one first filter operation brought to a satisfactory conclusion, applying on each image, recursive bidimensional selective low-pass N filter series, with losange support, including one of the main directions, which is regularly oriented in the image plan between 0 and {pi}-{pi}/N, from {pi}/N to {pi}/N, to detect each vessel part direction, like that where one filter answer is maximal. Uses: numerical angiography systems. 5 refs., 16 figs.

  7. Preliminary study of automatic detection method for anatomical landmarks in body trunk CT images

    International Nuclear Information System (INIS)

    Nemoto, Mitsutaka; Nomura, Yukihiro; Masutani, Yoshitaka; Yoshikawa, Takeharu; Hayashi, Naoto; Yoshioka, Naoki; Ohtomo, Kuni; Hanaoka, Shouhei

    2010-01-01

    In the research field of medical image processing and analysis, it is important to develop medical image understanding methods which are robust for individual and case differences, since they often interfere with accurate medical image processing and analysis. Location of anatomical landmarks, which are localized regions with anatomical reference to the human body, allows for robust medical understanding since the relative position of anatomical landmarks is basically the same among cases. This is a preliminary study for detecting anatomical point landmarks by using a technique of local area model matching. The model for matching process, which is called appearance model, shows the spatial appearance of voxel values at the detection target landmark and its surrounding region, while the Principal Component Analysis (PCA) is used to train appearance models. In this study, we experimentally investigate the optimal appearance model for landmark detection and analyze detection accuracy of anatomical point landmarks. (author)

  8. Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.

    Science.gov (United States)

    Ubeyli, Elif Derya

    2009-10-01

    This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer.

  9. Precise Automatic Image Coregistration Tools to Enable Pixel-Level Change Detection, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Automated detection of land cover changes between multitemporal images (i.e., images captured at different times) has long been a goal of the remote sensing...

  10. Precise automatic image coregistration tools to enable pixel-level change detection, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Automated detection of land cover changes between multitemporal images has long been a goal of the remote sensing discipline. Most research in this area has focused...

  11. Automatic near-real-time detection of CMEs in Mauna Loa K-Cor coronagraph images

    Science.gov (United States)

    Thompson, W. T.; St Cyr, O. C.; Burkepile, J.; Posner, A.

    2017-12-01

    A simple algorithm has been developed to detect the onset of coronal massejections (CMEs), together with an estimate of their speed, in near-real-timeusing images of the linearly polarized white-light solar corona taken by theK-Cor telescope at the Mauna Loa Solar Observatory (MLSO). The algorithm usedis a variation on the Solar Eruptive Event Detection System (SEEDS) developedat George Mason University. The algorithm was tested against K-Cor data takenbetween 29 April 2014 and 20 February 2017, on days which the MLSO websitemarked as containing CMEs. This resulted in testing of 139 days worth of datacontaining 171 CMEs. The detection rate varied from close to 80% in 2014-2015when solar activity was high, down to as low as 20-30% in 2017 when activitywas low. The difference in effectiveness with solar cycle is attributed to thedifference in relative prevalance of strong CMEs between active and quietperiods. There were also twelve false detections during this time period,leading to an average false detection rate of 8.6% on any given day. However,half of the false detections were clustered into two short periods of a fewdays each when special conditions prevailed to increase the false detectionrate. The K-Cor data were also compared with major Solar Energetic Particle(SEP) storms during this time period. There were three SEP events detectedeither at Earth or at one of the two STEREO spacecraft where K-Cor wasobserving during the relevant time period. The K-Cor CME detection algorithmsuccessfully generated alerts for two of these events, with lead times of 1-3hours before the SEP onset at 1 AU. The third event was not detected by theautomatic algorithm because of the unusually broad width of the CME in positionangle.

  12. Automatic target recognition and detection in infrared imagery under cluttered background

    Science.gov (United States)

    Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

    2017-10-01

    Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

  13. Dual-energy X-ray radiography for automatic high-Z material detection

    International Nuclear Information System (INIS)

    Chen Gongyin; Bennett, Gordon; Perticone, David

    2007-01-01

    There is an urgent need for high-Z material detection in cargo. Materials with Z > 74 can indicate the presence of fissile materials or radiation shielding. Dual (high) energy X-ray material discrimination is based on the fact that different materials have different energy dependence in X-ray attenuation coefficients. This paper introduces the basic physics and analyzes the factors that affect dual-energy material discrimination performance. A detection algorithm is also discussed

  14. Lamb wave based automatic damage detection using matching pursuit and machine learning

    International Nuclear Information System (INIS)

    Agarwal, Sushant; Mitra, Mira

    2014-01-01

    In this study, matching pursuit (MP) has been tested with machine learning algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) to automate the process of damage detection in metallic plates. Here, damage detection is done using the Lamb wave response in a thin aluminium plate simulated using a finite element (FE) method. To reduce the complexity of the Lamb wave response, only the A 0 mode is excited and sensed. The procedure adopted for damage detection consists of three major steps, involving signal processing and machine learning (ML). In the first step, MP is used for de-noising and enhancing the sparsity of the database. In the existing literature, MP is used to decompose any signal into a linear combination of waveforms that are selected from a redundant dictionary. In this work, MP is deployed in two stages to make the database sparse as well as to de-noise it. After using MP on the database, it is then passed as input data for ML classifiers. ANN and SVM are used to detect the location of the potential damage from the reduced data. The study demonstrates that the SVM is a robust classifier in the presence of noise and is more efficient than the ANN. Out-of-sample data are used for the validation of the trained and tested classifier. Trained classifiers are found to be successful in the detection of damage with a detection rate of more than 95%. (paper)

  15. Automatic detection of the optimal ejecting direction based on a discrete Gauss map

    Directory of Open Access Journals (Sweden)

    Masatomo Inui

    2014-01-01

    Full Text Available In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level “rough” Gauss map with rather sparse point distribution and another lower level “fine” Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

  16. Campylobacter spp. as emerging food-borne pathogen - incidence, detection and resistance

    Directory of Open Access Journals (Sweden)

    S. Smole Možina

    2005-02-01

    Full Text Available Campylobacter jejuni and Campylobacter. coli are the leading cause of bacterial food-borne enteric infection with still increasing incidence in the most developed countries. Consuming and/or handling poultry meat is the most consistent risk factor, linked to the high prevalence of campylobacters in retail poultry meat. Recent data about the incidence of human campylobacteriosis and prevalence of C.jejuni and C. coli in poultry meat are presented. Important aspects of Campylobacter transmission along the food chain are discussed – physiological specificities possibly enabling adaptation and survival in the food production environment as well as the emerging resistance to antimicrobial agents used in veterinary and human medicine. Recent advances in detection and identification methods of Campylobacter spp. are mentioned as a basis for preventive strategies to bring these food-borne pat hogens under control. Recent risk assessments show that mitigation strategies could be applied at different points from food-animals production to the finalconsumptionoffoods.Educating the consumers is important,sincecritical control point remains the hygiene in the final food preparation.

  17. Radar Satellite Imagery and Automatic Detection of Water Bodies : Radarski satelitski snimci i automatsko otkrivanje vodenih površina

    Directory of Open Access Journals (Sweden)

    Klemen Čotar

    2016-12-01

    Full Text Available System for mapping of water bodies in Slovenia and its immediate neighbourhood with Sentinel-1 radar satellites have implemented. Algorithms automatically detect presence of new data in the archive, download the data, analyse it, write the results, and upload them to a web portal. New acquisitions are currently available every six days, but this time will be halved when the second Sentinel-1 starts delivering the data. : Implementiran je sistem za kartiranje vodenih površina u Sloveniji i u neposrednoj blizini sa Sentinel-1 radarskim satelitima. Algoritmi automatski otkrivaju prisutnost novih podataka u arhivu, preuzimaju podatake, analiziraju, objavljuju rezultate, te ih prenose na web-portal. Nove akvizicije su trenutno dostupne svakih šest dana, ali ovaj puta će vrijeme biti prepolovljeno, kada drugi Sentinel-1 počne sa isporukom podataka.

  18. Novel Automatic Detection of Pleura and B-lines (Comet-Tail Artifacts) on In-Vivo Lung Ultrasound Scans

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hansen, Kristoffer Lindskov; Møller-Sørensen, Hasse

    2016-01-01

    This paper presents a novel automatic method for detection of B-lines (comet-tail artifacts) in lung ultrasound scans. B-lines are the most commonly used artifacts for analyzing the pulmonary edema. They appear as laser-like vertical beams, which arise from the pleural line and spread down without...... fading to the edge of the screen. An increase in their number is associated with presence of edema. All the scans used in this study were acquired using a BK3000 ultrasound scanner (BK Ultrasound, Denmark) driving a 192-element 5.5 MHz wide linear transducer (10L2W, BK Ultrasound). The dynamic received...

  19. Hazard detection in noise-related incidents - the role of driving experience with battery electric vehicles.

    Science.gov (United States)

    Cocron, Peter; Bachl, Veronika; Früh, Laura; Koch, Iris; Krems, Josef F

    2014-12-01

    The low noise emission of battery electric vehicles (BEVs) has led to discussions about how to address potential safety issues for other road users. Legislative actions have already been undertaken to implement artificial sounds. In previous research, BEV drivers reported that due to low noise emission they paid particular attention to pedestrians and bicyclists. For the current research, we developed a hazard detection task to test whether drivers with BEV experience respond faster to incidents, which arise due to the low noise emission, than inexperienced drivers. The first study (N=65) revealed that BEV experience only played a minor role in drivers' response to hazards resulting from low BEV noise. The tendency to respond, reaction times and hazard evaluations were similar among experienced and inexperienced BEV drivers; only small trends in the assumed direction were observed. Still, both groups clearly differentiated between critical and non-critical scenarios and responded accordingly. In the second study (N=58), we investigated additionally if sensitization to low noise emission of BEVs had an effect on hazard perception in incidents where the noise difference is crucial. Again, participants in all groups differentiated between critical and non-critical scenarios. Even though trends in response rates and latencies occurred, experience and sensitization to low noise seemed to only play a minor role in detecting hazards due to low BEV noise. An additional global evaluation of BEV noise further suggests that even after a short test drive, the lack of noise is perceived more as a comfort feature than a safety threat. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. AUTOMATIC DETECTION AND TRACKING OF CORONAL MASS EJECTIONS. II. MULTISCALE FILTERING OF CORONAGRAPH IMAGES

    International Nuclear Information System (INIS)

    Byrne, Jason P.; Morgan, Huw; Habbal, Shadia R.; Gallagher, Peter T.

    2012-01-01

    Studying coronal mass ejections (CMEs) in coronagraph data can be challenging due to their diffuse structure and transient nature, and user-specific biases may be introduced through visual inspection of the images. The large amount of data available from the Solar and Heliospheric Observatory (SOHO), Solar TErrestrial RElations Observatory (STEREO), and future coronagraph missions also makes manual cataloging of CMEs tedious, and so a robust method of detection and analysis is required. This has led to the development of automated CME detection and cataloging packages such as CACTus, SEEDS, and ARTEMIS. Here, we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of current catalogs. It works by first employing the dynamic CME separation technique outlined in a companion paper, and then characterizing CME structure via a multiscale edge-detection algorithm. The detections are chained through time to determine the CME kinematics and morphological changes as it propagates across the plane of sky. The effectiveness of the method is demonstrated by its application to a selection of SOHO/LASCO and STEREO/SECCHI images, as well as to synthetic coronagraph images created from a model corona with a variety of CMEs. The algorithms described in this article are being applied to the whole LASCO and SECCHI data sets, and a catalog of results will soon be available to the public.

  1. Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2011-09-01

    Full Text Available Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

  2. Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

    Science.gov (United States)

    Liu, Yi-Hung; Chen, Yan-Jen

    2011-01-01

    Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

  3. Automatic detection and classification of malarial retinopathy- associated retinal whitening in digital retinal images

    International Nuclear Information System (INIS)

    Akram, M.U.; Alvi, A.B.N.; Khan, S.A.

    2017-01-01

    Malarial retinopathy addresses diseases that are characterized by abnormalities in retinal fundus imaging. Macular whitening is one of the distinct signs of cerebral malaria but has hardly been explored as a critical bio-marker. The paper proposes a computerized detection and classification method for malarial retinopathy using retinal whitening as a bio-marker. The paper combines various statistical and color based features to form a sound feature set for accurate detection of retinal whitening. All features are extracted at image level and feature selection is performed to detect most discriminate features. A new method for macula location is also presented. The detected macula location is further used for grading of whitening as macular or peripheral whitening. Support vector machine along with radial basis function is used for classification of normal and malarial retinopathy patients. The evaluation is performed using a locally gathered dataset from malarial patients and it achieves an accuracy of 95% for detection of retinal whitening and 100% accuracy for grading of retinal whitening as macular or non-macular. One of the major contributions of proposed method is grading of retinal whitening into macular or peripheral whitening. (author)

  4. Morphological detection algorithms for the automatic implantable cardioverter/defibrillator (AICD).

    Science.gov (United States)

    Kaup, H J; Hexamer, M; Werner, J

    2004-11-01

    To prevent sudden cardiac death of patients who are at risk from long standing tachyarrhythmia the implantable cardioverter defibrillator (ICD) is the first choice therapy. ICDs use a range of electrostimuli up to defibrillation, which is a non synchronous high energy shock, whereas cardioversion is synchronous with the ECG. In order to know when and how to react, a detection algorithm, which analyses an intracardial electrocardiogram (ECG) and classifies the heart rhythm, is implemented in every ICD. All detection algorithms use the heart rate to classify the different heart rhythms roughly. If a tachycardia is detected, it is important to discriminate between a ventricular tachycardia, which is life threatening and a supraventricular tachycardia, which is much less threatening. To be able to make this distinction the detection algorithms analyse the behaviour of the heart cycle intervals, the ECG-morphology or in addition to the ventricular ECG, an atrial ECG. In this paper morphological algorithms will be evaluated and newly developed algorithms will be presented. Recent algorithms use the mathematical wavelet theory. The evaluation shows that these get better results than all but one of the simpler classical morphological algorithms. A new wavelet based algorithm, developed by the authors, exhibits the best detection results.

  5. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies

    Energy Technology Data Exchange (ETDEWEB)

    Ganiler, Onur; Oliver, Arnau; Diez, Yago; Freixenet, Jordi; Llado, Xavier [University of Girona, VICOROB Computer Vision and Robotics Group, Girona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Beltran, Brigitte [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Girona (Spain); Ramio-Torrenta, Lluis [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Multiple Sclerosis and Neuroimmunology Unit, Girona (Spain); Rovira, Alex [Vall d' Hebron University Hospital, Magnetic Resonance Unit, Department of Radiology, Barcelona (Spain)

    2014-05-15

    Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies. The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections. Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher. Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies. (orig.)

  6. Method and apparatus for automatically detecting patterns in digital point-ordered signals

    Science.gov (United States)

    Brudnoy, D.M.

    1998-10-20

    The present invention is a method and system for detecting a physical feature of a test piece by detecting a pattern in a signal representing data from inspection of the test piece. The pattern is detected by automated additive decomposition of a digital point-ordered signal which represents the data. The present invention can properly handle a non-periodic signal. A physical parameter of the test piece is measured. A digital point-ordered signal representative of the measured physical parameter is generated. The digital point-ordered signal is decomposed into a baseline signal, a background noise signal, and a peaks/troughs signal. The peaks/troughs from the peaks/troughs signal are located and peaks/troughs information indicating the physical feature of the test piece is output. 14 figs.

  7. An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

    Science.gov (United States)

    Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

    2014-01-01

    This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

  8. Automatic Tool Selection in V-bending Processes by Using an Intelligent Collision Detection Algorithm

    Science.gov (United States)

    Salem, A. A.

    2017-09-01

    V-bending is widely used to produce the sheet metal components. There are global Changes in the shape of the sheet metal component during progressive bending processes. Accordingly, collisions may be occurred between part and tool during bending. Collision-free is considered one of the feasibility conditions of V-bending process planning which the tool selection is verified by the absence of the collisions. This paper proposes an intelligent collision detection algorithm which has the ability to distinguish between 2D bent parts and the other bent parts. Due to this ability, 2D and 3D collision detection subroutines have been developed in the proposed algorithm. This division of algorithm’s subroutines could reduce the computational operations during collisions detecting.

  9. Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.

    Science.gov (United States)

    Zolgharni, Massoud; Negoita, Madalina; Dhutia, Niti M; Mielewczik, Michael; Manoharan, Karikaran; Sohaib, S M Afzal; Finegold, Judith A; Sacchi, Stefania; Cole, Graham D; Francis, Darrel P

    2017-07-01

    Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (Paverage human operator was an outlier in 254/564 heartbeats (45%). An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame. © 2017, Wiley Periodicals, Inc.

  10. Automatic detection of outlines. Application to the quantitative analysis of renal scintiscanning pictures

    International Nuclear Information System (INIS)

    Morcos-Ghrab, Nadia.

    1979-01-01

    The purpose of the work described is the finalizing of a method making it possible automatically to extract the significant outlines on a renal scintiscanning picture. The algorithms must be simple and of high performance, their routine execution on a mini-computer must be fast enough to compete effectively with human performances. However, the method that has been developed is general enough to be adapted, with slight modifications, to another type of picture. The first chapter is a brief introduction to the principle of scintiscanning, the equipment used and the type of picture obtained therefrom. In the second chapter the various approaches used for form recognition and scene analysis are very briefly described with the help of examples. The third chapter deals with pretreatment techniques (particularly the machine operators) used for segmenting the pictures. Chapter four presents techniques which segment the picture by parallel processing of all its points. In chapter five a description is given of the sequential research techniques of the outline elements, drawing inspiration from the methods used in artificial intelligence for resolving the optimization problem. The sixth chapter shows the difficulties encountered in extracting the renal outlines and the planning technique stages adopted to overcome these difficulties. Chapter seven describes in detail the two research methods employed for generating the plan. In chapter eight, the methods used for extending the areas obtained on the plan and for refining the outlines that bound them are dealt with. Chapter nine is a short presentation of the organization of the programmes and of their data structure. Finally, examples of results are given in chapter ten [fr

  11. Automatic parametrization of Support Vector Machines for short texts polarity detection

    Directory of Open Access Journals (Sweden)

    Aurelio Sanabria Rodríguez

    2017-04-01

    Full Text Available The information from social media is emerging as a valuable source in decision-making, unfortunately the tools to turn these data into useful information still need some work. Using Support Vector Machines for polarity detection in short texts are popular among researchers for their good results, but parameter optimization to train classification models is a complex and costly process. This article compares two algorithms for automated parameter optimization in the process of creating classification models for polarity detection: the recently created Grey Wolf Optimizer and the Grid Search, using accuracy and f-score metrics.

  12. Automatic Detection of Atrial Fibrillation Using Basic Shannon Entropy of RR Interval Feature

    Science.gov (United States)

    Afdala, Adfal; Nuryani, Nuryani; Satriyo Nugroho, Anto

    2017-01-01

    Atrial Fibrillation is one of heart disease, that common characterized by irregularity heart beat. Atrial fibrillation leads to severe complications such as cardiac failure with the subsequent risk of a stroke. A method to detect atrial fibrillation is needed to prevent a risk of atrial fibrillation. This research uses data from physionet in atrial fibrillation database category. The performance of Shannon entropy has the highest accuracy if a threshold is 0.5 with accuracy 89.79%, sensitivity 91.04% and specificity 89.01%. Based on the result we get a conclusion, the ability of Shannon entropy to detect atrial fibrillation is good.

  13. Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

    Science.gov (United States)

    Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.

    2018-04-01

    Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

  14. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

    Directory of Open Access Journals (Sweden)

    Shuang Mei

    2018-04-01

    Full Text Available Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality. Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

  15. Automatic change detection and quantification of dermatological diseases with an application to psoriasis images

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Butakoff, C.; Ersbøll, Bjarne Kjær

    2007-01-01

    Change monitoring in skin lesion analysis has proven to be a useful adjunct in their assessment. This article presents a comparative study of the available change detection techniques applied to change visualization and quantification in bi-temporal psoriasis images. The chosen methods are evalua...

  16. Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

    Czech Academy of Sciences Publication Activity Database

    Dvořák, Pavel; Kropatsch, W.G.; Bartušek, Karel

    2013-01-01

    Roč. 13, č. 5 (2013), s. 223-230 ISSN 1335-8871 R&D Projects: GA ČR GAP102/12/1104; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : Brain tumor * Brain tumor detection * Symmetry analysis Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.162, year: 2013

  17. Automatic lameness detection based on consecutive 3D-video recordings

    NARCIS (Netherlands)

    Hertem, van T.; Viazzi, S.; Steensels, M.; Maltz, E.; Antler, A.; Alchanatis, V.; Schlageter-Tello, A.; Lokhorst, C.; Romanini, C.E.B.; Bahr, C.; Berckmans, D.; Halachmi, I.

    2014-01-01

    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive

  18. Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients

    Directory of Open Access Journals (Sweden)

    Mahsa Partovi

    2016-05-01

    Full Text Available Introduction: Diabetic retinopathy (DR is the most frequent microvascular complication of diabetes and can lead to several retinal abnormalities including microaneurysms, exudates, dot and blot hemorrhages, and cotton wool spots. Automated early detection of these abnormalities could limit the severity of the disease and assist ophthalmologists in investigating and treating the disease more efficiently. Segmentation of retinal image features provides the basis for automated assessment. In this study, exudates lesion on retinopathy retinal images was segmented by different image processing techniques. The objective of this study is detection of the exudates regions on retinal images of retinopathy patients by different image processing techniques. Methods: A total of 30 color images from retinopathy patients were selected for this study. The images were taken by Topcon TRC-50 IX mydriatic camera and saves with TIFF format with a resolution of 500 × 752 pixels. The morphological function was applied on intensity components of hue saturation intensity (HSI space. To detect the exudates regions, thresholding was performed on all images and the exudates region was segmented. To optimize the detection efficiency, the binary morphological functions were applied. Finally, the exudates regions were quantified and evaluated for further statistical purposes. Results: The average of sensitivity of 76%, specificity of 98%, and accuracy of 97% was obtained. Conclusion: The results showed that our approach can identify the exudate regions in retinopathy images.

  19. [Detection of Brucella with an automatic hemoculture system: Bact/Alert].

    Science.gov (United States)

    Casas, J; Partal, Y; Llosá, J; Leiva, J; Navarro, J M; de la Rosa, M

    1994-12-01

    The ability of in vitro and in vivo detection of Brucella spp. with the Bact/Alert system was studied. Three strains of Brucella melitensis and two of Brucella abortus were used. Different dilutions of the five strains were performed in trypticase soy broth (TSB), achieving concentrations of 1 cfu/ml, 5 cfu/ml, 10 cfu/ml and 100 cfu/ml. Ten ml of each dilution and strain were inoculated into 5 aerobic bottles Bact/Alert and 5 biphasic Hemóline bottles. Furthermore, over a 9 month period, 8,216 bottles of Bact/Alert bottles from hospitalized patients and from the emergency department were processed in the authors' laboratory. The mean detection time for Brucella growth was from 2 to 3 days with the Bact/Alert system, and 14 days in the biphasic bottles. Former bottles processed in the authors' laboratory, 11 aerobic bottles belonged to 5 patients in whom brucelosis was confirmed by bloodculture. The Bact/Alert system detected Brucella melitensis in only on bottle at 2.9 days of incubation. In 7 bottles Bact/Alert detected B. melitensis by a blind pass of these bottles at 10 to 20 days of incubation. These results suggest that the Bact/Alert system does not totally solve the diagnosis of brucellosis. Blind passes of the bloodcultures are required.

  20. Detection of tuberculosis with digital chest radiography: automatic reading versus interpretation by clinical officers

    NARCIS (Netherlands)

    Maduskar, P.; Muyoyeta, M.; Ayles, H.; Hogeweg, L.; Peters-Bax, L.; Ginneken, B. van

    2013-01-01

    SETTING: A busy urban health centre in Lusaka, Zambia. OBJECTIVE: To compare the accuracy of automated reading (CAD4TB) with the interpretation of digital chest radiograph (CXR) by clinical officers for the detection of tuberculosis (TB). DESIGN: A retrospective analysis was performed on 161

  1. Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

    KAUST Repository

    Salas-Zárate, María del Pilar

    2017-04-24

    In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.

  2. Automatic detection of large pulmonary solid nodules in thoracic CT images

    International Nuclear Information System (INIS)

    Setio, Arnaud A. A.; Jacobs, Colin; Gelderblom, Jaap; Ginneken, Bram van

    2015-01-01

    Purpose: Current computer-aided detection (CAD) systems for pulmonary nodules in computed tomography (CT) scans have a good performance for relatively small nodules, but often fail to detect the much rarer larger nodules, which are more likely to be cancerous. We present a novel CAD system specifically designed to detect solid nodules larger than 10 mm. Methods: The proposed detection pipeline is initiated by a three-dimensional lung segmentation algorithm optimized to include large nodules attached to the pleural wall via morphological processing. An additional preprocessing is used to mask out structures outside the pleural space to ensure that pleural and parenchymal nodules have a similar appearance. Next, nodule candidates are obtained via a multistage process of thresholding and morphological operations, to detect both larger and smaller candidates. After segmenting each candidate, a set of 24 features based on intensity, shape, blobness, and spatial context are computed. A radial basis support vector machine (SVM) classifier was used to classify nodule candidates, and performance was evaluated using ten-fold cross-validation on the full publicly available lung image database consortium database. Results: The proposed CAD system reaches a sensitivity of 98.3% (234/238) and 94.1% (224/238) large nodules at an average of 4.0 and 1.0 false positives/scan, respectively. Conclusions: The authors conclude that the proposed dedicated CAD system for large pulmonary nodules can identify the vast majority of highly suspicious lesions in thoracic CT scans with a small number of false positives

  3. Automatic detection of erythemato-squamous diseases using k-means clustering.

    Science.gov (United States)

    Ubeyli, Elif Derya; Doğdu, Erdoğan

    2010-04-01

    A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

  4. Image processing for an automatic detection of defect signals from electromagnetic cartographies

    International Nuclear Information System (INIS)

    Benoist, B.; Marqueste, L.; Birac, C.

    1993-01-01

    As the population of nuclear power plants ages, new defects are appearing in steam reactor tubes (stress corrosion, corrosion pitting and intergranular corrosion). For more sophisticated expert appraisal of these defects, tubes can be examined by multifrequency eddy-current testing with an absolute coil (diameter value of 1 mm). A device, consisting of a push-puller mechanism and a motor-driven probe carrying this absolute coil, gives a helical movement to scan the inner surface of the tube. The signals obtained can be represented in the form of cartographies (3D representation in which the coordinates are the circumference, the length and amplitude of the X or Y component at a given frequency). The detection of defect signals by visual examination of these eddy-current cartographies is not always reproducible. The article describes an image processing procedure for the detection of defect signals which leads to better reproducibility for more safety. (authors). 3 refs., 7 figs

  5. Image processing for an automatic detection of defect signals from electromagnetic cartographies

    International Nuclear Information System (INIS)

    Benoist, B.; Marqueste, L.; Birac, C.

    1994-01-01

    As the population of nuclear power plants ages, new defects are appearing in steam generator tubes (stress corrosion, corrosion pitting and intergranular corrosion). For more sophisticated expert appraisal of these defects, tubes can be examined by multifrequency eddy-current testing with an absolute coil (diameter value of 1 mm). A device, consisting of a push-puller mechanism and a motor-driven probe carrying this absolute coil, gives a helical movement to scan the inner surface of the tube. The signals obtained can be represented in the form of cartographies (3D representation in which the coordinates are the circumference, the length and amplitude of the X or Y component at a given frequency). The detection of defect signals by visual examination of these eddy-current cartographies is not always reproducible. The article describes an image processing procedure for the detection of defect signals which leads to a better reproductibility for more safety

  6. Automatic Traffic Sign Detection and Recognition Using Colour Segmentation and Shape Identification

    Directory of Open Access Journals (Sweden)

    Horak Karel

    2016-01-01

    Full Text Available The paper describes a colour-based segmentation method of European traffic signs for detection in an image and a feature-based recognition method for categorizing them into given classes. At first, we have performed analysis of several well-known colour spaces as the RGB, HSV and YCbCr often used for segmentation purposes. The HSV colour space has been chosen as the most convenient for segmentation step and colour-based models of traffic signs representatives were created. Next, the fast radial symmetry (FRS detection method and the Harris corner detector were used to recognize circles, triangles and squares as main geometrical shapes of the traffic signs. For these purposes a new gallery of real-life images containing traffic signs has been created and analysed. Overall efficiency of our recognition method is approx. 93 % on our gallery and is usable for real-time implementations.

  7. Automatic detection of the unknown number point targets in FMICW radar signals

    Czech Academy of Sciences Publication Activity Database

    Rejfek, L.; Mošna, Zbyšek; Beran, L.; Fišer, O.; Dobrovolný, M.

    2017-01-01

    Roč. 4, č. 11 (2017), s. 116-120 ISSN 2313-626X R&D Projects: GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : FMICW radar * 2D FFT * signal filtration * taraget detection * target parameter estimation Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences http://science-gate.com/IJAAS/ Articles /2017-4-11/18%202017-4-11-pp.116-120.pdf

  8. Automatic recognition of thermographic examinations for early detection of breast cancer

    Science.gov (United States)

    Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz

    2016-09-01

    This article describes the processing and classification of thermographic examinations taken with device developed by Braster SA. The device records the surface temperature of the breast skin using the liquid crystal matrices. Images are analyzed with the use of machine learning algorithms. The result of classification is available after a few minutes and when it detects suspicious changes patient may be referred for detailed examinations.

  9. Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

    Science.gov (United States)

    Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

    2013-01-01

    Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Patients Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. Measurements and Results To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Conclusions Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients. PMID:24391903

  10. Automatic detection of whole night snoring events using non-contact microphone.

    Directory of Open Access Journals (Sweden)

    Eliran Dafna

    Full Text Available OBJECTIVE: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN: Sounds during polysomnography (PSG were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2, m/f 40/27 referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND RESULTS: To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental. A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore and specificity of 98.3% (noise as noise. CONCLUSIONS: Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  11. Automatic detection of whole night snoring events using non-contact microphone.

    Science.gov (United States)

    Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

    2013-01-01

    Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2), m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  12. System for Automatic Detection and Analysis of Targets in FMICW Radar Signal

    Czech Academy of Sciences Publication Activity Database

    Rejfek, Luboš; Mošna, Zbyšek; Urbář, Jaroslav; Koucká Knížová, Petra

    2016-01-01

    Roč. 67, č. 1 (2016), s. 36-41 ISSN 1335-3632 R&D Projects: GA ČR(CZ) GAP209/12/2440; GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : power spectral density (PSD) * FMICW radar * Doppler measurement * thresholding * false alert detection Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.483, year: 2016 http://iris.elf.stuba.sk/JEEEC/data/pdf/1_116-05.pdf

  13. Automatic detection of karstic sinkholes in seismic 3D images using circular Hough transform

    International Nuclear Information System (INIS)

    Parchkoohi, Mostafa Heydari; Farajkhah, Nasser Keshavarz; Delshad, Meysam Salimi

    2015-01-01

    More than 30% of hydrocarbon reservoirs are reported in carbonates that mostly include evidence of fractures and karstification. Generally, the detection of karstic sinkholes prognosticate good quality hydrocarbon reservoirs where looser sediments fill the holes penetrating hard limestone and the overburden pressure on infill sediments is mostly tolerated by their sturdier surrounding structure. They are also useful for the detection of erosional surfaces in seismic stratigraphic studies and imply possible relative sea level fall at the time of establishment. Karstic sinkholes are identified straightforwardly by using seismic geometric attributes (e.g. coherency, curvature) in which lateral variations are much more emphasized with respect to the original 3D seismic image. Then, seismic interpreters rely on their visual skills and experience in detecting roughly round objects in seismic attribute maps. In this paper, we introduce an image processing workflow to enhance selective edges in seismic attribute volumes stemming from karstic sinkholes and finally locate them in a high quality 3D seismic image by using circular Hough transform. Afterwards, we present a case study from an on-shore oilfield in southwest Iran, in which the proposed algorithm is applied and karstic sinkholes are traced. (paper)

  14. A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

    Directory of Open Access Journals (Sweden)

    Mehdi Alilou

    2014-03-01

    Full Text Available Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9. Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

  15. Automatic helmet-wearing detection for law enforcement using CCTV cameras

    Science.gov (United States)

    Wonghabut, P.; Kumphong, J.; Satiennam, T.; Ung-arunyawee, R.; Leelapatra, W.

    2018-04-01

    The objective of this research is to develop an application for enforcing helmet wearing using CCTV cameras. The developed application aims to help law enforcement by police, and eventually resulting in changing risk behaviours and consequently reducing the number of accidents and its severity. Conceptually, the application software implemented using C++ language and OpenCV library uses two different angle of view CCTV cameras. Video frames recorded by the wide-angle CCTV camera are used to detect motorcyclists. If any motorcyclist without helmet is found, then the zoomed (narrow-angle) CCTV is activated to capture image of the violating motorcyclist and the motorcycle license plate in real time. Captured images are managed by database implemented using MySQL for ticket issuing. The results show that the developed program is able to detect 81% of motorcyclists on various motorcycle types during daytime and night-time. The validation results reveal that the program achieves 74% accuracy in detecting the motorcyclist without helmet.

  16. Automatic detection of diseased tomato plants using thermal and stereo visible light images.

    Directory of Open Access Journals (Sweden)

    Shan-e-Ahmed Raza

    Full Text Available Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.

  17. First tests of a multi-wavelength mini-DIAL system for the automatic detection of greenhouse gases

    Science.gov (United States)

    Parracino, S.; Gelfusa, M.; Lungaroni, M.; Murari, A.; Peluso, E.; Ciparisse, J. F.; Malizia, A.; Rossi, R.; Ventura, P.; Gaudio, P.

    2017-10-01

    Considering the increase of atmospheric pollution levels in our cities, due to emissions from vehicles and domestic heating, and the growing threat of terrorism, it is necessary to develop instrumentation and gather know-how for the automatic detection and measurement of dangerous substances as quickly and far away as possible. The Multi- Wavelength DIAL, an extension of the conventional DIAL technique, is one of the most powerful remote sensing methods for the identification of multiple substances and seems to be a promising solution compared to existing alternatives. In this paper, first in-field tests of a smart and fully automated Multi-Wavelength mini-DIAL will be presented and discussed in details. The recently developed system, based on a long-wavelength infrared (IR-C) CO2 laser source, has the potential of giving an early warning, whenever something strange is found in the atmosphere, followed by identification and simultaneous concentration measurements of many chemical species, ranging from the most important Greenhouse Gases (GHG) to other harmful Volatile Organic Compounds (VOCs). Preliminary studies, regarding the fingerprint of the investigated substances, have been carried out by cross-referencing database of infrared (IR) spectra, obtained using in-cell measurements, and typical Mixing Ratios in the examined region, extrapolated from the literature. First experiments in atmosphere have been performed into a suburban and moderately-busy area of Rome. Moreover, to optimize the automatic identification of the harmful species to be recognized on the basis of in cell measurements of the absorption coefficient spectra, an advanced multivariate statistical method for classification has been developed and tested.

  18. Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses.

    Science.gov (United States)

    Caballero Gaudes, César; Petridou, Natalia; Francis, Susan T; Dryden, Ian L; Gowland, Penny A

    2013-03-01

    The ability to detect single trial responses in functional magnetic resonance imaging (fMRI) studies is essential, particularly if investigating learning or adaptation processes or unpredictable events. We recently introduced paradigm free mapping (PFM), an analysis method that detects single trial blood oxygenation level dependent (BOLD) responses without specifying prior information on the timing of the events. PFM is based on the deconvolution of the fMRI signal using a linear hemodynamic convolution model. Our previous PFM method (Caballero-Gaudes et al., 2011: Hum Brain Mapp) used the ridge regression estimator for signal deconvolution and required a baseline signal period for statistical inference. In this work, we investigate the application of sparse regression techniques in PFM. In particular, a novel PFM approach is developed using the Dantzig selector estimator, solved via an efficient homotopy procedure, along with statistical model selection criteria. Simulation results demonstrated that, using the Bayesian information criterion to select the regularization parameter, this method obtains high detection rates of the BOLD responses, comparable with a model-based analysis, but requiring no information on the timing of the events and being robust against hemodynamic response function variability. The practical operation of this sparse PFM method was assessed with single-trial fMRI data acquired at 7T, where it automatically detected all task-related events, and was an improvement on our previous PFM method, as it does not require the definition of a baseline state and amplitude thresholding and does not compromise on specificity and sensitivity. Copyright © 2011 Wiley Periodicals, Inc.

  19. Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching

    Science.gov (United States)

    Sharifi Sarabi, Mona; Villaroman, Diane; Beckett, Joel; Attiah, Mark; Marcus, Logan; Ahn, Christine; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

    2017-03-01

    Lower back pain and pathologies related to it are one of the most common results for a referral to a neurosurgical clinic in the developed and the developing world. Quantitative evaluation of these pathologies is a challenge. Image based measurements of angles/vertebral heights and disks could provide a potential quantitative biomarker for tracking and measuring these pathologies. Detection of vertebral bodies is a key element and is the focus of the current work. From the variety of medical imaging techniques, MRI and CT scans have been typically used for developing image segmentation methods. However, CT scans are known to give a large dose of x-rays, increasing cancer risk [8]. MRI can be substituted for CTs when the risk is high [8] but are difficult to obtain in smaller facilities due to cost and lack of expertise in the field [2]. X-rays provide another option with its ability to control the x-ray dosage, especially for young people, and its accessibility for smaller facilities. Hence, the ability to create quantitative biomarkers from x-ray data is especially valuable. Here, we develop a multiscale template matching, inspired by [9], to detect centers of vertebral bodies from x-ray data. The immediate application of such detection lies in developing quantitative biomarkers and in querying similar images in a database. Previously, shape similarity classification methods have been used to address this problem, but these are challenging to use in the presence of variation due to gross pathology and even subtle effects [1].

  20. Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

    Science.gov (United States)

    Sun, Shaohui

    Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a "divide-and-conquer" scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected

  1. Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

    DEFF Research Database (Denmark)

    Helle, K.B.; Müller, T.O.; Astrup, Poul

    2014-01-01

    Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often...... of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64...

  2. Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

    Science.gov (United States)

    Poczatek, J. Collin; Turck, Christoph W.; Lechene, Claude

    2012-01-01

    Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php. PMID:22347386

  3. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning.

    Science.gov (United States)

    Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

    2017-06-01

    Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

  4. Fault prevention by early stage symptoms detection for automatic vehicle transmission using pattern recognition and curve fitting

    Science.gov (United States)

    Balbin, Jessie R.; Cruz, Febus Reidj G.; Abu, Jon Ervin A.; Siño, Carlo G.; Ubaldo, Paolo E.; Zulueta, Christelle Jianne T.

    2017-06-01

    Automobiles have become essential parts of our everyday lives. It can correlate many factors that may affect a vehicle primarily those which may inconvenient or in some cases harm lives or properties. Thus, focusing on detecting an automatic transmission vehicle engine, body and other parts that cause vibration and sound may help prevent car problems using MATLAB. By using sound, vibration, and temperature sensors to detect the defects of the car and with the help of the transmitter and receiver to gather data wirelessly, it is easy to install on to the vehicle. A technique utilized from Toyota Balintawak Philippines that every car is treated as panels(a, b, c, d, and e) 'a' being from the hood until the front wheel of the car and 'e' the rear shield to the back of the car, this was applied on how to properly place the sensors so that precise data could be gathered. Data gathered would be compared to the normal graph taken from the normal status or performance of a vehicle, data that would surpass 50% of the normal graph would be considered that a problem has occurred. The system is designed to prevent car accidents by determining the current status or performance of the vehicle, also keeping people away from harm.

  5. Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia.

    Science.gov (United States)

    Longépé, Nicolas; Hajduch, Guillaume; Ardianto, Romy; Joux, Romain de; Nhunfat, Béatrice; Marzuki, Marza I; Fablet, Ronan; Hermawan, Indra; Germain, Olivier; Subki, Berny A; Farhan, Riza; Muttaqin, Ahmad Deni; Gaspar, Philippe

    2017-10-26

    The Indonesian fisheries management system is now equipped with the state-of-the-art technologies to deter and combat Illegal, Unreported and Unregulated (IUU) fishing. Since October 2014, non-cooperative fishing vessels can be detected from spaceborne Vessel Detection System (VDS) based on high resolution radar imagery, which directly benefits to coordinated patrol vessels in operation context. This study attempts to monitor the amount of illegal fishing in the Arafura Sea based on this new source of information. It is analyzed together with Vessel Monitoring System (VMS) and satellite-based Automatic Identification System (Sat-AIS) data, taking into account their own particularities. From October 2014 to March 2015, i.e. just after the establishment of a new moratorium by the Indonesian authorities, the estimated share of fishing vessels not carrying VMS, thus being illegal, ranges from 42 to 47%. One year later in January 2016, this proportion decreases and ranges from 32 to 42%. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Automatic road traffic safety management system in urban areas

    Directory of Open Access Journals (Sweden)

    Oskarbski Jacek

    2017-01-01

    Full Text Available Traffic incidents and accidents contribute to decreasing levels of transport system reliability and safety. Traffic management and emergency systems on the road, using, among others, automatic detection, video surveillance, communication technologies and institutional solutions improve the organization of the work of various departments involved in traffic and safety management. Automation of incident management helps to reduce the time of a rescue operation as well as of the normalization of the flow of traffic after completion of a rescue operation, which also affects the reduction of the risk of secondary accidents and contributes to reducing their severity. The paper presents the possibility of including city traffic departments in the process of incident management. The results of research on the automatic incident detection in cities are also presented.

  7. Lake Storage Change Automatic Detection by Multi-source Remote Sensing without Underwater Terrain Data

    Directory of Open Access Journals (Sweden)

    ZHU Changming

    2015-03-01

    Full Text Available Focusing on lake underwater terrain unknown and dynamic storage that is difficult to obtain by the traditional methods, a new method is proposed for lake dynamic storage estimation by multi-source and multi-temporal remote sensing without underwater terrain data. The details are as follows. Firstly, extraction dynamic lake boundary through steps by steps adaptive iteration water body detection algorithm from multi-temporal remote sensing imagery. And then, retrieve water level information from ICESat GLAS laser point data. Thirdly, comprehensive utilizing lake area and elevation data, the lake boundary is converted to contour of water by the water level is assigned to the lake boundary line, according to the time and water level information. Fourthly, through the contour line construction TIN (triangulated irregular network model and Kriging interpolation, it is gotten that the simulated three-dimensional lake digital elevation model. Finally, on the basis of simulated DEM, it is calculated that the dynamic lake volume, lake area distribution and water level information. The Bosten lake is selected as a case studying to verify the algorithm. The area and dynamic water storage variations of Bosten lake are detected since 2000. The results show that, the maximum error is 2.21× 108 m3, the minimum error is 0.00002× 108 m3, the average error is 0.044×108 m3, the root mean square is 0.59 and the correlation coefficient reached 0.99.

  8. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

    Directory of Open Access Journals (Sweden)

    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

  9. Age-related incidence of pulmonary embolism and additional pathologic findings detected by computed tomography pulmonary angiography

    International Nuclear Information System (INIS)

    Groth, M.; Henes, F.O.; Mayer, U.; Regier, M.; Adam, G.; Begemann, P.G.C.

    2012-01-01

    Objective: To compare the incidence of pulmonary embolism (PE) and additional pathologic findings (APF) detected by computed tomography pulmonary angiography (CTPA) according to different age-groups. Materials and methods: 1353 consecutive CTPA cases for suspected PE were retrospectively reviewed. Patients were divided into seven age groups: ≤29, 30–39, 40–49, 50–59, 60–69, 70–79 and ≥80 years. Differences between the groups were tested using Fisher's exact or chi-square test. A p-value 0.0024). Conclusion: The incidences of PE and APF detected by CTPA reveal no significant differences between various age groups.

  10. a New Mask for Automatic Building Detection from High Density Point Cloud Data and Multispectral Imagery

    Science.gov (United States)

    Awrangjeb, M.; Siddiqui, F. U.

    2017-11-01

    In complex urban and residential areas, there are buildings which are not only connected with and/or close to one another but also partially occluded by their surrounding vegetation. Moreover, there may be buildings whose roofs are made of transparent materials. In transparent buildings, there are point returns from both the ground (or materials inside the buildings) and the rooftop. These issues confuse the previously proposed building masks which are generated from either ground points or non-ground points. The normalised digital surface model (nDSM) is generated from the non-ground points and usually it is hard to find individual buildings and trees using the nDSM. In contrast, the primary building mask is produced using the ground points, thereby it misses the transparent rooftops. This paper proposes a new building mask based on the non-ground points. The dominant directions of non-ground lines extracted from the multispectral imagery are estimated. A dummy grid with the target mask resolution is rotated at each dominant direction to obtain the corresponding height values from the non-ground points. Three sub-masks are then generated from the height grid by estimating the gradient function. Two of these sub-masks capture planar surfaces whose height remain constant in along and across the dominant direction, respectively. The third sub-mask contains only the flat surfaces where the height (ideally) remains constant in all directions. All the sub-masks generated in all estimated dominant directions are combined to produce the candidate building mask. Although the application of the gradient function helps in removal of most of the vegetation, the final building mask is obtained through removal of planar vegetation, if any, and tiny isolated false candidates. Experimental results on three Australian data sets show that the proposed method can successfully remove vegetation, thereby separate buildings from occluding vegetation and detect buildings with

  11. Automatic First-Arrival Detection and Picking With Multiscale Wavelet Analysis

    Science.gov (United States)

    Zhang, H.; Thurber, C.; Rowe, C.

    2001-12-01

    Quickly detecting and accurately picking the P wave first-arrival is of great importance in locating earthquakes and characterizing velocity structure, especially in the era of large volumes of digital and real-time seismic data. The detector should be capable of finding the onset of the P-wave arrival against the background of microseismic and cultural noise. Normally, P-wave onset is characterized by a rapid change in amplitude and/or the arrival of high-frequency energy. The wavelet transform decomposes the signal at different scales, thus adaptively characterizing its components at different resolutions. Wavelet coefficients at high resolutions show the fine structure of the signal, and those at low resolution characterize its coarse features. The main features in the signal will be retained over several resolution scales and irrelevant ones will decay quickly at larger scales. We move a 30 s time window from the first sample of the earthquake data and decompose the signal in the window into 3 different resolutions with the fast wavelet transform. The border effect of the wavelet transform is compensated for by overlapping neighboring time windows by 5 s at both ends. At different resolutions, the Akaike Information Criteria (AIC) picker is used on the corresponding wavelet coefficients. If no two time picks in different resolution bands are within 0.6 s, then it is concluded that there is no P first-arrival in this window. The window is then moved forward in time until a P first-arrival is found. We test our method on regional earthquake data from Dead Sea Rift region and find that it can detect about 95% of P first-arrivals correctly. It will detect the wrong P-wave onset when the time window only includes an isolated glitch. When the detector finds the P first-arrival, the picker will determine the onset time and its uncertainty based on the features of the time picks corresponding to the different resolutions. Compared with manual picks, our picker provides

  12. Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows

    NARCIS (Netherlands)

    Schlageter-Tello, Andrés; Hertem, Van Tom; Bokkers, Eddie A.M.; Viazzi, Stefano; Bahr, Claudia; Lokhorst, Kees

    2018-01-01

    The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data

  13. Automatically detecting Himalayan Glacial Lake Outburst Floods in LANDSAT time series

    Science.gov (United States)

    Veh, Georg; Korup, Oliver; Roessner, Sigrid; Walz, Ariane

    2017-04-01

    More than 5,000 meltwater lakes currently exist in the Himalayas, and some of them have grown rapidly in past decades due to glacial retreat. This trend might raise the risk of Glacial Lake Outburst Floods (GLOFs), which have caused catastrophic damage and several hundred fatalities in historic time. Yet the growing number and size of Himalayan glacial lakes have no detectable counterpart in increasing GLOF frequency. Only 35 events are documented in detail since the 1950s, mostly in the Himalayas of Eastern Nepal and Bhutan. Observations are sparse in the far eastern and totally missing in the northwestern parts of the mountain belt. The GLOF record is prone to a censoring bias, such that mainly larger floods or flood impacts have been registered. Thus, establishing a more complete record and learning from past GLOFs is essential for hazard assessment and regional planning. To detect previously unreported GLOFs in the Himalayas, we developed an automated processing chain for generating GLOF related surface-cover time series from LANDSAT data. We downloaded more than 5,000 available LANDSAT TM, ETM+ and OLI images from 1987 to present. We trained a supervised machine-learning classifier with >4,000 randomly selected image pixels and topographic variables derived from digital topographic data (SRTM and ALOS DEMs), defining water, sediment, shadow, clouds, and ice as the five main classes. We hypothesize that GLOFs significantly decrease glacial lake area while increasing the amount of sediment cover in the channel network downstream simultaneously. Thus we excluded shadows, clouds, and lake ice from the analysis. We derived surface cover maps from the fitted model for each satellite image and compiled a pixelwise time-series stack. Customized rule sets were applied to systematically remove misclassifications and to check for a sediment fan in the flow path downstream of the former lake pixels. We verified our mapping approach on thirteen GLOFs documented in the

  14. Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images.

    Science.gov (United States)

    Suárez-Cuenca, Jorge Juan; Tahoces, Pablo G; Souto, Miguel; Lado, María J; Remy-Jardin, Martine; Remy, Jacques; Vidal, Juan José

    2009-10-01

    We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.

  15. Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

    Science.gov (United States)

    Fukuda, T.; Fukunaga, S.; Ishida, H.; Kodama, K.; Matsuo, T.; Mikado, S.; Ogawa, S.; Shibuya, H.; Sudo, J.

    2013-01-01

    Nuclear emulsion, a tracking detector with sub-micron position resolution, has played a successful role in the field of particle physics and the analysis speed has been substantially improved by the development of automated scanning systems. This paper describes a newly developed automated scanning system and its application to the analysis of nuclear fragments emitted almost isotropically in nuclear evaporation. This system is able to recognize tracks of nuclear fragments up to |tan θ| systems have an angular acceptance limited to |tan θ| system will in particular be applied in the framework of the OPERA experiment: the background in the sample of τ decay candidates due to hadronic interactions will be reduced by a better detection of the emitted nuclear fragments.

  16. Automatic visual impairment detection system for age-related eye diseases through gaze analysis.

    Science.gov (United States)

    Ai Ping Yow; Damon Wong; Huiying Liu; Hongyuan Zhu; Ivy Jing-Wen Ong; Laude, Augustinus; Tock Han Lim

    2017-07-01

    Visual impairment associated with Age-related Macular Degeneration (AMD) often results in a central scotoma which is an alteration in the central vision, leading to distortion or loss of vision. Current methods for assessing visual performance such as Amsler grid and Microperimetry are typically manual and have limitations as an indicator of visual field. In this paper, we present an automated system for detecting visual impairment through gaze tracking (AVIGA). Two types of assessments namely, Impulse Stimuli Response (ISR) test and Pursuit Stimuli Response (PSR) test were implemented in AVIGA system. A Support Vector Regression (SVR)-based approach is applied on the assessment results to differentiate the severity of visual impairment. The results show that AVIGA system is well-correlated to visual acuity test (VA) and performs better in identifying presence of visual impairments in eyes, compared to Microperimetry.

  17. Automatic detection of potentially illegal online sales of elephant ivory via data mining

    Directory of Open Access Journals (Sweden)

    Julio Hernandez-Castro

    2015-07-01

    Full Text Available In this work, we developed an automated system to detect potentially illegal elephant ivory items for sale on eBay. Two law enforcement experts, with specific knowledge of elephant ivory identification, manually classified items on sale in the Antiques section of eBay UK over an 8 week period. This set the “Gold Standard” that we aim to emulate using data-mining. We achieved close to 93% accuracy with less data than the experts, as we relied entirely on metadata, but did not employ item descriptions or associated images, thus proving the potential and generality of our approach. The reported accuracy may be improved with the addition of text mining techniques for the analysis of the item description, and by applying image classification for the detection of Schreger lines, indicative of elephant ivory. However, any solution relying on images or text description could not be employed on other wildlife illegal markets where pictures can be missing or misleading and text absent (e.g., Instagram. In our setting, we gave human experts all available information while only using minimal information for our analysis. Despite this, we succeeded at achieving a very high accuracy. This work is an important first step in speeding up the laborious, tedious and expensive task of expert discovery of illegal trade over the internet. It will also allow for faster reporting to law enforcement and better accountability. We hope this will also contribute to reducing poaching, by making this illegal trade harder and riskier for those involved.

  18. Automatic Acoustic Events Detection, Classification, and Semantic Annotation for Persistent Surveillance Applications

    Science.gov (United States)

    Alkilani, Amjad H. I.

    Acoustic surveillance and human behavior analysis represent some of the ongoing research topics in signal processing. Acoustic sensors offer a promising sensing modality, primarily because they can capture a huge amount of information from the environment. Moreover, they can be rapidly deployed and are low-cost. In the past, significant efforts have been devoted to detecting sounds of individual objects or events. However, the issue of understanding human activities based on sporadic acoustic sound events has received unequal attention in the literature and hence is not well understood. This dissertation presents an extensive literature survey on this topic and discusses existing advanced techniques for acoustic signal processing and pattern recognition. A novel theoretic framework (Acoustic Events Detection, Classification, and Annotation (AEDCA)) is proposed which accommodates sound events ontology for improved human activities recognition. Based on a generalized taxonomy, three sound categories signifying interaction of human with each other, with vehicles, and with other objects are introduced. In order to understand different type of human interactions salient sound events are preliminarily identified and classified based on trained set of data. To interlink salient events representing an ontology-based hypothesis, a Hidden Markov Model-Acoustic Activity Recognizer (HMM-AAR) is modeled to recognize spatiotemporally correlated events. Once such a connection is established, an annotation of perceived sound activity is generated. The performance of the AEDCA system was tested and measured experimentally in both indoor and outdoor environments. Appropriate confusion matrices are developed for the assessment of performance reliability, and computational efficiency of the AEDCA system. The obtained results are very promising and strongly demonstrate the AEDCA is both reliable and effective, and can be extended to future surveillance applications.

  19. A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification.

    Science.gov (United States)

    Schneider, Harald J; Kosilek, Robert P; Günther, Manuel; Roemmler, Josefine; Stalla, Günter K; Sievers, Caroline; Reincke, Martin; Schopohl, Jochen; Würtz, Rolf P

    2011-07-01

    The delay between onset of first symptoms and diagnosis of the acromegaly is 6-10 yr. Acromegaly causes typical changes of the face that might be recognized by face classification software. The objective of the study was to assess classification accuracy of acromegaly by face-classification software. This was a diagnostic study. The study was conducted in specialized care. Participants in the study included 57 patients with acromegaly (29 women, 28 men) and 60 sex- and age-matched controls. We took frontal and side photographs of the faces and grouped patients into subjects with mild, moderate, and severe facial features of acromegaly by overall impression. We then analyzed all pictures using computerized similarity analysis based on Gabor jets and geometry functions. We used the leave-one-out cross-validation method to classify subjects by the software. Additionally, all subjects were classified by visual impression by three acromegaly experts and three general internists. Classification accuracy by software, experts, and internists was measured. The software correctly classified 71.9% of patients and 91.5% of controls. Classification accuracy for patients by visual analysis was 63.2 and 42.1% by experts and general internists, respectively. Classification accuracy for controls was 80.8 and 87.0% by experts and internists, respectively. The highest differences in accuracy between software and experts and internists were present for patients with mild acromegaly. Acromegaly can be detected by computer software using photographs of the face. Classification accuracy by software is higher than by medical experts or general internists, particularly in patients with mild features of acromegaly. This is a promising tool to help detecting acromegaly.

  20. Fault Detection Coverage Quantification of Automatic Test Functions of Digital I and C System in NPPs

    International Nuclear Information System (INIS)

    Choi, Jong Gyun; Lee, Seung Jun; Hur, Seop; Lee, Young Jun; Jang, Seung Cheol

    2011-01-01

    Recently, analog instrument and control (I and C) systems in nuclear power plants (NPPs) have been replaced with digital systems for safer and more efficient operations. Digital I and C systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions in spite of the presence of faults. Each fault-tolerant technique has a different inspection period from real-time monitoring to monthly testing. The range covered by each fault-tolerant technique is also different. The digital I and C system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects have not been properly considered yet in most PSA models. Therefore, it is necessary to develop an evaluation method that can describe these features of a digital I and C system. Several issues must be considered in the fault coverage estimation of a digital I and C system, and two of them were handled in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, fault injection experiment was used to obtain the exact relations between faults and multiple barriers of fault-tolerant techniques. This experiment was applied to a bistable processor (BP) of a reactor protection system