WorldWideScience

Sample records for automatic incident detection

  1. Development of a new automatic incident detection system for freeways using a bi-classifier approach

    Energy Technology Data Exchange (ETDEWEB)

    Razavi, A.

    1998-12-31

    The development and assessment of a new automatic incident detection (AID) system for traffic management authorities was presented. The AID is designed to provide early response to traffic delays caused by traffic incidents. This newly proposed AID system makes effective use of information obtained from people travelling in the opposite direction of the traffic jam. The method was tested on a stretch of the Trans-Canada Highway and was used to develop a simulation model. A comparison of the new method with two other in-use systems showed that it is possible to reduce the detection time by about 40 per cent.

  2. Fusing moving average model and stationary wavelet decomposition for automatic incident detection: case study of Tokyo Expressway

    Directory of Open Access Journals (Sweden)

    Qinghua Liu

    2014-12-01

    Full Text Available Traffic congestion is a growing problem in urban areas all over the world. The transport sector has been in full swing event study on intelligent transportation system for automatic detection. The functionality of automatic incident detection on expressways is a primary objective of advanced traffic management system. In order to save lives and prevent secondary incidents, accurate and prompt incident detection is necessary. This paper presents a methodology that integrates moving average (MA model with stationary wavelet decomposition for automatic incident detection, in which parameters of layer coefficient are extracted from the difference between the upstream and downstream occupancy. Unlike other wavelet-based method presented before, firstly it smooths the raw data with MA model. Then it uses stationary wavelet to decompose, which can achieve accurate reconstruction of the signal, and does not shift the signal transfer coefficients. Thus, it can detect the incidents more accurately. The threshold to trigger incident alarm is also adjusted according to normal traffic condition with congestion. The methodology is validated with real data from Tokyo Expressway ultrasonic sensors. Experimental results show that it is accurate and effective, and that it can differentiate traffic accident from other condition such as recurring traffic congestion.

  3. Real-time Detection of Road Traffic Incidents

    Directory of Open Access Journals (Sweden)

    Pero Škorput

    2010-07-01

    KEY WORDS: intelligent transport system, incident management system, traffic model in the status space, theory of estimation, extended Kalman filter, automatic incident detection, decision support system

  4. Automatic Sarcasm Detection: A Survey

    OpenAIRE

    Joshi, Aditya; Bhattacharyya, Pushpak; Carman, Mark James

    2016-01-01

    Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pat...

  5. 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.

  6. Automatic detection of laughter

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2005-01-01

    In the context of detecting ‘paralinguistic events’ with the aim to make classification of the speaker’s emotional state possible, a detector was developed for one of the most obvious ‘paralinguistic events’, namely laughter. Gaussian Mixture Models were trained with Perceptual Linear Prediction fea

  7. Automatic spikes detection in seismogram

    Institute of Scientific and Technical Information of China (English)

    王海军; 靳平; 刘贵忠

    2003-01-01

    @@ Data processing for seismic network is very complex and fussy, because a lot of data is recorded in seismic network every day, which make it impossible to process these data all by manual work. Therefore, seismic data should be processed automatically to produce a initial results about events detection and location. Afterwards, these results are reviewed and modified by analyst. In automatic processing data quality checking is important. There are three main problem data thatexist in real seismic records, which include: spike, repeated data and dropouts. Spike is defined as isolated large amplitude point; the other two problem datahave the same features that amplitude of sample points are uniform in a interval. In data quality checking, the first step is to detect and statistic problem data in a data segment, if percent of problem data exceed a threshold, then the whole data segment is masked and not be processed in the later process.

  8. 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...

  9. Automatic Target Detection Using Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Ganesan L

    2004-01-01

    Full Text Available Automatic target recognition (ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. This paper presents an algorithm for detecting a specified set of target objects embedded in visual images for an ATR application. The developed algorithm employs a novel technique for automatically detecting man-made and non-man-made single, two, and multitargets from nontarget objects, located within a cluttered environment by evaluating nonoverlapping image blocks, where block-by-block comparison of wavelet cooccurrence feature is done. The results of the proposed algorithm are found to be satisfactory.

  10. 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.

  11. 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...

  12. Automatic Fall Detection using Smartphone Acceleration Sensor

    Directory of Open Access Journals (Sweden)

    Tran Tri Dang

    2016-12-01

    Full Text Available In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

  13. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

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

    2012-01-01

    of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure detection...

  14. Automatic Detection of Electric Power Troubles (ADEPT)

    Science.gov (United States)

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

    1988-01-01

    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.

  15. Line matching for automatic change detection algorithm

    Science.gov (United States)

    Dhollande, Jérôme; Monnin, David; Gond, Laetitia; Cudel, Christophe; Kohler, Sophie; Dieterlen, Alain

    2012-06-01

    During foreign operations, Improvised Explosive Devices (IEDs) are one of major threats that soldiers may unfortunately encounter along itineraries. Based on a vehicle-mounted camera, we propose an original approach by image comparison to detect signicant changes on these roads. The classic 2D-image registration techniques do not take into account parallax phenomena. The consequence is that the misregistration errors could be detected as changes. According to stereovision principles, our automatic method compares intensity proles along corresponding epipolar lines by extrema matching. An adaptive space warping compensates scale dierence in 3D-scene. When the signals are matched, the signal dierence highlights changes which are marked in current video.

  16. Automatic basal slice detection for cardiac analysis

    Science.gov (United States)

    Paknezhad, Mahsa; Marchesseau, Stephanie; Brown, Michael S.

    2016-03-01

    Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction (EF) of the left ventricle (LV). Despite research on cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, has been shown to have high inter-observer variability, with a variation of the EF by up to 8%. Therefore, an automatic way of identifying the basal slice is still required. Prior published methods operate by automatically tracking the mitral valve points from the long-axis view of the LV. These approaches assumed that the basal slice is the first short-axis slice below the mitral valve. However, guidelines published in 2013 by the society for cardiovascular magnetic resonance indicate that the basal slice is the uppermost short-axis slice with more than 50% myocardium surrounding the blood cavity. Consequently, these existing methods are at times identifying the incorrect short-axis slice. Correct identification of the basal slice under these guidelines is challenging due to the poor image quality and blood movement during image acquisition. This paper proposes an automatic tool that focuses on the two-chamber slice to find the basal slice. To this end, an active shape model is trained to automatically segment the two-chamber view for 51 samples using the leave-one-out strategy. The basal slice was detected using temporal binary profiles created for each short-axis slice from the segmented two-chamber slice. From the 51 successfully tested samples, 92% and 84% of detection results were accurate at the end-systolic and the end-diastolic phases of the cardiac cycle, respectively.

  17. Assessing facial wrinkles: automatic detection and quantification

    Science.gov (United States)

    Cula, Gabriela O.; Bargo, Paulo R.; Kollias, Nikiforos

    2009-02-01

    Nowadays, documenting the face appearance through imaging is prevalent in skin research, therefore detection and quantitative assessment of the degree of facial wrinkling is a useful tool for establishing an objective baseline and for communicating benefits to facial appearance due to cosmetic procedures or product applications. In this work, an algorithm for automatic detection of facial wrinkles is developed, based on estimating the orientation and the frequency of elongated features apparent on faces. By over-filtering the skin texture image with finely tuned oriented Gabor filters, an enhanced skin image is created. The wrinkles are detected by adaptively thresholding the enhanced image, and the degree of wrinkling is estimated based on the magnitude of the filter responses. The algorithm is tested against a clinically scored set of images of periorbital lines of different severity and we find that the proposed computational assessment correlates well with the corresponding clinical scores.

  18. Automatic Sarcasm Detection in Twitter Messages

    OpenAIRE

    Ræder, Johan Georg Cyrus Mazaher

    2016-01-01

    In the past decade, social media like Twitter have become popular and a part of everyday life for many people. Opinion mining of the thoughts and opinions they share can be of interest to, e.g., companies and organizations. The sentiment of a text can be drastically altered when figurative language such as sarcasm is used. This thesis presents a system for automatic sarcasm detection in Twitter messages. To get a better understanding of the field, state-of-the-art systems fo...

  19. Automatic Detection of Omissions in Translations

    CERN Document Server

    Melamed, I D

    1996-01-01

    ADOMIT is an algorithm for Automatic Detection of OMIssions in Translations. The algorithm relies solely on geometric analysis of bitext maps and uses no linguistic information. This property allows it to deal equally well with omissions that do not correspond to linguistic units, such as might result from word-processing mishaps. ADOMIT has proven itself by discovering many errors in a hand-constructed gold standard for evaluating bitext mapping algorithms. Quantitative evaluation on simulated omissions showed that, even with today's poor bitext mapping technology, ADOMIT is a valuable quality control tool for translators and translation bureaus.

  20. AUTOMATIC RETINAL VESSEL DETECTION AND TORTUOSITY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Temitope Mapayi

    2016-07-01

    Full Text Available As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents study on the combination of difference image and K-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed K-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

  1. 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

  2. Biological models for automatic target detection

    Science.gov (United States)

    Schachter, Bruce

    2008-04-01

    Humans are better at detecting targets in literal imagery than any known algorithm. Recent advances in modeling visual processes have resulted from f-MRI brain imaging with humans and the use of more invasive techniques with monkeys. There are four startling new discoveries. 1) The visual cortex does not simply process an incoming image. It constructs a physics based model of the image. 2) Coarse category classification and range-to-target are estimated quickly - possibly through the dorsal pathway of the visual cortex, combining rapid coarse processing of image data with expectations and goals. This data is then fed back to lower levels to resize the target and enhance the recognition process feeding forward through the ventral pathway. 3) Giant photosensitive retinal ganglion cells provide data for maintaining circadian rhythm (time-of-day) and modeling the physics of the light source. 4) Five filter types implemented by the neurons of the primary visual cortex have been determined. A computer model for automatic target detection has been developed based upon these recent discoveries. It uses an artificial neural network architecture with multiple feed-forward and feedback paths. Our implementation's efficiency derives from the observation that any 2-D filter kernel can be approximated by a sum of 2-D box functions. And, a 2-D box function easily decomposes into two 1-D box functions. Further efficiency is obtained by decomposing the largest neural filter into a high pass filter and a more sparsely sampled low pass filter.

  3. Automatic detection of aircraft emergency landing sites

    Science.gov (United States)

    Shen, Yu-Fei; Rahman, Zia-ur; Krusienski, Dean; Li, Jiang

    2011-06-01

    An automatic landing site detection algorithm is proposed for aircraft emergency landing. Emergency landing is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on the external environmental factors that can impair human vision, and on the pilot's flight experience that can vary significantly among pilots. Therefore, we propose a robust, reliable and efficient algorithm that is expected to alleviate the negative impact of these factors. We present only the detection mechanism of the proposed algorithm and assume that the image enhancement for increased visibility, and image stitching for a larger field-of-view have already been performed on the images acquired by aircraftmounted cameras. Specifically, we describe an elastic bound detection method which is designed to position the horizon. The terrain image is divided into non-overlapping blocks which are then clustered according to a "roughness" measure. Adjacent smooth blocks are merged to form potential landing sites whose dimensions are measured with principal component analysis and geometric transformations. If the dimensions of the candidate region exceed the minimum requirement for safe landing, the potential landing site is considered a safe candidate and highlighted on the human machine interface. At the end, the pilot makes the final decision by confirming one of the candidates, also considering other factors such as wind speed and wind direction, etc. Preliminary results show the feasibility of the proposed algorithm.

  4. Automatic Fringe Detection Of Dynamic Moire Patterns

    Science.gov (United States)

    Fang, Jing; Su, Xian-ji; Shi, Hong-ming

    1989-10-01

    Fringe-carrier method is used in automatic fringe-order numbering of dynamic in-plane moire patterns. In experiment both static carrier and dynamic moire patterns are recorded. The image files corresponding to instants are set up to assign fringe orders automatically. Subtracting the carrier image from the modulated ones, the moire patterns due to the dynamic deformations are restored with fringe-order variation displayed by different grey levels.

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

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, N.; Havinga, P.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

  6. 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 us

  7. 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. Secti

  8. Automatic Detection of Cyberbullying on Social Media

    OpenAIRE

    Engman, Love

    2016-01-01

    Bullying on social media is a dire problem for many youths, leading to severe health problems. In this thesis we describe the construction of a software prototype capable of automatically identifying bullying comments on the social media platform ASKfm using Natural Language Processing (NLP) and Machine Learning (ML) techniques. State of the art NLP and ML algorithms from previous research are studied and evaluated for the task of identifying bullying comments in a data set from ASKfm. The be...

  9. The Role of Attentional Resources in Automatic Detection.

    Science.gov (United States)

    1981-01-01

    short-term memory is fully occupied with an attended message serves as a basic experimental separation of the two different processing stages. Atkinson ...the letters are potential targets (Schneider and Shiffrin , 1977). It predicts that the slope of the RT vs. memory set size function should be greater...short-term memory . The "automatic attention response" described by Shiffrin and Schneider (1977) suggests that controlled and automatic detection may

  10. Automatic detection of microcalcifications with multi-fractal spectrum.

    Science.gov (United States)

    Ding, Yong; Dai, Hang; Zhang, Hang

    2014-01-01

    For improving the detection of micro-calcifications (MCs), this paper proposes an automatic detection of MC system making use of multi-fractal spectrum in digitized mammograms. The approach of automatic detection system is based on the principle that normal tissues possess certain fractal properties which change along with the presence of MCs. In this system, multi-fractal spectrum is applied to reveal such fractal properties. By quantifying the deviations of multi-fractal spectrums between normal tissues and MCs, the system can identify MCs altering the fractal properties and finally locate the position of MCs. The performance of the proposed system is compared with the leading automatic detection systems in a mammographic image database. Experimental results demonstrate that the proposed system is statistically superior to most of the compared systems and delivers a superior performance.

  11. Automatic Fiber Orientation Detection for Sewed Carbon Fibers

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Automatic production and precise positioning of carbon fiber reinforced plastics (FRP) require precise detection of the fiber orientations. This paper presents an automatic method for detecting fiber orientations of sewed carbon fibers in the production of FRP. Detection was achieved by appropriate use of regional filling, edge detection operators, autocorrelation methods, and the Hough transformation. Regional filling was used to reduce the influence of the sewed regions, autocorrelation was used to clarify the fiber directions, edge detection operators were used to extract the edge features for the fiber orientations, and the Hough transformation was used to calculate the angles. Results for two kinds of carbon fiber materials show that the method is relatively quick and precise for detecting carbon fiber orientations.

  12. Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

    Science.gov (United States)

    Pylatiuk, Christian; Sanchez, Daniela; Mikut, Ralf; Alshut, Rüdiger; Reischl, Markus; Hirth, Sofia; Rottbauer, Wolfgang; Just, Steffen

    2014-08-01

    A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

  13. Novel automatic eye detection and tracking algorithm

    Science.gov (United States)

    Ghazali, Kamarul Hawari; Jadin, Mohd Shawal; Jie, Ma; Xiao, Rui

    2015-04-01

    The eye is not only one of the most complex but also the most important sensory organ of the human body. Eye detection and eye tracking are basement and hot issue in image processing. A non-invasive eye location and eye tracking is promising for hands-off gaze-based human-computer interface, fatigue detection, instrument control by paraplegic patients and so on. For this purpose, an innovation work frame is proposed to detect and tracking eye in video sequence in this paper. The contributions of this work can be divided into two parts. The first contribution is that eye filters were trained which can detect eye location efficiently and accurately without constraints on the background and skin colour. The second contribution is that a framework of tracker based on sparse representation and LK optic tracker were built which can track eye without constraint on eye status. The experimental results demonstrate the accuracy aspects and the real-time applicability of the proposed approach.

  14. Automatic Student Plagiarism Detection: Future Perspectives

    Science.gov (United States)

    Mozgovoy, Maxim; Kakkonen, Tuomo; Cosma, Georgina

    2010-01-01

    The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to…

  15. Automatic landslide and mudflow detection method via multichannel sparse representation

    Science.gov (United States)

    Chao, Chen; Zhou, Jianjun; Hao, Zhuo; Sun, Bo; He, Jun; Ge, Fengxiang

    2015-10-01

    Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it's necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.

  16. Automatic Detection of Adenocarcinoma using Active Contours

    Directory of Open Access Journals (Sweden)

    NeelapalaAnilKumar

    2013-09-01

    Full Text Available CT scan is the one of the image representation for abdomen, where the tumour to be located and specified effectively with clarity, by the medical expert. This role can be hold by using one of the image processing techniques called segmentation. Image segmentation is the technique which isolates the image into different regions to simplify the image and identify the Tumour easily. Image segmentation has been extensively studied by various approaches. This work, focus on the one of the image segmentation technique with a new regularization term that yields an unsupervised segmentation model which identifies different Tumour locations in a given CT image. Active contours form a boundary around a particular part of the image based on an energy function. The energy function may include intensity values of pixels or gradient values. Chen-Vase method of active contour algorithm is adopted for image segmentation. The segmentation is done after properly masking of CT scan image. The cancer prone area is generalized prior to the masking of the image. Effected abdomen cancer can be identified for better analysis of medical experts using image processing MATLAB tools. This paper describes a new method to detect and extract the features in CT scan images, which shows good performance in detection of difficult features. And the developed technique makes use of major image processing methods and fundamentals to detect the cancer with minimum possible human interaction.

  17. 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...

  18. 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...

  19. Comparing different approaches for automatic pronunciation error detection

    NARCIS (Netherlands)

    Strik, Helmer; Truong, Khiet; Wet, de Febe; Cucchiarini, Catia

    2009-01-01

    One of the biggest challenges in designing computer assisted language learning (CALL) applications that provide automatic feedback on pronunciation errors consists in reliably detecting the pronunciation errors at such a detailed level that the information provided can be useful to learners. In our

  20. Automatic player detection and identification for sports entertainment applications

    NARCIS (Netherlands)

    Mahmood, Zahid; Ali, Tauseef; Khattak, Shadid; Hasan, Laiq; Khan, Samee U.

    2014-01-01

    In this paper, we develop an augmented reality sports broadcasting application for automatic detection, recognition of players during play, followed by display of personal information of players. The proposed application can be divided into four major steps. In first step, each player in the image i

  1. 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...

  2. 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....... The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images....

  3. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    Science.gov (United States)

    Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; van Huis, Jasper R.; Dijk, Judith; van Rest, Jeroen H. C.

    2014-10-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

  4. @INGVterremoti: Tweeting the Automatic Detection of Earthquakes

    Science.gov (United States)

    Casarotti, E.; Amato, A.; Comunello, F.; Lauciani, V.; Nostro, C.; Polidoro, P.

    2014-12-01

    The use of social media is emerging as a powerful tool fordisseminating trusted information about earthquakes. Since 2009, theTwitter account @INGVterremoti provides constant and timely detailsabout M2+ seismic events detected by the Italian National SeismicNetwork, directly connected with the seismologists on duty at IstitutoNazionale di Geofisica e Vulcanologia (INGV). After the 2012 seismicsequence, the account has been awarded by a national prize as the"most useful Twitter account". Currently, it updates more than 110,000followers (one the first 50 Italian Twitter accounts for number offollowers). Nevertheless, since it provides only the manual revisionof seismic parameters, the timing (approximately between 10 and 20minutes after an event) has started to be under evaluation.Undeniably, mobile internet, social network sites and Twitter in particularrequire a more rapid and "real-time" reaction.During the last 18 months, INGV tested the tweeting of the automaticdetection of M3+ earthquakes, obtaining results reliable enough to bereleased openly 1 or 2 minutes after a seismic event. During the summerof 2014, INGV, with the collaboration of CORIS (Department ofCommunication and Social Research, Sapienza University of Rome),involved the followers of @INGVterremoti and citizens, carrying out aquali-quantitative study (through in-depth interviews and a websurvey) in order to evaluate the best format to deliver suchinformation. In this presentation we will illustrate the results of the reliability test and theanalysis of the survey.

  5. Automatic Brain Tumour Detection Using Symmetry Information

    Directory of Open Access Journals (Sweden)

    Mr.Mubarak Jamadar

    2015-07-01

    Full Text Available Image segmentation is used to separate an image into several “meaningful” parts. Image segmentation is identification of homogeneous regions in the image. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for color images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar surveys for color images did not emerge. Image segmentation is a process of pixel classification. An image is segmented into subsets by assigning individual pixels to classes. It is an important step towards pattern detection and recognition. Segmentation is one of the first steps in image analysis. It refers to the process of partitioning a digital image into multiple regions (sets of pixels. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. The level of segmentation is decided by the particular characteristics of the problem being considered. Image segmentation could be further used for object matching between two images. An object of interest is specified in the first image by using the segmentation result of that image; then the specified object is matched in the second image by using the segmentation result of that image

  6. Method for automatic detection of wheezing in lung sounds

    Directory of Open Access Journals (Sweden)

    R.J. Riella

    2009-07-01

    Full Text Available The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

  7. 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.

  8. Automatic Emotional State Detection using Facial Expression Dynamic in Videos

    Directory of Open Access Journals (Sweden)

    Hongying Meng

    2014-11-01

    Full Text Available In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems.

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

    Science.gov (United States)

    Hess, Phillip; Colaninno, Robin C.

    2017-02-01

    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.

  10. Automatic adverse drug events detection using letters to the editor.

    Science.gov (United States)

    Yang, Chao; Srinivasan, Padmini; Polgreen, Philip M

    2012-01-01

    We present and test the intuition that letters to the editor in journals carry early signals of adverse drug events (ADEs). Surprisingly these letters have not yet been exploited for automatic ADE detection unlike for example, clinical records and PubMed. Part of the challenge is that it is not easy to access the full-text of letters (for the most part these do not appear in PubMed). Also letters are likely underrated in comparison with full articles. Besides demonstrating that this intuition holds we contribute techniques for post market drug surveillance. Specifically, we test an automatic approach for ADE detection from letters using off-the-shelf machine learning tools. We also involve natural language processing for feature definitions. Overall we achieve high accuracy in our experiments and our method also works well on a second new test set. Our results encourage us to further pursue this line of research.

  11. Detection of Off-normal Images for NIF Automatic Alignment

    Energy Technology Data Exchange (ETDEWEB)

    Candy, J V; Awwal, A S; McClay, W A; Ferguson, S W; Burkhart, S C

    2005-07-11

    One of the major purposes of National Ignition Facility at Lawrence Livermore National Laboratory is to accurately focus 192 high energy laser beams on a nanoscale (mm) fusion target at the precise location and time. The automatic alignment system developed for NIF is used to align the beams in order to achieve the required focusing effect. However, if a distorted image is inadvertently created by a faulty camera shutter or some other opto-mechanical malfunction, the resulting image termed ''off-normal'' must be detected and rejected before further alignment processing occurs. Thus the off-normal processor acts as a preprocessor to automatic alignment image processing. In this work, we discuss the development of an ''off-normal'' pre-processor capable of rapidly detecting the off-normal images and performing the rejection. Wide variety of off-normal images for each loop is used to develop the criterion for rejections accurately.

  12. 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.

  13. 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.

  14. Automatic Emboli Detection System for the Artificial Heart

    Science.gov (United States)

    Steifer, T.; Lewandowski, M.; Karwat, P.; Gawlikowski, M.

    In spite of the progress in material engineering and ventricular assist devices construction, thromboembolism remains the most crucial problem in mechanical heart supporting systems. Therefore, the ability to monitor the patient's blood for clot formation should be considered an important factor in development of heart supporting systems. The well-known methods for automatic embolus detection are based on the monitoring of the ultrasound Doppler signal. A working system utilizing ultrasound Doppler is being developed for the purpose of flow estimation and emboli detection in the clinical artificial heart ReligaHeart EXT. Thesystem will be based on the existing dual channel multi-gate Doppler device with RF digital processing. A specially developed clamp-on cannula probe, equipped with 2 - 4 MHz piezoceramic transducers, enables easy system setup. We present the issuesrelated to the development of automatic emboli detection via Doppler measurements. We consider several algorithms for the flow estimation and emboli detection. We discuss their efficiency and confront them with the requirements of our experimental setup. Theoretical considerations are then met with preliminary experimental findings from a) flow studies with blood mimicking fluid and b) in-vitro flow studies with animal blood. Finally, we discuss some more methodological issues - we consider several possible approaches to the problem of verification of the accuracy of the detection system.

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

    NARCIS (Netherlands)

    Dadvar, Maral; Trieschnigg, Dolf; Jong, de Franciska

    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 YouTu

  16. Automatic event detection based on artificial neural networks

    Science.gov (United States)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  17. Smart optical distance sensor for automatic welding detection

    Science.gov (United States)

    Kahl, Michael; Rinner, Stefan; Ettemeyer, Andreas

    2015-05-01

    In this paper, we describe a simple and cost-effective method and measuring device for automatic detection of welding. The sensor is to be used in automatic darkening filters (ADF) of welding helmets protecting the operator from intensive hazardous UV radiation. For reasons discussed in detail below, conventional sensor principles used in ADF are being out-dated. Here, we critically revise some alternatives and propose an approach comprising an optical distance sensor. Its underlying principle is triangulation with two pin-hole cameras. The absence of optical components such as lenses results in very low cost. At first, feasibility is tested with optical simulations. Additionally, we present measurement results that prove the practicability of our proposal.

  18. 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.

  19. Early automatic detection of Parkinson's disease based on sleep recordings

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sorensen, Helge B D; Nikolic, Miki;

    2014-01-01

    SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs...... the number of outliers during REM sleep was used as a quantitative measure of muscle activity. RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. CONCLUSION: The proposed work is considered...... during REM sleep. PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification...

  20. 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.

  1. Automatic detection and segmentation of lymph nodes from CT data.

    Science.gov (United States)

    Barbu, Adrian; Suehling, Michael; Xu, Xun; Liu, David; Zhou, S Kevin; Comaniciu, Dorin

    2012-02-01

    Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.

  2. 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).

  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 Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

    Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system.Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented.Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold.The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%.When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.

  5. 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.

  6. Automatic detection of scoliotic curves in posteroanterior radiographs.

    Science.gov (United States)

    Duong, Luc; Cheriet, Farida; Labelle, Hubert

    2010-05-01

    Spinal deformities are diagnosed using posteroanterior (PA) radiographs. Automatic detection of the spine on conventional radiographs would be of interest to quantify curve severity, would help reduce observer variability and would allow large-scale retrospective studies on radiographic databases. The goal of this paper is to present a new method for automatic detection of spinal curves from a PA radiograph. A region of interest (ROI) is first extracted according to the 2-D shape variability of the spine obtained from a set of PA radiographs of scoliotic patients. This region includes 17 bounding boxes delimiting each vertebral level from T1 to L5. An adaptive filter combining shock with complex diffusion is used to individually restore the image of each vertebral level. Then, texture descriptors of small block elements are computed and submitted for training to support vector machines (SVM). Vertebral body's locations are thereby inferred for a particular vertebral level. The classifications of block elements for all 17 SVMs are identified in the image and a voting system is introduced to cumulate correctly predicted blocks. A spline curve is then fitted through the centers of the predicted vertebral regions and compared to a manual identification using a Student t-test. A clinical validation is performed using 100 radiographs of scoliotic patients (not used for training) and the detected spinal curve is found to be statistically similar (p < 0.05) in 93% of cases to the manually identified curve.

  7. Automatic Sea Bird Detection from High Resolution Aerial Imagery

    Science.gov (United States)

    Mader, S.; Grenzdörffer, G. J.

    2016-06-01

    Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

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

    OpenAIRE

    Dadvar, Maral; Trieschnigg, Dolf; Jong, de, F.

    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 YouTube users‟ behaviour and their characteristics through expert knowledge. Based on experts‟ knowledge, the system assigns a score to the users, which represents their level of “bulliness” based on th...

  9. 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.

  10. Automatic detection of asteroids and meteoroids. A Wide Field Survey

    Science.gov (United States)

    Vereš, P.; Tóth, J.; Jedicke, R.; Tonry, J.; Denneau, L.; Wainscoat, R.; Kornoš, L.; Šilha, J.

    2014-07-01

    We propose a low-cost robotic optical survey aimed at 1-300 m Near Earth Objects (NEO) based on four state-of-the-art telescopes having extremely wide field of view. The small Near-Earth Asteroids (NEA) represent a potential risk but also easily accessible space resources for future robotic or human space in-situ exploration, or commercial activities. The survey system will be optimized for the detection of fast moving-trailed-asteroids, space debris and will provide real-time alert notifications. The expected cost of the system including 1-year development and 2-year operation is 1,000,000 EUR. The successful demonstration of the system will promote cost-effectiveicient ADAM-WFS (Automatic Detection of Asteroids and Meteoroids -- A Wide Field Survey) systems to be built around the world.

  11. Automatic Detection of Asteroids and Meteoroids - A Wide Field Survey

    CERN Document Server

    Vereš, P; Jedicke, R; Tonry, J; Denneau, L; Wainscoat, R; Kornoš, L; Šilha, J

    2014-01-01

    We propose a low-cost robotic optical survey aimed at $1-300$ m Near Earth Objects (NEO) based on four state-of-the-art telescopes having extremely wide field of view. The small Near-Earth Asteroids (NEA) represent a potential risk but also easily accessible space resources for future robotic or human space in-situ exploration, or commercial activities. The survey system will be optimized for the detection of fast moving - trailed - asteroids, space debris and will provide real-time alert notifications. The expected cost of the system including 1-year development and 2-year operation is 1,000,000 EUR. The successful demonstration of the system will promote cost-efficient ADAM-WFS (Automatic Detection of Asteroids and Meteoroids - A Wide Field Survey) systems to be built around the world.

  12. Automatic Detection of Magnetic delta in Sunspot Groups

    CERN Document Server

    Padinhatteeri, Sreejith; Bloomfield, D Shaun; Gallagher, Peter T

    2015-01-01

    Large and magnetically complex sunspot groups are known to be associated with flares. To date, the Mount Wilson scheme has been used to classify sunspot groups based on their morphological and magnetic properties. The most flare prolific class, the delta sunspot-group, is characterised by opposite polarity umbrae within a common penumbra, separated by less than 2 degrees. In this article, we present a new system, called the Solar Monitor Active Region Tracker - Delta Finder (SMART-DF), that can be used to automatically detect and classify magnetic deltas in near-realtime. Using continuum images and magnetograms from the Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO), we first estimate distances between opposite polarity umbrae. Opposite polarity pairs having distances of less that 2 degrees are then identified, and if these pairs are found to share a common penumbra, they are identified as a magnetic delta configuration. The algorithm was compared to manual delta detect...

  13. 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.

  14. Incident detection and isolation in drilling using analytical redundancy relations

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    ratio test is applied for change detection. Data from a 1400 meter horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside......Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms...... measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability...

  15. Implementation of Advanced Techniques for Automated Freeway Incident Detection

    OpenAIRE

    Abdulhai, Baher; Ritchie, Stephen G.; Iyer, Mahadevan

    1999-01-01

    A significant body of research on advanced techniques for automated freeway incident detection has been conducted at the University of California, Irvine (UCI). Such advanced pattern recognition techniques as artificial neural networks (ANNs) have been thoroughly investigated and their potential superiority to other techniques has been demonstrated. Of the investigated ANN architectures, two have shown the best potential for real-time implementation: namely, the Probabilistic Neural Network (...

  16. 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.

  17. An Automatic Eye Detection Method for Gray Intensity Facial Images

    Directory of Open Access Journals (Sweden)

    M Hassaballah

    2011-07-01

    Full Text Available Eyes are the most salient and stable features in the human face, and hence automatic extraction or detection of eyes is often considered as the most important step in many applications, such as face identification and recognition. This paper presents a method for eye detection of still grayscale images. The method is based on two facts: eye regions exhibit unpredictable local intensity, therefore entropy in eye regions is high and the center of eye (iris is too dark circle (low intensity compared to the neighboring regions. A score based on the entropy of eye and darkness of iris is used to detect eye center coordinates. Experimental results on two databases; namely, FERET with variations in views and BioID with variations in gaze directions and uncontrolled conditions show that the proposed method is robust against gaze direction, variations in views and variety of illumination. It can achieve a correct detection rate of 97.8% and 94.3% on a set containing 2500 images of FERET and BioID databases respectively. Moreover, in the cases with glasses and severe conditions, the performance is still acceptable.

  18. 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.

  19. Highway Traffic Incident Detection using High-Resolution Aerial Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Seyed M.M. Kahaki

    2011-01-01

    Full Text Available Problem statement: As vehicle population increases, Intelligent Transportation Systems (ITS become more significant and mandatory in today’s overpopulated world. Vital problems in transportation such as mobility and safety of transportation are considered more, especially in metropolitans and highways. The main road traffic monitoring aims are: the acquisition and analysis of traffic figures, such as number of vehicles, incident detection and automatic driver warning systems are developed mainly for localization and safety purposes. Approach: The objective of this investigation was to propose a strategy for road extraction and incident detection using aerial images. Real time extraction and localization of roadways in an satellite image is an emerging research field which can applied to vision-based traffic controlling and unmanned air vehicles navigation. Results: The results of the proposed incident detection algorithm show that it has good detection performance, the maximum angle of vehicles applied for incidet detection is 45 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso

  20. Automatic character detection and segmentation in natural scene images

    Institute of Scientific and Technical Information of China (English)

    ZHU Kai-hua; QI Fei-hu; JIANG Ren-jie; XU Li

    2007-01-01

    We present a robust connected-component (CC) based method for automatic detection and segmentation of text in real-scene images. This technique can be applied in robot vision, sign recognition, meeting processing and video indexing. First, a Non-Linear Niblack method (NLNiblack) is proposed to decompose the image into candidate CCs. Then, all these CCs are fed into a cascade of classifiers trained by Adaboost algorithm. Each classifier in the cascade responds to one feature of the CC. Proposed here are 12 novel features which are insensitive to noise, scale, text orientation and text language. The classifier cascade allows non-text CCs of the image to be rapidly discarded while more computation is spent on promising text-like CCs. The CCs passing through the cascade are considered as text components and are used to form the segmentation result. A prototype system was built,with experimental results proving the effectiveness and efficiency of the proposed method.

  1. Automatic probe artifact detection in MRI-guided cryoablation

    Science.gov (United States)

    Liu, Xinyang; Tuncali, Kemal; Wells, William M.; Zientara, Gary P.

    2013-03-01

    Probe or needle artifact detection in 3D scans gives an approximate location for the tools inserted, and is thus crucial in assisting many image-guided procedures. Conventional needle localization algorithms often start with cropped images, where unwanted parts of raw scans are cropped either manually or by applying pre-defined masks. In cryoablation, however, the number of probes used, the placement and direction of probe insertion, and the portions of abdomen scanned differs significantly from case to case, and probes are often constantly being adjusted during the Probe Placement Phase. These features greatly reduce the practicality of approaches based on image cropping. In this work, we present a fully Automatic Probe Artifact Detection method, APAD, that works directly on uncropped raw MRI images, taken during the Probe Placement Phase in 3Tesla MRI-guided cryoablation. The key idea of our method is to first locate an initial 2D line strip within a slice of the MR image which approximates the position and direction of the 3D probes bundle, noting that cryoprobes or biopsy needles create a signal void (black) artifact in MRI with a bright cylindrical border. With the initial 2D line, standard approaches to detect line structures such as the 3D Hough Transform can be applied to quickly detect each probe's axis. By comparing with manually labeled probes, the analysis of 5 patient treatment cases of kidney cryoablation with varying probe placements demonstrates that our algorithm combined with standard 3D line detection is an accurate and robust method to detect probe artifacts.

  2. Automatic fault detection on BIPV systems without solar irradiation data

    CERN Document Server

    Leloux, Jonathan; Luna, Alberto; Desportes, Adrien

    2014-01-01

    BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar ...

  3. Automatically detecting pain in video through facial action units.

    Science.gov (United States)

    Lucey, Patrick; Cohn, Jeffrey F; Matthews, Iain; Lucey, Simon; Sridharan, Sridha; Howlett, Jessica; Prkachin, Kenneth M

    2011-06-01

    In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.

  4. 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.

  5. Automatic Detection of Magnetic δ in Sunspot Groups

    Science.gov (United States)

    Padinhatteeri, Sreejith; Higgins, Paul A.; Bloomfield, D. Shaun; Gallagher, Peter T.

    2016-01-01

    Large and magnetically complex sunspot groups are known to be associated with flares. To date, the Mount Wilson scheme has been used to classify sunspot groups based on their morphological and magnetic properties. The most flare-prolific class, the δ sunspot group, is characterised by opposite-polarity umbrae within a common penumbra, separated by less than 2∘. In this article, we present a new system, called the Solar Monitor Active Region Tracker-Delta Finder (SMART-DF), which can be used to automatically detect and classify magnetic δs in near-realtime. Using continuum images and magnetograms from the Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO), we first estimate distances between opposite-polarity umbrae. Opposite-polarity pairs with distances of less that 2∘ are then identified, and if these pairs are found to share a common penumbra, they are identified as a magnetic δ configuration. The algorithm was compared to manual δ detections reported by the Space Weather Prediction Center (SWPC), operated by the National Oceanic and Atmospheric Administration (NOAA). SMART-DF detected 21 out of 23 active regions (ARs) that were marked as δ spots by NOAA during 2011 - 2012 (within {±} 60° longitude). SMART-DF in addition detected five ARs that were not announced as δ spots by NOAA. The near-realtime operation of SMART-DF resulted in many δs being identified in advance of NOAA's daily notification. SMART-DF will be integrated into SolarMonitor (www.solarmonitor.org) and the near-realtime information will be available to the public.

  6. AUTOMATIC TURBIDIMETRY IN DETECTING PROTEIN IN URINE AND CEREBROSPINAL FLUID

    Institute of Scientific and Technical Information of China (English)

    张建荣; 李闻捷; 徐德安

    2002-01-01

    Objective To evaluate and validate the performance of automatic turbidimetry in detecting protein in urine and cerebrospinal fluid.Methods The detection limits, reportable range of results, precision and accuracy of the method were investigated by using the Roche chemical reagent, benzethonium chloride.Results The functional sensitivity was 0.08g/L of protein, the reportable range of result was between 0.08g/L and 2.0g/L; the intra-batch coefficient of variation(CV) was 1.5% and the inter-batch CV was 2.2%, and the regression relation between new method and routine SSA method in patient sample determination was Y1 = 0.86X+0.068, r=0.972 and Y2=0.86X+0.056, r=0.980 for urine and cerebrospinal fluid respectively.Conclusion This method is simple, accurate, time saving with minimal sample volume 5~15μl, and suitalbe for clinical practice.

  7. Automatic GPRS Rainfall Detecting Set Based on P89C669

    Institute of Scientific and Technical Information of China (English)

    Yang,Lei; Wu,Kun

    2005-01-01

    A new kind of remote and automatic GPRS rainfall detecting network system is established and developed. As the main unit of the network system, automatic rainfall detecting set based on P89C669 is used to acquire rainfall information automatically. GPRS station, combined with mobile wireless communication and internet technology is used to achieve the objective of dynamically share and display the meteorological information via internet.

  8. 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

  9. Automatic Registration and Error Detection of Multiple Slices Using Landmarks

    Directory of Open Access Journals (Sweden)

    Hans Frimmel

    2001-01-01

    Full Text Available Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. Methods. Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built‐in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. Results. The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. Conclusions. The model based registration performs better than traditional simple slice‐wise registration. In the case of prostate

  10. 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...

  11. 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.

  12. Automatic detection and measurement of femur length from fetal ultrasonography

    Science.gov (United States)

    Mukherjee, Prateep; Swamy, Gokul; Gupta, Madhumita; Patil, Uday; Krishnan, Kajoli Banerjee

    2010-03-01

    Femur bone length is used in the assessment of fetal development and in the prediction of gestational age (GA). In this paper, we present a completely automated two-step method for identifying fetal femur and measuring its length from 2D ultrasound images. The detection algorithm uses a normalized score premised on the distribution of anatomical shape, size and presentation of the femur bone in clinically acceptable scans. The measurement process utilizes a polynomial curve fitting technique to determine the end-points of the bone from a 1D profile that is most distal from the transducer surface. The method has been tested with manual measurements made on 90 third trimester femur images by two radiologists. The measurements made by the experts are strongly correlated (Pearson's coefficient = 0.95). Likewise, the algorithm estimate is strongly correlated with expert measurements (Pearson's coefficient = 0.92 and 0.94). Based on GA estimates and their bounds specified in Standard Obstetric Tables, the GA predictions from automated measurements are found to be within +/-2SD of GA estimates from both manual measurements in 89/90 cases and within +/-3SD in all 90 cases. The method presented in this paper can be adapted to perform automatic measurement of other fetal limbs.

  13. 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

  14. Automatic detection of surface changes on Mars - a status report

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2016-10-01

    Orbiter missions have acquired approximately 500,000 high-resolution visible images of the Martian surface, covering an area approximately 6 times larger than the overall area of Mars. This data abundance allows the scientific community to examine the Martian surface thoroughly and potentially make exciting new discoveries. However, the increased data volume, as well as its complexity, generate problems at the data processing stages, which are mainly related to a number of unresolved issues that batch-mode planetary data processing presents. As a matter of fact, the scientific community is currently struggling to scale the common ("one-at-a-time" processing of incoming products by expert scientists) paradigm to tackle the large volumes of input data. Moreover, expert scientists are more or less forced to use complex software in order to extract input information for their research from raw data, even though they are not data scientists themselves.Our work within the STFC and EU FP7 i-Mars projects aims at developing automated software that will process all of the acquired data, leaving domain expert planetary scientists to focus on their final analysis and interpretation. Moreover, after completing the development of a fully automated pipeline that processes automatically the co-registration of high-resolution NASA images to ESA/DLR HRSC baseline, our main goal has shifted to the automated detection of surface changes on Mars. In particular, we are developing a pipeline that uses as an input multi-instrument image pairs, which are processed by an automated pipeline, in order to identify changes that are correlated with Mars surface dynamic phenomena. The pipeline has currently been tested in anger on 8,000 co-registered images and by the time of DPS/EPSC we expect to have processed many tens of thousands of image pairs, producing a set of change detection results, a subset of which will be shown in the presentation.The research leading to these results has received

  15. Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

    Science.gov (United States)

    Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

    2013-09-01

    Diabetic retinopathy (DR) is one of the major complications of diabetes that changes the blood vessels of the retina and distorts patient vision that finally in high stages can lead to blindness. Micro-aneurysms (MAs) are one of the first pathologies associated with DR. The number and the location of MAs are very important in grading of DR. Early diagnosis of micro-aneurysms (MAs) can reduce the incidence of blindness. As MAs are tiny area of blood protruding from vessels in the retina and their size is about 25 to 100 microns, automatic detection of these tiny lesions is still challenging. MAs occurring in the macula can lead to visual loss. Also the position of a lesion such as MAs relative to the macula is a useful feature for analysis and classification of different stages of DR. Because MAs are more distinguishable in fundus fluorescin angiography (FFA) compared to color fundus images, we introduce a new method based on curvelet transform and morphological operations for MAs detection in FFA images. As vessels and MAs are the bright parts of FFA image, firstly extracted vessels by curvelet transform are removed from image. Then morphological operations are applied on resulted image for detecting MAs.

  16. 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.

  17. 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 ...

  18. [The application of atomic absorption spectrometry in automatic transmission fault detection].

    Science.gov (United States)

    Chen, Li-dan; Chen, Kai-kao

    2012-01-01

    The authors studied the innovative applications of atomic absorption spectrometry in the automatic transmission fault detection. After the authors have determined Fe, Cu and Cr contents in the five groups of Audi A6 main metal in automatic transmission fluid whose travel course is respectively 10-15 thousand kilometers, 20-26 thousand kilometers, 32-38 thousand kilometers, 43-49 thousand kilometers, and 52-58 thousand kilometers by atomic absorption spectrometry, the authors founded the database of primary metal content in the Audi A6 different mileage automatic transmission fluid (ATF). The research discovered that the main metal content in the automatic transmission fluid increased with the vehicles mileage and its normal metal content level in the automatic transmission fluid is between the two trend lines. The authors determined the main metal content of automatic transmission fluid which had faulty symptoms and compared it with its database value. Those can not only judge the wear condition of the automatic transmission which had faulty symptoms but also help the automobile detection and maintenance personnel to diagnose automatic transmission failure reasons without disintegration. This reduced automobile maintenance costs, and improved the quality of automobile maintenance.

  19. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis.

    NARCIS (Netherlands)

    Vos, P.C.; Barentsz, J.O.; Karssemeijer, N.; Huisman, H.J.

    2012-01-01

    In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performe

  20. Automatic Change Detection of Geo-spatial Data from Imagery

    Institute of Scientific and Technical Information of China (English)

    LI Deren; SUI Haigang; XIAO Ping

    2003-01-01

    The problems and difficulty of current change detection tech-niques are presented. Then, according to whether image registration is donebefore change detection algorithms,the authors classify the change detection into two categories:the change de-tection after image registration and the change detection simultaneous with image registration. For the former,four topics including the change detection between new image and old im-age, the change detection between newimage and old map, the change detection between new image/old image andold map, and the change detection between new multi-source images and old map/image are introduced. For the latter, three categories, I. E. Thechange detection between old DEM,DOM and new non-rectification image,the change detection between oldDLG, DRG and new non-rectificationimage, and the 3D change detectionbetween old 4D products and new multi overlapped photos, are discussed.

  1. Automatic Detection of Ringworm using Local Binary Pattern (LBP)

    CERN Document Server

    Kundu, Srimanta; Nasipuri, Mita

    2011-01-01

    In this paper we present a novel approach for automatic recognition of ring worm skin disease based on LBP (Local Binary Pattern) feature extracted from the affected skin images. The proposed method is evaluated by extensive experiments on the skin images collected from internet. The dataset is tested using three different classifiers i.e. Bayesian, MLP and SVM. Experimental results show that the proposed methodology efficiently discriminates between a ring worm skin and a normal skin. It is a low cost technique and does not require any special imaging devices.

  2. A hybrid model using logistic regression and wavelet transformation to detect traffic incidents

    Directory of Open Access Journals (Sweden)

    Shaurya Agarwal

    2016-07-01

    Full Text Available This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection.

  3. Automatic detection of articulation disorders in children with cleft lip and palate.

    Science.gov (United States)

    Maier, Andreas; Hönig, Florian; Bocklet, Tobias; Nöth, Elmar; Stelzle, Florian; Nkenke, Emeka; Schuster, Maria

    2009-11-01

    Speech of children with cleft lip and palate (CLP) is sometimes still disordered even after adequate surgical and nonsurgical therapies. Such speech shows complex articulation disorders, which are usually assessed perceptually, consuming time and manpower. Hence, there is a need for an easy to apply and reliable automatic method. To create a reference for an automatic system, speech data of 58 children with CLP were assessed perceptually by experienced speech therapists for characteristic phonetic disorders at the phoneme level. The first part of the article aims to detect such characteristics by a semiautomatic procedure and the second to evaluate a fully automatic, thus simple, procedure. The methods are based on a combination of speech processing algorithms. The semiautomatic method achieves moderate to good agreement (kappa approximately 0.6) for the detection of all phonetic disorders. On a speaker level, significant correlations between the perceptual evaluation and the automatic system of 0.89 are obtained. The fully automatic system yields a correlation on the speaker level of 0.81 to the perceptual evaluation. This correlation is in the range of the inter-rater correlation of the listeners. The automatic speech evaluation is able to detect phonetic disorders at an experts'level without any additional human postprocessing.

  4. 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

  5. An open-set detection evaluation methodology for automatic emotion recognition in speech

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification d

  6. 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

  7. 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...

  8. 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.

  9. Automatic gender detection of dream reports: A promising approach.

    Science.gov (United States)

    Wong, Christina; Amini, Reza; De Koninck, Joseph

    2016-08-01

    A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ=0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (κ=0.492), both of which were higher than chance of 50% (κ=0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction.

  10. Yuma proving grounds automatic UXO detection using biomorphic robots

    Energy Technology Data Exchange (ETDEWEB)

    Tilden, M.W.

    1996-07-01

    The current variety and dispersion of Unexploded Ordnance (UXO) is a daunting technological problem for current sensory and extraction techniques. The bottom line is that the only way to insure a live UXO has been found and removed is to step on it. As this is an upsetting proposition for biological organisms like animals, farmers, or Yuma field personnel, this paper details a non-biological approach to developing inexpensive, automatic machines that will find, tag, and may eventually remove UXO from a variety of terrains by several proposed methods. The Yuma proving grounds (Arizona) has been pelted with bombs, mines, missiles, and shells since the 1940s. The idea of automatic machines that can clean up after such testing is an old one but as yet unrealized because of the daunting cost, power and complexity requirements of capable robot mechanisms. A researcher at Los Alamos National Laboratory has invented and developed a new variety of living robots that are solar powered, legged, autonomous, adaptive to massive damage, and very inexpensive. This technology, called Nervous Networks (Nv), allows for the creation of capable walking mechanisms (known as Biomorphic robots, or Biomechs for short) that rather than work from task principles use instead a survival-based design philosophy. This allows Nv based machines to continue doing work even after multiple limbs and sensors have been removed or damaged, and to dynamically negotiate complex terrains as an emergent property of their operation (fighting to proceed, as it were). They are not programmed, and indeed, the twelve transistor Nv controller keeps their electronic cost well below that of most pocket radios. It is suspected that advanced forms of these machines in huge numbers may be an interesting, capable solution to the problem of general and specific UXO identification, tagging, and removal.

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

    Energy Technology Data Exchange (ETDEWEB)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R [Laboratorio de Ingenieria en Rehabilitacion e Investigaciones Neuromusculares y Sensoriales (Argentina); Facultad de Ingenieria, Universidad Nacional de Entre Rios, Ruta 11 - Km 10, Oro Verde, Entre Rios (Argentina)

    2007-11-15

    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.

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

    Science.gov (United States)

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

    2007-11-01

    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.

  13. Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

    Science.gov (United States)

    Doshi, Trushali; Soraghan, John; Grose, Derek; MacKenzie, Kenneth; Petropoulakis, Lykourgos

    2015-03-01

    Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78+/-0.04 and average root mean square error of 1.82+/-0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.

  14. 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.

  15. Automatic Detection of Steel Ball's Surface Flaws Based on Image Processing

    Institute of Scientific and Technical Information of China (English)

    YU Zheng-lin; TAN Wei; YANG Dong-lin; CAO Guo-hua

    2007-01-01

    A new method to detect steel ball's surface flaws is presented based on computer techniques of image processing and pattern recognition. The steel ball's surface flaws is the primary factor causing bearing failure. The high efficient and precision detections for the surface flaws of steel ball can be conducted by the presented method, including spot, abrasion, burn, scratch and crack, etc. The design of main components of the detecting system is described in detail including automatic feeding mechanism, automatic spreading mechanism of steel ball's surface, optical system of microscope, image acquisition system, image processing system. The whole automatic system is controlled by an industrial control computer, which can carry out the recognition of flaws of steel ball's surface effectively.

  16. 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.

  17. Improving Automation Routines for Automatic Heating Load Detection in Buildings

    Directory of Open Access Journals (Sweden)

    Stephen Timlin

    2012-11-01

    Full Text Available Energy managers use weather compensation data and heating system cut off routines to reduce heating energy consumption in buildings and improve user comfort. These routines are traditionally based on the calculation of an estimated building load that is inferred from the external dry bulb temperature at any point in time. While this method does reduce heating energy consumption and accidental overheating, it can be inaccurate under some weather conditions and therefore has limited effectiveness. There remains considerable scope to improve on the accuracy and relevance of the traditional method by expanding the calculations used to include a larger range of environmental metrics. It is proposed that weather compensation and automatic shut off routines that are commonly used could be improved notably with little additional cost by the inclusion of additional weather metrics. This paper examines the theoretical relationship between various external metrics and building heating loads. Results of the application of an advanced routine to a recently constructed building are examined, and estimates are made of the potential savings that can be achieved through the use of the routines proposed.

  18. Automatic colonic lesion detection and tracking in endoscopic videos

    Science.gov (United States)

    Li, Wenjing; Gustafsson, Ulf; A-Rahim, Yoursif

    2011-03-01

    The biology of colorectal cancer offers an opportunity for both early detection and prevention. Compared with other imaging modalities, optical colonoscopy is the procedure of choice for simultaneous detection and removal of colonic polyps. Computer assisted screening makes it possible to assist physicians and potentially improve the accuracy of the diagnostic decision during the exam. This paper presents an unsupervised method to detect and track colonic lesions in endoscopic videos. The aim of the lesion screening and tracking is to facilitate detection of polyps and abnormal mucosa in real time as the physician is performing the procedure. For colonic lesion detection, the conventional marker controlled watershed based segmentation is used to segment the colonic lesions, followed by an adaptive ellipse fitting strategy to further validate the shape. For colonic lesion tracking, a mean shift tracker with background modeling is used to track the target region from the detection phase. The approach has been tested on colonoscopy videos acquired during regular colonoscopic procedures and demonstrated promising results.

  19. Automatic Defect Detection in X-Ray Images Using Image Data Fusion

    Institute of Scientific and Technical Information of China (English)

    TIAN Yuan; DU Dong; CAI Guorui; WANG Li; ZHANG Hua

    2006-01-01

    Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape, or size. Thus, the method is more robust and practical than the current methods using only one method.

  20. Automatic line detection in document images using recursive morphological transforms

    Science.gov (United States)

    Kong, Bin; Chen, Su S.; Haralick, Robert M.; Phillips, Ihsin T.

    1995-03-01

    In this paper, we describe a system that detects lines of various types, e.g., solid lines and dotted lines, on document images. The main techniques are based on the recursive morphological transforms, namely the recursive opening and closing transforms. The advantages of the transforms are that they can perform binary opening and closing with any sized structuring element simultaneously in constant time per pixel, and that they offer a solution to morphological image analysis problems where the sizes of the structuring elements have to be determined after the examination of the image itself. The system is evaluated on about 1,200 totally ground-truthed IRS tax form images of different qualities. The line detection output is compared with a set of hand-drawn groundtruth lines. The statistics like the number and rate of correct detection, miss detection, and false alarm are calculated. The performance of 32 algorithms for solid line detection are compared to find the best one. The optimal algorithm tuning parameter settings could be estimated on the fly using a regression tree.

  1. Automatic Tree-Crown Detection in Challenging Scenarios

    Science.gov (United States)

    Bulatov, Dimitri; Wayand, Isabell; Schilling, Hendrik

    2016-06-01

    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.

  2. 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 hyp...

  3. Automatic Detection and Decoding of Photogrammetric Coded Targets

    OpenAIRE

    Wijenayake, Udaya; Choi, Sung-In; Park, Soon-Yong

    2016-01-01

    Close-range Photogrammetry is widely used in many industries because of the cost effectiveness and efficiency of the technique. In this research, we introduce an automated coded target detection method which can be used to enhance the efficiency of the Photogrammetry.

  4. Incidence of prostate cancer in Lithuania after introduction of the Early Prostate Cancer Detection Programme.

    Science.gov (United States)

    Smailyte, G; Aleknaviciene, B

    2012-12-01

    In Lithuania, prostate-specific antigen (PSA) testing is offered to healthy asymptomatic men as a screening test in the population-based Early Prostate Cancer Detection Programme (EPCDP). The aim of this study was to analyse the incidence of prostate cancer before and after introduction of the EPCDP in Lithuania. Prostate cancer incidence and mortality data from the Lithuanian Cancer Registry were analysed for the period 1990-2008. Age-specific incidence and mortality data were adjusted to the European Standard Population. There have been extraordinary changes in the incidence of prostate cancer in Lithuania following introduction of the EPCDP, and there is strong evidence that these changes are the result of increased detection rates, especially in men of screening age. Further observation of changes in prostate cancer incidence and mortality in Lithuania may help to determine the extent to which PSA testing at the population level influences incidence and mortality in the general population.

  5. 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.

  6. 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.

  7. 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 handling of symptoms of downhole drilling contingencies at an early stage are therefore crucial for the reliability and safety of MPD operations. In this paper we describe a method for early detection and localization of such incidents, based on a fit for purpose model of the downhole pressure hydraulics......, these incidents have been success- fully detected at an early stage, when detection by a human operator observing the measured data is almost impossible. The developed detection and localization method can be included as a diagnosis tool in a drilling system with MPD or conventional drilling...

  8. 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.

  9. Automatic failure detection of serial products using novelty filter

    Directory of Open Access Journals (Sweden)

    Márcia Helena Veleda Moita

    2013-08-01

    Full Text Available The present paper focus on a computer tool that seeks the failure detection of serial products. This paper begins with a brief description about the quality on manufacturing process and points out the relevance of product inspection for detecting fails aiming the product's quality. For such inspection to be accomplished, was used Digital Image Processing Techniques and Artificial Intelligence. This research were done in a mobile phone industry located in the Manaus Industrial Polo - PIM. The tool's methodology localize the possible defect areas and uses a process in which, from a data base composed by pattern images, the Novelty Filter Technique is able to discern regions of  failure through what it was instructed.

  10. 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.

  11. Automatic detection of anatomical landmarks in uterine cervix images.

    Science.gov (United States)

    Greenspan, Hayit; Gordon, Shiri; Zimmerman, Gali; Lotenberg, Shelly; Jeronimo, Jose; Antani, Sameer; Long, Rodney

    2009-03-01

    The work focuses on a unique medical repository of digital cervicographic images ("Cervigrams") collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the "os"), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.

  12. Alcohol Detection and Automatic Drunken Drive Avoiding System

    Directory of Open Access Journals (Sweden)

    Prof. P. H. Kulkarni

    2014-04-01

    Full Text Available The main aim of this project is to design an embedded system for implementing a efficient alcohol detection system that will be useful to avoid accidents. There are many different types of accidents which occur in daily life. Accidents may cause due to many reasons it may be due to brake fail. Most often accidents occur due to over drunken person. Though there are laws to punish drunken drivers they cannot be fully implemented. Because traffic police cannot stand on every road to check each and every car driver whether he/she has drunk or not. This can be a major reason for accidents. So there is a need for a effective system to check drunken drivers. Therefore in order to avoid these accidents we have implemented a prototype project. In our project, Initially we check whether the person has drunken or not by using the MQ3 GAS sensor. In this system, sensor circuit is used to detect whether the alcohol was consumed by driver or not. To this end, we have designed such a system that when alcohol concentration is detected then car will be stopped and the related information will go to nearby location through GSM. This project is based on EMBEDDED C programming using AVR-AT mega 16 microcontroller.

  13. Automatic Deformation Detection for Aircraft Engine Disk Inspection

    Directory of Open Access Journals (Sweden)

    Dirk Padfield

    2007-08-01

    Full Text Available Computer vision algorithms are seeing increased use in industrial inspection applications. Here, we present an “Aid to Visual” system that can detect post deformations of less than 0.005 inches in jet engine high pressure turbine disks. We create a gold-standard reference post from the posts of sample turbine disks and then use registration, edge detection, and curve-similarity algorithms to identify unacceptable post deformations. We address the challenges associated with adapting academic algorithms for use in functioning inspection systems. We present novel solutions to deal with practical issues such as accuracy, speed, robustness, and ease of use. We also present a novel, highly-efficient sub-pixel contour matching algorithm and demonstrate the effectiveness of using sub-pixel distance calculation. We demonstrate overall error rates less than 1% on over 2400 images of posts. We have integrated our algorithms into the commercial LabVIEW software running on the Aid To Visual workstation. Our algorithms will enable plant-factory inspectors to identify minute post deformations that were previously difficult to detect.

  14. Mastitis therapy and control - Automatic on-line detection of abnormal milk.

    NARCIS (Netherlands)

    Hogeveen, H.

    2011-01-01

    Automated online detection of mastitis and abnormal milk is an important subject in the dairy industry, especially because of the introduction of automatic milking systems and the growing farm sizes with consequently less labor available per cow. Demands for performance, which is expressed as sensit

  15. Automatic colonic polyp detection using multi-objective evolutionary techniques

    Science.gov (United States)

    Li, Jiang; Huang, Adam; Yao, Jianhua; Bitter, Ingmar; Petrick, Nicholas; Summers, Ronald M.; Pickhardt, Perry J.; Choi, J. Richard

    2006-03-01

    Colonic polyps appear like elliptical protrusions on the inner wall of the colon. Curvature based features for colonic polyp detection have proved to be successful in several computer-aided diagnostic CT colonography (CTC) systems. Some simple thresholds are set for those features for creating initial polyp candidates, sophisticated classification scheme are then applied on these polyp candidates to reduce false positives. There are two objective functions, the number of missed polyps and false positive rate, that need to be minimized when setting those thresholds. These two objectives conflict and it is usually difficult to optimize them both by a gradient search. In this paper, we utilized a multiobjective evolutionary method, the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize those thresholds. SPEA2 incorporates the concept of Pareto dominance and applies genetic techniques to evolve individual solutions to the Pareto front. The SPEA2 algorithm was applied to colon CT images from 27 patients each having a prone and a supine scan. There are 40 colonoscopically confirmed polyps resulting in 72 positive detections in CTC reading. The results obtained by SPEA2 were compared with those obtained by our old system, where an appropriate value was set for each of those thresholds by a histogram examination method. If we keep the sensitivity the same as that of our old system, the SPEA2 algorithm reduced false positive rate by 76.4% from average false positive 55.6 to 13.3 per data set. If the false positive rate is kept the same for both systems, SPEA2 increased the sensitivity by 13.1% from 53 to 61 among 72 ground truth detections.

  16. Automatic Damage Detection for Sensitive Cultural Heritage Sites

    Science.gov (United States)

    Cerra, D.; Tian, J.; Lysandrou, V.; Plank, S.

    2016-06-01

    The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State (IS) have received wide coverage from the media worldwide. Earth Observation data is an important tool to assess these damages in such non-accessible areas: If a fast response is desired, automated image processing techniques would be needed to speed up the analysis. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites in the observed images.

  17. 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.

  18. Automatic oil spill detection on quad polarimetric UAVSAR imagery

    Science.gov (United States)

    Rahnemoonfar, Maryam; Dhakal, Shanti

    2016-05-01

    Oil spill on the water bodies has adverse effects on coastal and marine ecology. Oil spill contingency planning is of utmost importance in order to plan for mitigation and remediation of the oceanic oil spill. Remote sensing technologies are used for monitoring the oil spills on the ocean and coastal region. Airborne and satellite sensors such as optical, infrared, ultraviolet, radar and microwave sensors are available for remote surveillance of the ocean. Synthetic Aperture Radar (SAR) is used most extensively for oil-spill monitoring because of its capability to operate during day/night and cloud-cover condition. This study detects the possible oil spill regions on fully polarimetric Uninhabited Aerial Vehicle - Synthetic Aperture Radar (UAVSAR) images. The UAVSAR image is decomposed using Cloude-Pottier polarimetric decomposition technique to obtain entropy and alpha parameters. In addition, other polarimetric features such as co-polar correlation and degree of polarization are obtained for the UAVSAR images. These features are used to with fuzzy logic based classification to detect oil spill on the SAR images. The experimental results show the effectiveness of the proposed method.

  19. Automatic detection and morphological delineation of bacteriophages in electron microscopy images.

    Science.gov (United States)

    Gelzinis, A; Verikas, A; Vaiciukynas, E; Bacauskiene, M; Sulcius, S; Simoliunas, E; Staniulis, J; Paskauskas, R

    2015-09-01

    Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host.

  20. Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals.

    Science.gov (United States)

    O'Regan, Simon; Faul, Stephen; Marnane, William

    2013-07-01

    Contamination of EEG signals by artefacts arising from head movements has been a serious obstacle in the deployment of automatic neurological event detection systems in ambulatory EEG. In this paper, we present work on categorizing these head-movement artefacts as one distinct class and on using support vector machines to automatically detect their presence. The use of additional physical signals in detecting head-movement artefacts is also investigated by means of support vector machines classifiers implemented with gyroscope waveforms. Finally, the combination of features extracted from EEG and gyroscope signals is explored in order to design an algorithm which incorporates both physical and physiological signals in accurately detecting artefacts arising from head-movements.

  1. 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 bo

  2. Unattended vehicle detection for automatic traffic light control

    Science.gov (United States)

    Abdel Hady, Aya Salama; Moustafa, Mohamed

    2013-12-01

    Machine vision based traffic light control depends mainly on measuring traffic statistics at cross roads. Most of the previous studies have not taken unattended vehicles into consideration when calculating either the traffic density or the traffic flow. In this paper, we propose incorporating unattended vehicles into a new metric for measuring the traffic congestion. In addition to the vehicle motion analysis, opening the driver's side door is an important indicator that this vehicle is going to be unattended. Therefore, we focus in this paper on presenting how to detect this event for stationary vehicles from a live camera or a video feed. Through a set of experiments, we have found out that a Scale Invariant Feature Transform (SIFT) feature-descriptor with a Support Vector Machines (SVM) classifier was able to successfully classify open-door vehicles from closed-door ones in 96.7% of our test dataset.

  3. Exploring the Thermal Limits of IR-Based Automatic Whale Detection

    Science.gov (United States)

    2014-09-30

    surface temperature) and species . To this end, we developed a ship-based thermal imaging system for automated marine mammal detection, consisting...exclusion zone. Marine mammal observers usually scan the ship’s environs for whales using binoculars or the naked eye. Sightings mostly rely on spotting a...overcome these difficulties and to develop a reliable, automatic whale detection system for the full range of oceanic environmental conditions (wind, sea

  4. 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...

  5. AUTOMATIC URBAN ILLEGAL BUILDING DETECTION USING MULTI-TEMPORAL SATELLITE IMAGES AND GEOSPATIAL INFORMATION SYSTEMS

    Directory of Open Access Journals (Sweden)

    N. Khalili Moghadam

    2015-12-01

    Full Text Available With the unprecedented growth of urban population and urban development, we are faced with the growing trend of illegal building (IB construction. Field visit, as the currently used method of IB detection, is time and man power consuming, in addition to its high cost. Therefore, an automatic IB detection is required. Acquiring multi-temporal satellite images and using image processing techniques for automatic change detection is one of the optimum methods which can be used in IB monitoring. In this research an automatic method of IB detection has been proposed. Two-temporal panchromatic satellite images of IRS-P5 of the study area in a part of Tehran, the city map and an updated spatial database of existing buildings were used to detect the suspected IBs. In the pre-processing step, the images were geometrically and radiometrically corrected. In the next step, the changed pixels were detected using K-means clustering technique because of its quickness and less user’s intervention required. Then, all the changed pixels of each building were identified and the change percentage of each building with the standard threshold of changes was compared to detect the buildings which are under construction. Finally, the IBs were detected by checking the municipality database. The unmatched constructed buildings with municipal database will be field checked to identify the IBs. The results show that out of 343 buildings appeared in the images; only 19 buildings were detected as under construction and three of them as unlicensed buildings. Furthermore, the overall accuracies of 83%, 79% and 75% were obtained for K-means change detection, detection of under construction buildings and IBs detection, respectively.

  6. 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...... to investigate the performance of paroxysm detection. Using only a single scalp electroencephalogram channel from 20 patients with a total of 125 paroxysms >2 seconds, 97.2% of paroxysms could be detected with no false detections. This result leads us to recommend further investigations of tiny, one......-channel electroencephalogram systems in an ambulatory setting....

  7. Automatic detection of microaneurysms using microstructure and wavelet methods

    Indian Academy of Sciences (India)

    M Tamilarasi; K Duraiswamy

    2015-06-01

    Retinal microaneurysm is one of the earliest signs in diabetic retinopathy diagnosis. This paper has developed an approach to automate the detection of microaneurysms using wavelet-based Gaussian mixture model and microstructure texture feature extraction. First, the green channel of the colour retinal fundus image is extracted and pre-processed using various enhancement techniques such as bottom-hat filtering and gamma correction. Second, microstructures are extracted as Gaussian profiles in wavelet domain using the three-level generative model. Multiscale Gaussian kernels are obtained and histogram-based features are extracted from the best kernel. Using the Markov Chain Monte Carlo method, microaneurysms are classified using the optimal feature set. The proposed approach is experimented with DIARETDB0 and DIARETDB1 datasets using a classifier based on multi-layer perceptron procedure. For DIARETDB0 dataset, the proposed algorithm obtains the results with a sensitivity of 98.32 and specificity of 97.59. In the case of DIARETDB1 dataset, the sensitivity and specificity of 98.91 and 97.65 have been achieved. The accuracies achieved by the proposed algorithm are 97.86 and 98.33 using DIARETDB0 and DIARETDB1 datasets respectively. Based on ground truth validation, good segmentation results are achieved when compared to existing algorithms such as local relative entropy-based thresholding, inverse adaptive surface thresholding, inverse segmentation method, and dark object segmentation.

  8. SRM improved X-rays examination: automatic detection in sight

    Science.gov (United States)

    Lamarque, P.

    2004-04-01

    Reliability needs in Ariane V program require a high level of nondestructive testing (NDT) specially for solid rocket motors (SRM). In this way, 100% X-rays control is done on SRM segment with a real time radioscopic technique. The development of Ariane V showed the interest to use this technique for large sample examination. But it showed also that numerical radiograph has a poor contrast and a low signal to noise ratio images. In these conditions, inspectors work is not easy and twice result reading is needed. So in order to increase reliability, production rate and cost reduction, a development has been done supported by CNES and SNPE. To achieve these goals we have worked on X-ray detector and image processing tools improvement with LETI laboratory (CEA Grenoble). In this paper we present work and results achieved in the two domains. X-ray detector has been changed by a high energy specific design and numerical CCD camera has been tested. Image processing method, based on tomosynthesis algorithms, has been developed. It allows pictures summation while the sample is moving and gives three-dimensional information. To speed up the processing time of these tasks, optimized algorithms on dedicated machine have been set up. The implementation of those improvements has been done on UPG production site and some results are shown. Thanks to these improvements on X-rays quality pictures the implementation of detection attended by computer is considered.

  9. 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.

  10. Automatic detection of exonic splicing enhancers (ESEs using SVMs

    Directory of Open Access Journals (Sweden)

    Suhai Sándor

    2008-09-01

    Full Text Available Abstract Background Exonic splicing enhancers (ESEs activate nearby splice sites and promote the inclusion (vs. exclusion of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins. Results The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters. Conclusion The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

  11. 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.

  12. Automatic Change Detection for Road Networks from Images Based on GIS

    Institute of Scientific and Technical Information of China (English)

    SUI Haigang; LI Deren; GONG Jianya

    2003-01-01

    Up to now, detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images. Forfeature-level change detection (FL-CD), a so-called buffer detection algo-rithm is proposed to detect changes offeatures. Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.

  13. Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system.

    Science.gov (United States)

    Gao, Xinting; Wong, Damon Wing Kee; Aryaputera, Aloysius Wishnu; Sun, Ying; Cheng, Ching-Yu; Cheung, Carol; Wong, Tien Yin

    2012-01-01

    In this paper, we present a new method to detect pterygiums using cornea images. Due to the similarity of appearances and spatial locations between pterygiums and cortical cataracts, pterygiums are often falsely detected as cortical cataracts on retroillumination images by a computer-aided grading system. The proposed method can be used to filter out the pterygium which improves the accuracy of cortical cataract grading system. This work has three major contributions. First, we propose a new pupil segmentation method for visible wavelength images. Second, an automatic detection method of pterygiums is proposed. Third, we develop an enhanced compute-aided cortical cataract grading system that excludes pterygiums. The proposed method is tested using clinical data and the experimental results demonstrate that the proposed method can improve the existing automatic cortical cataract grading system.

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

    Science.gov (United States)

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-06-25

    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.

  15. Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

    Directory of Open Access Journals (Sweden)

    Juan Ródenas

    2015-09-01

    Full Text Available This work introduces for the first time the application of wavelet entropy (WE to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF, automatically from the electrocardiogram (ECG. Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient’s rhythm identification (sinus rhythm or AF. Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmia.

  16. Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    Downhole abnormal incidents during oil and gas drilling causes costly delays, any may also potentially lead to dangerous scenarios. Dierent incidents willcause changes to dierent parts of the physics of the process. Estimating thechanges in physical parameters, and correlating these with changes...... expectedfrom various defects, can be used to diagnose faults while in development.This paper shows how estimated friction parameters and ow rates can de-tect and isolate the type of incident, as well as isolating the position of adefect. Estimates are shown to be subjected to non......-Gaussian,t-distributednoise, and a dedicated multivariate statistical change detection approach isused that detects and isolates faults by detecting simultaneous changes inestimated parameters and ow rates. The properties of the multivariate di-agnosis method are analyzed, and it is shown how detection and false alarmprobabilities...

  17. Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    S.M.M. Kahaki

    2012-09-01

    Full Text Available One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

  18. Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Sayed M.M. Kahaki

    2013-09-01

    Full Text Available One  of  the  most  important  methods  to  solve  traffic  congestion  is  to detect the incident state of a roadway. This paper describes the development of a method  for  road  traffic  monitoring  aimed  at  the  acquisition  and  analysis  of remote  sensing  imagery.  We  propose  a  strategy  for  road  extraction,  vehicle detection  and incident detection  from remote sensing imagery using techniques based on neural networks, Radon transform  for angle detection and traffic-flow measurements.  Traffic-bottleneck  detection  is  another  method  that  is  proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection  method had a detection rate of 87.5%.

  19. Automatic detection of reservoir influx in conventional drilling, managed pressure drilling and dual gradient drilling

    OpenAIRE

    Pettersen, Sigmund

    2012-01-01

    Reservoir influxes, or kicks, are well control incidents with the potential of severe consequences to health, safety and the environment, as well as economics. Although the main focus will always be to prevent such incidents from happening, drilling crew will also need to be able to spot reservoir influx as quickly as possible. This thesis presents a method for automated detection of reservoir influx or losses based on simulations of the surface circulation system. Theoretical background...

  20. Automatic laser beam alignment using blob detection for an environment monitoring spectroscopy

    Science.gov (United States)

    Khidir, Jarjees; Chen, Youhua; Anderson, Gary

    2013-05-01

    This paper describes a fully automated system to align an infra-red laser beam with a small retro-reflector over a wide range of distances. The component development and test were especially used for an open-path spectrometer gas detection system. Using blob detection under OpenCV library, an automatic alignment algorithm was designed to achieve fast and accurate target detection in a complex background environment. Test results are presented to show that the proposed algorithm has been successfully applied to various target distances and environment conditions.

  1. 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.

  2. Research on automatic spraying of single-walled carbon nanotubes and detection of spraying effects

    Directory of Open Access Journals (Sweden)

    Jianwen Zhao

    2014-04-01

    Full Text Available Single-walled carbon nanotubes (SWNTs have been introduced as compliant electrodes for dielectric elastomers (DEs due to fault tolerance. To acquire a better electrostrictive strain and longer lifetime, it is essential to obtain a certain and uniform width of the SWNT electrode. To ensure uniform width manually, a small flux and longer time are necessary. Moreover, it is difficult to control the width of the electrode for the randomness of manual spraying. Therefore, a new type of automatic spraying process is presented in this paper. The width and homogeneity of the electrode can be easily controlled by certain parameters of the process. Two methods for detecting the homogeneity of the electrode are introduced in this paper: Measurement of surface resistance and luminosity. The coefficient of variation (CV values detected by the two methods are virtually equal and less than 8%, which shows the feasibility of the detection method and homogeneity of automatic spraying. The speed of automatic spraying is 102 mm2/s, which is higher than that of manual spraying. The spraying process and the method used to detect homogeneity in this paper provide a reference for the relevant processes.

  3. Detection of protein microarrays by oblique-incidence reflectivity difference technique

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Biological microarrays with different proteins and different protein concentrations are detected without external labeling by an oblique-incidence reflectivity difference (OIRD) technique. The initial experiment results reveal that the intensities of OIRD signals can distinguish the different proteins and concentrations of protein. The OIRD technique promises feasible applications to life sciences for label-free and high-throughput detection.

  4. An automatic 3D CAD model errors detection method of aircraft structural part for NC machining

    Directory of Open Access Journals (Sweden)

    Bo Huang

    2015-10-01

    Full Text Available Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

  5. Automatic Open Space Area Extraction and Change Detection from High Resolution Urban Satellite Images

    CERN Document Server

    Kodge, B G

    2011-01-01

    In this paper, we study efficient and reliable automatic extraction algorithm to find out the open space area from the high resolution urban satellite imagery, and to detect changes from the extracted open space area during the period 2003, 2006 and 2008. This automatic extraction and change detection algorithm uses some filters, segmentation and grouping that are applied on satellite images. The resultant images may be used to calculate the total available open space area and the built up area. It may also be used to compare the difference between present and past open space area using historical urban satellite images of that same projection, which is an important geo spatial data management application.

  6. 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.

  7. 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.

  8. Automatic Detection and Processing of Attributes Inconsistency for Fuzzy Ontologies Merging

    Directory of Open Access Journals (Sweden)

    Yonghong Luo

    2013-11-01

    Full Text Available Semantic fusion of multiple data sources and semantic interoperability between heterogeneous systems in distributed environment can be implemented through integrating multiple fuzzy local ontologies. However, ontology merging is one of the valid ways for ontology integration. In order to solve the problem of attributes inconsistency for concept mapping in fuzzy ontology merging system, we present an automatic detection algorithm of inconsistency for the range, number and membership grade of attributes between mapping concepts, and adopt corresponding processing strategy during the fuzzy ontologies merging according to the different types of attributes inconsistency. Experiment results show that with regard to merging accuracy, the fuzzy ontology merging system in which the automatic detection algorithm and processing strategy of attributes inconsistency is embedded is better than those traditional ontology merging systems like GLUE, PROMPT and Chimaera.    

  9. Automatic dental arch detection and panoramic image synthesis from CT images.

    Science.gov (United States)

    Sa-Ing, Vera; Wangkaoom, Kongyot; Thongvigitmanee, Saowapak S

    2013-01-01

    Due to accurate 3D information, computed tomography (CT), especially cone-beam CT or dental CT, has been widely used for diagnosis and treatment planning in dentistry. Axial images acquired from both medical and dental CT scanners can generate synthetic panoramic images similar to typical 2D panoramic radiographs. However, the conventional way to reconstruct the simulated panoramic images is to manually draw the dental arch on axial images. In this paper, we propose a new fast algorithm for automatic detection of the dental arch. Once the dental arch is computed, a series of synthetic panoramic images as well as a ray-sum panoramic image can be automatically generated. We have tested the proposed algorithm on 120 CT axial images and all of them can provide the decent estimate of the dental arch. The results show that our proposed algorithm can mostly detect the correct dental arch.

  10. Automatic Earthquake Detection and Location by Waveform coherency in Alentejo (South Portugal) Using CatchPy

    Science.gov (United States)

    Custodio, S.; Matos, C.; Grigoli, F.; Cesca, S.; Heimann, S.; Rio, I.

    2015-12-01

    Seismic data processing is currently undergoing a step change, benefitting from high-volume datasets and advanced computer power. In the last decade, a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered mainland Portugal. This outstanding regional coverage currently enables the computation of a high-resolution image of the seismicity of Portugal, which contributes to fitting together the pieces of the regional seismo-tectonic puzzle. Although traditional manual inspections are valuable to refine automatic results they are impracticable with the big data volumes now available. When conducted alone they are also less objective since the criteria is defined by the analyst. In this work we present CatchPy, a scanning algorithm to detect earthquakes in continuous datasets. Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e. lowering the detection threshold). CatchPY is designed to produce an event database that could be easily located using existing location codes (e.g.: Grigoli et al. 2013, 2014). We use CatchPy to perform automatic detection and location of earthquakes that occurred in Alentejo region (South Portugal), taking advantage of a dense seismic network deployed in the region for two years during the DOCTAR experiment. Results show that our automatic procedure is particularly suitable for small aperture networks. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event location is performed by waveform coherence analysis, scanning different hypocentral coordinates

  11. GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos

    Science.gov (United States)

    Yi, Steven; Jiao, Heng; Meng, Fan; Leighton, Jonathon A.; Shabana, Pasha; Rentz, Lauri

    2014-03-01

    In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

  12. 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.

  13. Robust automatic detection and removal of fiducial projections in fluoroscopy images: an integrated solution.

    Science.gov (United States)

    Zhang, Xuan; Zheng, Guoyan

    2008-01-01

    Automatic detection and removal of fiducial projections in fluoroscopy images is an essential prerequisite for fluoroscopy-based navigation and image-based 3D-2D registration. This paper presents an integrated solution to fulfill this task. A custom-designed calibration cage with a two-plane pattern of fiducials is utilized in our solution. The cage is attached to the C-arm image intensifier and the projections of the fiducials are automatically detected and removed by an on-line algorithm consisting of following 6 steps: image binarization, connected-component labeling, region classification, adaptive template matching, shape analysis, and fiducial projection removal. A similarity measure which is proposed previously for image-based 3D-2D registration is employed in the adaptive template matching to improve the accuracy of the detection. Shape analysis based on the geometrical constraints satisfied by those fiducials in the calibration cage is used to further improve the robustness of the detection. An image inpainting technique based on the fast marching method for level set applications is used to remove the detected fiducial projections. Our in vitro experiments show on average 4 seconds execution time on a Pentium IV machine, a zero false-detection rate, a miss-detection rate of 1.6+/-2.3%, and a sub-pixel localization error.

  14. Automatic layout feature extraction for lithography hotspot detection based on deep neural network

    Science.gov (United States)

    Matsunawa, Tetsuaki; Nojima, Shigeki; Kotani, Toshiya

    2016-03-01

    Lithography hotspot detection in the physical verification phase is one of the most important techniques in today's optical lithography based manufacturing process. Although lithography simulation based hotspot detection is widely used, it is also known to be time-consuming. To detect hotspots in a short runtime, several machine learning based methods have been proposed. However, it is difficult to realize highly accurate detection without an increase in false alarms because an appropriate layout feature is undefined. This paper proposes a new method to automatically extract a proper layout feature from a given layout for improvement in detection performance of machine learning based methods. Experimental results show that using a deep neural network can achieve better performance than other frameworks using manually selected layout features and detection algorithms, such as conventional logistic regression or artificial neural network.

  15. RESEARCH ON EXPERT SYSTEM OF FAULT DETECTION AND DIAGNOSING FOR PNEUMATIC SYSTEM OF AUTOMATIC PRODUCTION LINE

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Fault detection and diagnosis for pneumatic system of automatic production line are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosis instrument are designed. The mathematical model of various pneumatic faults and experimental device are built. In the end, some experiments are done, which shows that the expert system using fuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.

  16. Automatic detection of mitochondria from electron microscope tomography images: a curve fitting approach

    Science.gov (United States)

    Tasel, Serdar F.; Hassanpour, Reza; Mumcuoglu, Erkan U.; Perkins, Guy C.; Martone, Maryann

    2014-03-01

    Mitochondria are sub-cellular components which are mainly responsible for synthesis of adenosine tri-phosphate (ATP) and involved in the regulation of several cellular activities such as apoptosis. The relation between some common diseases of aging and morphological structure of mitochondria is gaining strength by an increasing number of studies. Electron microscope tomography (EMT) provides high-resolution images of the 3D structure and internal arrangement of mitochondria. Studies that aim to reveal the correlation between mitochondrial structure and its function require the aid of special software tools for manual segmentation of mitochondria from EMT images. Automated detection and segmentation of mitochondria is a challenging problem due to the variety of mitochondrial structures, the presence of noise, artifacts and other sub-cellular structures. Segmentation methods reported in the literature require human interaction to initialize the algorithms. In our previous study, we focused on 2D detection and segmentation of mitochondria using an ellipse detection method. In this study, we propose a new approach for automatic detection of mitochondria from EMT images. First, a preprocessing step was applied in order to reduce the effect of nonmitochondrial sub-cellular structures. Then, a curve fitting approach was presented using a Hessian-based ridge detector to extract membrane-like structures and a curve-growing scheme. Finally, an automatic algorithm was employed to detect mitochondria which are represented by a subset of the detected curves. The results show that the proposed method is more robust in detection of mitochondria in consecutive EMT slices as compared with our previous automatic method.

  17. Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

    Science.gov (United States)

    Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

    2012-09-01

    The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

  18. Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

    Science.gov (United States)

    Marghany, Maged

    2016-10-01

    In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

  19. Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Marghany Maged

    2016-10-01

    Full Text Available In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiver-operating characteristic (ROC curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

  20. Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

    Directory of Open Access Journals (Sweden)

    Alemi Farrokh

    2010-01-01

    Full Text Available Abstract Background The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level. Methods Influenza-Like Illness (ILI was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes. Results Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters. Conclusions Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual

  1. Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs

    Science.gov (United States)

    Išgum, Ivana; de Vos, Bob D.; Wolterink, Jelmer M.; Dey, Damini; Berman, Daniel S.; Rubeaux, Mathieu; Leiner, Tim; Slomka, Piotr J.

    2016-03-01

    CT attenuation correction (CTAC) images acquired with PET/CT visualize coronary artery calcium (CAC) and enable CAC quantification. CAC scores acquired with CTAC have been suggested as a marker of cardiovascular disease (CVD). In this work, an algorithm previously developed for automatic CAC scoring in dedicated cardiac CT was applied to automatic CAC detection in CTAC. The study included 134 consecutive patients undergoing 82-Rb PET/CT. Low-dose rest CTAC scans were acquired (100 kV, 11 mAs, 1.4mm×1.4mm×3mm voxel size). An experienced observer defined the reference standard with the clinically used intensity level threshold for calcium identification (130 HU). Five scans were removed from analysis due to artifacts. The algorithm extracted potential CAC by intensity-based thresholding and 3D connected component labeling. Each candidate was described by location, size, shape and intensity features. An ensemble of extremely randomized decision trees was used to identify CAC. The data set was randomly divided into training and test sets. Automatically identified CAC was quantified using volume and Agatston scores. In 33 test scans, the system detected on average 469mm3/730mm3 (64%) of CAC with 36mm3 false positive volume per scan. The intraclass correlation coefficient for volume scores was 0.84. Each patient was assigned to one of four CVD risk categories based on the Agatston score (0-10, 11-100, 101-400, Cohen's linearly weighted κ0.82). Automatic detection of CVD risk based on CAC scoring in rest CTAC images is feasible. This may enable large scale studies evaluating clinical value of CAC scoring in CTAC data.

  2. Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering.

    Science.gov (United States)

    Mualla, Firas; Scholl, Simon; Sommerfeldt, Bjorn; Maier, Andreas; Hornegger, Joachim

    2013-12-01

    We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.

  3. 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....

  4. Comparing seismic tomographic images from automatically- and manually-detected arrival times

    Science.gov (United States)

    Spallarossa, Daniele; Scafidi, Davide; Turino, Chiara; Ferretti, Gabriele; Viganò, Alfio

    2013-04-01

    In this work we compare local earthquake tomographic images obtained using arrival times detected by an automatic picking procedure and by an expert seismologist. For this purpose we select a reference dataset composed of 476 earthquakes occurred in the Trentino region (north-eastern Italy) in the period 1994-2007. Local magnitudes are comprised between 0.8 and 5.3. Original recordings are mainly from the Provincia Autonoma di Trento (PAT), and from other networks operating in the surrounding areas (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - INOGS; Istituto Nazionale di Geofisica e Vulcanologia - INGV; others available via the European Integrated Data Archive). The automatic picking of P and S phases is performed through a picker engine based on the Akaike information criterion (AIC). In particular, the proposed automatic phase picker includes: (i) envelope calculation, (ii) band-pass filtering, (iii) Akaike information criterion (AIC) detector for both P- and S-arrivals, (iv) checking for impulsive arrivals, (v) evaluation of expected S onset on the basis of a preliminary location derived from the P-arrival times, and (vi) quality assessment. Simultaneously, careful manual inspection by expert seismologists is applied to the same waveform dataset, to obtain manually-repicked phase readings. Both automatic and manual procedures generate a comparable amount of readings (about 6000 P- and 5000 S-phases). These data are used for the determination of two similar 3-D propagation models for the Trentino region, applying the SIMULPS code. In order to quantitatively estimate the difference of these two models we measure their discrepancies in terms of velocity at all grid points. The small differences observed among tomographic results allow us to demonstrate that the automatic picking engine adopted in this test can be used for reprocessing large amount of seismic recordings with the aim of perform a local tomographic study with an accuracy

  5. 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.)

  6. Automatic Threshold Determination for a Local Approach of Change Detection in Long-Term Signal Recordings

    Directory of Open Access Journals (Sweden)

    Khalil Mohamad

    2007-01-01

    Full Text Available CUSUM (cumulative sum is a well-known method that can be used to detect changes in a signal when the parameters of this signal are known. This paper presents an adaptation of the CUSUM-based change detection algorithms to long-term signal recordings where the various hypotheses contained in the signal are unknown. The starting point of the work was the dynamic cumulative sum (DCS algorithm, previously developed for application to long-term electromyography (EMG recordings. DCS has been improved in two ways. The first was a new procedure to estimate the distribution parameters to ensure the respect of the detectability property. The second was the definition of two separate, automatically determined thresholds. One of them (lower threshold acted to stop the estimation process, the other one (upper threshold was applied to the detection function. The automatic determination of the thresholds was based on the Kullback-Leibler distance which gives information about the distance between the detected segments (events. Tests on simulated data demonstrated the efficiency of these improvements of the DCS algorithm.

  7. Learning-based automatic detection of severe coronary stenoses in CT angiographies

    Science.gov (United States)

    Melki, Imen; Cardon, Cyril; Gogin, Nicolas; Talbot, Hugues; Najman, Laurent

    2014-03-01

    3D cardiac computed tomography angiography (CCTA) is becoming a standard routine for non-invasive heart diseases diagnosis. Thanks to its high negative predictive value, CCTA is increasingly used to decide whether or not the patient should be considered for invasive angiography. However, an accurate assessment of cardiac lesions using this modality is still a time consuming task and needs a high degree of clinical expertise. Thus, providing automatic tool to assist clinicians during the diagnosis task is highly desirable. In this work, we propose a fully automatic approach for accurate severe cardiac stenoses detection. Our algorithm uses the Random Forest classi cation to detect stenotic areas. First, the classi er is trained on 18 CT cardiac exams with CTA reference standard. Then, then classi cation result is used to detect severe stenoses (with a narrowing degree higher than 50%) in a 30 cardiac CT exam database. Features that best captures the di erent stenoses con guration are extracted along the vessel centerlines at di erent scales. To ensure the accuracy against the vessel direction and scale changes, we extract features inside cylindrical patterns with variable directions and radii. Thus, we make sure that the ROIs contains only the vessel walls. The algorithm is evaluated using the Rotterdam Coronary Artery Stenoses Detection and Quantication Evaluation Framework. The evaluation is performed using reference standard quanti cations obtained from quantitative coronary angiography (QCA) and consensus reading of CTA. The obtained results show that we can reliably detect severe stenosis with a sensitivity of 64%.

  8. Automatic pronunciation error detection in non-native speech: the case of vowel errors in Dutch.

    Science.gov (United States)

    van Doremalen, Joost; Cucchiarini, Catia; Strik, Helmer

    2013-08-01

    This research is aimed at analyzing and improving automatic pronunciation error detection in a second language. Dutch vowels spoken by adult non-native learners of Dutch are used as a test case. A first study on Dutch pronunciation by L2 learners with different L1s revealed that vowel pronunciation errors are relatively frequent and often concern subtle acoustic differences between the realization and the target sound. In a second study automatic pronunciation error detection experiments were conducted to compare existing measures to a metric that takes account of the error patterns observed to capture relevant acoustic differences. The results of the two studies do indeed show that error patterns bear information that can be usefully employed in weighted automatic measures of pronunciation quality. In addition, it appears that combining such a weighted metric with existing measures improves the equal error rate by 6.1 percentage points from 0.297, for the Goodness of Pronunciation (GOP) algorithm, to 0.236.

  9. Automatic detection and segmentation of stems of potted tomato plant using Kinect

    Science.gov (United States)

    Fu, Daichang; Xu, Lihong; Li, Dawei; Xin, Longjiao

    2014-04-01

    The automatic segmentation and recognition of greenhouse crop is an important aspect in digitized facility agriculture. Crop stems are closely related with the growth of the crop. Meanwhile, they are also an important physiological trait to identify the species of plants. For these reasons, this paper focuses on the digitization process to collect and analysis stems of greenhouse plants (tomatoes). An algorithm for automatic stem detection and extraction is proposed, based on a cheap and effective stereo vision system—Kinect. In order to demonstrate the usefulness and the potential applicability of our algorithm, a virtual tomato plant, whose stems are rendered by segmented stem texture samples, is reconstructed on OpenGL graphic platform.

  10. Automatic face detection and tracking based on Adaboost with camshift algorithm

    Science.gov (United States)

    Lin, Hui; Long, JianFeng

    2011-10-01

    With the development of information technology, video surveillance is widely used in security monitoring and identity recognition. For most of pure face tracking algorithms are hard to specify the initial location and scale of face automatically, this paper proposes a fast and robust method to detect and track face by combining adaboost with camshift algorithm. At first, the location and scale of face is specified by adaboost algorithm based on Haar-like features and it will be conveyed to the initial search window automatically. Then, we apply camshift algorithm to track face. The experimental results based on OpenCV software yield good results, even in some special circumstances, such as light changing and face rapid movement. Besides, by drawing out the tracking trajectory of face movement, some abnormal behavior events can be analyzed.

  11. 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.

  12. vMMN for schematic faces: automatic detection of change in emotional expression

    Directory of Open Access Journals (Sweden)

    Kairi eKreegipuu

    2013-10-01

    Full Text Available Our brain is able to automatically detect changes in sensory stimulation, including in vision. A large variety of changes of features in stimulation elicit a deviance-reflecting ERP component known as the mismatch negativity (MMN. The present study has three main goals: (1 to register vMMN using a rapidly presented stream of schematic faces (neutral, happy, angry; adapted from Öhman et al., 2001; (2 to compare elicited vMMNs to angry and happy schematic faces in two different paradigms, in a traditional oddball design with frequent standard and rare target and deviant stimuli (12.5% each and in an version of an optimal multi-feature paradigm with several deviant stimuli (altogether 37.5% in the stimulus block; (3 to compare vMMNs to subjective ratings of valence, arousal and attention capture for happy and angry schematic faces, i.e., to estimate the effect of affective value of stimuli on their automatic detection. Eleven observers (19-32 years, 6 women took part in both experiments, an oddball and optimum paradigm. Stimuli were rapidly presented schematic faces and an object with face-features that served as the target stimulus to be detected by a button-press. Results show that a vMMN-type response at posterior sites was equally elicited in both experiments. Post-experimental reports confirmed that the angry face attracted more automatic attention than the happy face but the difference did not emerge directly at the ERP level. Thus, when interested in studying change detection in facial expressions we encourage the use of the optimum (multi-feature design in order to save time and other experimental resources.

  13. Texture Analysis and Modified Level Set Method for Automatic Detection of Bone Boundaries in Hand Radiographs

    Directory of Open Access Journals (Sweden)

    Syaiful Anam

    2014-10-01

    Full Text Available Rheumatoid Arthritis (RA is a chronic inflammatory joint disease characterized by a distinctive pattern of bone and joint destruction. To give an RA diagnosis, hand bone radiographs are taken and analyzed. A hand bone radiograph analysis starts with the bone boundary detection. It is however an extremely exhausting and time consuming task for radiologists. An automatic bone boundary detection in hand radiographs is thus strongly required. Garcia et al. have proposed a method for automatic bone boundary detection in hand radiographs by using an adaptive snake method, but it doesn’t work for those affected by RA. The level set method has advantages over the snake method. It however often leads to either a complete breakdown or a premature termination of the curve evolution process, resulting in unsatisfactory results. For those reasons, we propose a modified level set method for detecting bone boundaries in hand radiographs affected by RA. Texture analysis is also applied for distinguishing the hand bones and other areas. Evaluating the experiments using a particular set of hand bone radiographs, the effectiveness of the proposed method has been proved.

  14. 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.

  15. Detecting cognitive impairment by eye movement analysis using automatic classification algorithms.

    Science.gov (United States)

    Lagun, Dmitry; Manzanares, Cecelia; Zola, Stuart M; Buffalo, Elizabeth A; Agichtein, Eugene

    2011-09-30

    The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer's Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.

  16. An improved automatic computer aided tube detection and labeling system on chest radiographs

    Science.gov (United States)

    Ramakrishna, Bharath; Brown, Matthew; Goldin, Jonathan; Cagnon, Christopher; Enzmann, Dieter

    2012-03-01

    Tubes like Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed the patient and drain contents of the stomach are very commonly used in Intensive Care Units (ICU). The placement of these tubes is critical for their proper functioning and improper tube placement can even be fatal. Bedside chest radiographs are considered the quickest and safest method to check the placement of these tubes. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for tube detection on bedside chest radiographs. The core of the CAD system is the randomized algorithm which selects tubes based on their average repeatability from seed points. The CAD algorithm is designed as a 5 stage process: Preprocessing (removing borders, histogram equalization, anisotropic filtering), Anatomy Segmentation (to identify neck, esophagus, abdomen ROI's), Seed Generation, Region Growing and Tube Selection. The preliminary evaluation was carried out on 64 cases. The prototype CAD system was able to detect ET tubes with a True Positive Rate of 0.93 and False Positive Rate of 0.02/image and NG tubes with a True Positive Rate of 0.84 and False Positive Rate of 0.02/image respectively. The results from the prototype system show that it is feasible to automatically detect both tubes on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

  17. Label-free detection of hybridization of oligonucleotides by oblique-incidence reflectivity difference method

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The microarrays of 20-base oligonucleotide with different concentrations are detected before and after hybridization by the oblique-incidence reflectivity difference (OI-RD) method. The experimental results prove that OI-RD is a label-free method which can not only distinguish the concentration difference of oligonucleotides before and after the hybridization but also detect the hybridization of short oligonucleotides. At present the OI-RD method can detect 0.39 μmol/L 20-base oligonucleotide or less. These results suggest that the OI-RD method is a promising and potential technique for label-free detection of biological microarrays.

  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. Do tests devised to detect recent HIV-1 infection provide reliable estimates of incidence in Africa?

    Science.gov (United States)

    Sakarovitch, Charlotte; Rouet, Francois; Murphy, Gary; Minga, Albert K; Alioum, Ahmadou; Dabis, Francois; Costagliola, Dominique; Salamon, Roger; Parry, John V; Barin, Francis

    2007-05-01

    The objective of this study was to assess the performance of 4 biologic tests designed to detect recent HIV-1 infections in estimating incidence in West Africa (BED, Vironostika, Avidity, and IDE-V3). These tests were assessed on a panel of 135 samples from 79 HIV-1-positive regular blood donors from Abidjan, Côte d'Ivoire, whose date of seroconversion was known (Agence Nationale de Recherches sur le SIDA et les Hépatites Virales 1220 cohort). The 135 samples included 26 from recently infected patients (180 days), and 15 from patients with clinical AIDS. The performance of each assay in estimating HIV incidence was assessed through simulations. The modified commercial assays gave the best results for sensitivity (100% for both), and the IDE-V3 technique gave the best result for specificity (96.3%). In a context like Abidjan, with a 10% HIV-1 prevalence associated with a 1% annual incidence, the estimated test-specific annual incidence rates would be 1.2% (IDE-V3), 5.5% (Vironostika), 6.2% (BED), and 11.2% (Avidity). Most of the specimens falsely classified as incident cases were from patients infected for >180 days but <1 year. The authors conclude that none of the 4 methods could currently be used to estimate HIV-1 incidence routinely in Côte d'Ivoire but that further adaptations might enhance their accuracy.

  20. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    Science.gov (United States)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  1. 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.

  2. Automatic Detection Of Electrocardiogram ST Segment: Application In Ischemic Disease Diagnosis

    Directory of Open Access Journals (Sweden)

    Duck Hee Lee

    2013-03-01

    Full Text Available The analysis of electrocardiograph (ECG signal provides important clinical information for heart disease diagnosis. The ECG signal consists of the P, QRS complex, and T-wave. These waves correspond to the fields induced by specific electric phenomenon on the cardiac surface. Among them, the detection of ischemia can be achieved by analysis the ST segment. Ischemia is one of the most serious and prevalent heart diseases. In this paper, the European database was used for evaluation of automatic detection of the ST segment. The method comprises several steps; ECG signal loading from database, signal preprocessing, detection of QRS complex and R-peak, ST segment, and other relation parameter measurement. The developed application displays the results of the analysis.

  3. Automatic detection and classification of obstacles with applications in autonomous mobile robots

    Science.gov (United States)

    Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

    2016-04-01

    Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

  4. MAXIMUM A POSTERIORI-BASED AUTOMATIC TARGET DETECTION IN SAR IMAGES

    Institute of Scientific and Technical Information of China (English)

    Wang Yimin; An Jinwen

    2005-01-01

    The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture distribution to approximate and estimate multi-modal histogram of SAR image. Then, based on the principle of MAP, when a priori probability is both unknown and learned respectively, the sample pixels are classified into different classes c = {target,shadow, background}. Last, it compares the results of two different target detections. Simulation results preferably indicate that the presented algorithm is fast and robust, with the learned a priori probability, an approach to target detection is reliable and promising.

  5. 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.

  6. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    Science.gov (United States)

    Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

    2014-03-01

    Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

  7. Automatic detection of the belt-like region in an image with variational PDE model

    Institute of Scientific and Technical Information of China (English)

    Shoutao Li; Xiaomao Li; Yandong Tang

    2007-01-01

    In this paper, we propose a novel method to automatically detect the belt-like object, such as highway,river, etc., in a given image based on Mumford-Shah function and the evolution of two phase curves. The method can automatically detect two curves that are the boundaries of the belt-like object. In fact, this is a partition problem and we model it as an energy minimization of a Mumford-Shah function based minimal partition problem like active contour model. With Eulerian formulation the partial differential equations (PDEs) of curve evolution are given and the two curves will stop on the desired boundary. The stop term does not depend on the gradient of the image and the initial curves can be anywhere in the image. We also give a numerical algorithm using finite differences and present various experimental results. Compared with other methods, our method can directly detect the boundaries of belt-like object as two continuous curves, even if the image is very noisy.

  8. Automatic Detection and Vulnerability Analysis of Areas Endangered by Heavy Rain

    Science.gov (United States)

    Krauß, Thomas; Fischer, Peter

    2016-08-01

    In this paper we present a new method for fully automatic detection and derivation of areas endangered by heavy rainfall based only on digital elevation models. Tracking news show that the majority of occuring natural hazards are flood events. So already many flood prediction systems were developed. But most of these existing systems for deriving areas endangered by flooding events are based only on horizontal and vertical distances to existing rivers and lakes. Typically such systems take not into account dangers arising directly from heavy rain events. In a study conducted by us together with a german insurance company a new approach for detection of areas endangered by heavy rain was proven to give a high correlation of the derived endangered areas and the losses claimed at the insurance company. Here we describe three methods for classification of digital terrain models and analyze their usability for automatic detection and vulnerability analysis for areas endangered by heavy rainfall and analyze the results using the available insurance data.

  9. Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

    Science.gov (United States)

    Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

    2016-01-01

    We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

  10. Mapping of Planetary Surface Age Based on Crater Statistics Obtained by AN Automatic Detection Algorithm

    Science.gov (United States)

    Salih, A. L.; Mühlbauer, M.; Grumpe, A.; Pasckert, J. H.; Wöhler, C.; Hiesinger, H.

    2016-06-01

    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 2.9 Ga) and higher

  11. 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

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

    Science.gov (United States)

    Yoshida, Wataru; Kezuka, Aki; Murakami, Yoshiyuki; Lee, Jinhee; Abe, Koichi; Motoki, Hiroaki; Matsuo, Takafumi; Shimura, Nobuaki; Noda, Mamoru; Igimi, Shizunobu; Ikebukuro, Kazunori

    2013-11-01

    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(6) copies.

  13. [Automatic houses detection with color aerial images based on image segmentation].

    Science.gov (United States)

    He, Pei-Pei; Wan, You-Chuan; Jiang, Peng-Rui; Gao, Xian-Jun; Qin, Jia-Xin

    2014-07-01

    In order to achieve housing automatic detection from high-resolution aerial imagery, the present paper utilized the color information and spectral characteristics of the roofing material, with the image segmentation theory, to study the housing automatic detection method. Firstly, This method proposed in this paper converts the RGB color space to HIS color space, uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmentation to isolate red tiled roofs and gray cement roof areas, and gets the initial segmentation housing areas by using the marked watershed algorithm. Then, region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region. Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area. Compared with the traditional pixel-based region segmentation algorithm, the improved method proposed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human intervention, and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved. A case study was conducted to apply the method proposed in this paper to high resolution aerial images, and the experimental results demonstrate that this method has a high precision and rational robustness.

  14. Automatic detection of ECG electrode misplacement: a tale of two algorithms.

    Science.gov (United States)

    Xia, Henian; Garcia, Gabriel A; Zhao, Xiaopeng

    2012-09-01

    Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records.

  15. 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.

  16. 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.

  17. A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

    Directory of Open Access Journals (Sweden)

    Vaishnavi Ravindran

    2016-11-01

    Full Text Available In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG and Gray level co-occurrence matrix (GLCM features. Since damaged car detection has not been attempted, two datasets of damaged cars - Damaged Cars Dataset-1 (DCD-1 and Damaged Cars Dataset-2 (DCD-2 – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.

  18. 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.

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

    Science.gov (United States)

    Xu, Lili; Luo, Shuqian

    2010-11-01

    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.

  20. 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.

  1. Automatic Detection of Repetitive Components in 3D Mechanical Engineering Models

    Directory of Open Access Journals (Sweden)

    Laixiang Wen

    2013-01-01

    Full Text Available We present an intelligent method to automatically detect repetitive components in 3D mechanical engineering models. In our work, a new Voxel-based Shape Descriptor (VSD is proposed for effective matching, based on which a similarity function is defined. It uses the voxels intersecting with 3D outline of mechanical components as the feature descriptor. Because each mechanical component may have different poses, the alignment before the matching is needed. For the alignment, we adopt the genetic algorithm to search for optimal solution where the maximum global similarity is the objective. Two components are the same if the maximum global similarity is over a certain threshold. Note that the voxelization of component during feature extraction and the genetic algorithm for searching maximum global similarity are entirely implemented on GPU; the efficiency is improved significantly than with CPU. Experimental results show that our method is more effective and efficient than that existing methods for repetitive components detection.

  2. Automatic Detection and Evaluation of Solar Cell Micro-Cracks in Electroluminescence Images Using Matched Filters

    Energy Technology Data Exchange (ETDEWEB)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2016-11-21

    A method for detecting micro-cracks in solar cells using two dimensional matched filters was developed, derived from the electroluminescence intensity profile of typical micro-cracks. We describe the image processing steps to obtain a binary map with the location of the micro-cracks. Finally, we show how to automatically estimate the total length of each micro-crack from these maps, and propose a method to identify severe types of micro-cracks, such as parallel, dendritic, and cracks with multiple orientations. With an optimized threshold parameter, the technique detects over 90 % of cracks larger than 3 cm in length. The method shows great potential for quantifying micro-crack damage after manufacturing or module transportation for the determination of a module quality criterion for cell cracking in photovoltaic modules.

  3. 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...

  4. 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.

  5. Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

    OpenAIRE

    Po-Hsiung Chen; Rintaro Shimada; Sohshi Yabumoto; Hajime Okajima; Masahiro Ando; Chiou-Tzu Chang; Li-Tzu Lee; Yong-Kie Wong; Arthur Chiou; Hiro-o Hamaguchi

    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 ...

  6. A Feasibility Study on the Automatic Detection of Atrial Fibrillations using an Unobtrusive Bed-Mounted Sensor

    OpenAIRE

    Brueser, C.; Zink, M.D.H.; Winter, S; Schauerte, P.; Leonhardt, S.

    2012-01-01

    We present a feasibility study on the automatic detection of atrialfibrillations (AF) from a cardiac vibration signal (ballistocardiogram). Signals were recorded by means of an electromechanical foil attached to a bed’s mattress. A clinical study with 10 AF patients wasconducted to assess whether ballistocardiograms (BCG) provide sufficient information to automatically distinguish atrial fibrillations from normal sinus rhythms. For this purpose, the BCGs were split into30 s long epochs which ...

  7. Heart Beat Detection in Noisy ECG Signals Using Statistical Analysis of the Automatically Detected Annotations

    Directory of Open Access Journals (Sweden)

    Andrius Gudiškis

    2015-07-01

    Full Text Available This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%.

  8. Automatic detection of non-cosmetic soft contact lenses in ocular images

    Science.gov (United States)

    Erdogan, Gizem; Ross, Arun

    2013-05-01

    Recent research in iris recognition has established the impact of non-cosmetic soft contact lenses on the recognition performance of iris matchers. Researchers in Notre Dame demonstrated an increase in False Reject Rate (FRR) when an iris without a contact lens was compared against the same iris with a transparent soft contact lens. Detecting the presence of a contact lens in ocular images can, therefore, be beneficial to iris recognition systems. This study proposes a method to automatically detect the presence of non-cosmetic soft contact lenses in ocular images of the eye acquired in the Near Infrared (NIR) spectrum. While cosmetic lenses are more easily discernible, the problem of detecting non-cosmetic lenses is substantially difficult and poses a significant challenge to iris researchers. In this work, the lens boundary is detected by traversing a small annular region in the vicinity of the outer boundary of the segmented iris and locating candidate points corresponding to the lens perimeter. Candidate points are identified by examining intensity profiles in the radial direction within the annular region. The proposed detection method is evaluated on two databases: ICE 2005 and MBGC Iris. In the ICE 2005 database, a correct lens detection rate of 72% is achieved with an overall classification accuracy of 76%. In the MBGC Iris database, a correct lens detection rate of 70% is obtained with an overall classification accuracy of 66:8%. To the best of our knowledge, this is one of the earliest work attempting to detect the presence of non-cosmetic soft contact lenses in NIR ocular images. The results of this research suggest the possibility of detecting soft contact lenses in ocular images but highlight the need for further research in this area.

  9. IDC: a system for automatically detecting and classifying manmade objects in overhead imagery

    Science.gov (United States)

    Carlotto, Mark J.; Nebrich, Mark; De Michael, David

    2010-04-01

    The automatic detection and classification of manmade objects in overhead imagery is key to generating geospatial intelligence (GEOINT) from today's high space-time bandwidth sensors in a timely manner. A flexible multi-stage object detection and classification capability known as the IMINT Data Conditioner (IDC) has been developed that can exploit different kinds of imagery using a mission-specific processing chain. A front-end data reader/tiler converts standard imagery products into a set of tiles for processing, which facilitates parallel processing on multiprocessor/multithreaded systems. The first stage of processing contains a suite of object detectors designed to exploit different sensor modalities that locate and chip out candidate object regions. The second processing stage segments object regions, estimates their length, width, and pose, and determines their geographic location. The third stage classifies detections into one of K predetermined object classes (specified in a models file) plus clutter. Detections are scored based on their salience, size/shape, and spatial-spectral properties. Detection reports can be output in a number of popular formats including flat files, HTML web pages, and KML files for display in Google Maps or Google Earth. Several examples illustrating the operation and performance of the IDC on Quickbird, GeoEye, and DCS SAR imagery are presented.

  10. Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation.

    Science.gov (United States)

    Larsson, Linnéa; Nyström, Marcus; Ardö, Håkan; Åström, Kalle; Stridh, Martin

    2016-12-01

    An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eye-tracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The binocular algorithm detects 98% of fixations in image stimuli compared to 95% when only one eye is used, while for video stimuli, both the binocular and monocular algorithms detect around 40% of smooth pursuit movements. The present article shows that using binocular information for discrimination of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithm's ability to detect smooth pursuit movements in video and moving-dot stimuli. With an automated evaluation strategy, time-consuming manual annotations are avoided and a larger amount of data can be used in the evaluation process.

  11. 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.

  12. 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.

  13. Developing a system that can automatically detect health changes using transfer times of older adults

    Directory of Open Access Journals (Sweden)

    Greet Baldewijns

    2016-02-01

    Full Text Available Abstract Background As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. Methods This paper presents a system that employs statistical process control techniques (SPC to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. Results The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. Conclusions The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.

  14. 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.

  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 segmentation and centroid detection of skin sensors for lung interventions

    Science.gov (United States)

    Lu, Kongkuo; Xu, Sheng; Xue, Zhong; Wong, Stephen T.

    2012-02-01

    Electromagnetic (EM) tracking has been recognized as a valuable tool for locating the interventional devices in procedures such as lung and liver biopsy or ablation. The advantage of this technology is its real-time connection to the 3D volumetric roadmap, i.e. CT, of a patient's anatomy while the intervention is performed. EM-based guidance requires tracking of the tip of the interventional device, transforming the location of the device onto pre-operative CT images, and superimposing the device in the 3D images to assist physicians to complete the procedure more effectively. A key requirement of this data integration is to find automatically the mapping between EM and CT coordinate systems. Thus, skin fiducial sensors are attached to patients before acquiring the pre-operative CTs. Then, those sensors can be recognized in both CT and EM coordinate systems and used calculate the transformation matrix. In this paper, to enable the EM-based navigation workflow and reduce procedural preparation time, an automatic fiducial detection method is proposed to obtain the centroids of the sensors from the pre-operative CT. The approach has been applied to 13 rabbit datasets derived from an animal study and eight human images from an observation study. The numerical results show that it is a reliable and efficient method for use in EM-guided application.

  17. Single-beam water vapor detection system with automatic photoelectric conversion gain control

    Science.gov (United States)

    Zhu, C. G.; Chang, J.; Wang, P. P.; Wang, Q.; Wei, W.; Liu, Z.; Zhang, S. S.

    2014-11-01

    A single-beam optical sensor system with automatic photoelectric conversion gain control is proposed for doing high reliability water vapor detection under relatively rough environmental conditions. Comparing to a dual-beam system, it can distinguish the finer photocurrent variations caused by the optical power drift and provide timely compensation by automatically adjusting the photoelectric conversion gain. This system can be rarely affected by the optical power drift caused by fluctuating ambient temperature or variation of fiber bending loss. The deviation of the single-beam system is below 1.11% when photocurrent decays due to fiber bending loss for bending radius of 5 mm, which is obviously lower than the dual-beam system (8.82%). We also demonstrate the long-term stability of the single-beam system by monitoring a 660 ppm by volume (ppmv) water vapor sample continuously for 24 h. The maximum deviation of the measured concentration during the whole testing period does not exceed 10 ppmv. Experiments have shown that the new system features better reliability and is more apt for remote sensing application which is often subject to light transmission loss.

  18. 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.

  19. 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.

  20. Automatic Three-dimensional Detection of Photoreceptor Ellipsoid Zone Disruption Caused by Trauma in the OCT

    Science.gov (United States)

    Zhu, Weifang; Chen, Haoyu; Zhao, Heming; Tian, Bei; Wang, Lirong; Shi, Fei; Xiang, Dehui; Luo, Xiaohong; Gao, Enting; Zhang, Li; Yin, Yilong; Chen, Xinjian

    2016-05-01

    Detection and assessment of the integrity of the photoreceptor ellipsoid zone (EZ) are important because it is critical for visual acuity in retina trauma and other diseases. We have proposed and validated a framework that can automatically analyse the 3D integrity of the EZ in optical coherence tomography (OCT) images. The images are first filtered and automatically segmented into 10 layers, of which EZ is located in the 7th layer. For each voxel of the EZ, 57 features are extracted and a principle component analysis is performed to optimize the features. An Adaboost classifier is trained to classify each voxel of the EZ as disrupted or non-disrupted. Finally, blood vessel silhouettes and isolated points are excluded. To demonstrate its effectiveness, the proposed framework was tested on 15 eyes with retinal trauma and 15 normal eyes. For the eyes with retinal trauma, the sensitivity (SEN) was 85.69% ± 9.59%, the specificity (SPE) was 85.91% ± 5.48%, and the balanced accuracy rate (BAR) was 85.80% ± 6.16%. For the normal eyes, the SPE was 99.03% ± 0.73%, and the SEN and BAR levels were not relevant. Our framework has the potential to become a useful tool for studying retina trauma and other conditions involving EZ integrity.

  1. Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image

    CERN Document Server

    Boujelben, Atef; Mnif, Jameleddine; Abid, Mohamed

    2011-01-01

    Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours...

  2. Automatic detection of zebra crossings from mobile LiDAR data

    Science.gov (United States)

    Riveiro, B.; González-Jorge, H.; Martínez-Sánchez, J.; Díaz-Vilariño, L.; Arias, P.

    2015-07-01

    An algorithm for the automatic detection of zebra crossings from mobile LiDAR data is developed and tested to be applied for road management purposes. The algorithm consists of several subsequent processes starting with road segmentation by performing a curvature analysis for each laser cycle. Then, intensity images are created from the point cloud using rasterization techniques, in order to detect zebra crossing using the Standard Hough Transform and logical constrains. To optimize the results, image processing algorithms are applied to the intensity images from the point cloud. These algorithms include binarization to separate the painting area from the rest of the pavement, median filtering to avoid noisy points, and mathematical morphology to fill the gaps between the pixels in the border of white marks. Once the road marking is detected, its position is calculated. This information is valuable for inventorying purposes of road managers that use Geographic Information Systems. The performance of the algorithm has been evaluated over several mobile LiDAR strips accounting for a total of 30 zebra crossings. That test showed a completeness of 83%. Non-detected marks mainly come from painting deterioration of the zebra crossing or by occlusions in the point cloud produced by other vehicles on the road.

  3. Simple procedure for automatic detection of unstable alleles in the myotonic dystrophy and Huntington's disease loci.

    Science.gov (United States)

    Falk, M; Vojtísková, M; Lukás, Z; Kroupová, I; Froster, U

    2006-01-01

    Human neurodegenerative and neuromuscular disorders are associated with a class of gene mutations represented by expansion of trinucleotide repeats. DNA testing is important for the diagnosis of these diseases because clinical discrimination is complicated by their late onset and frequently overlapping symptomatology. However, detection of pathologic alleles expanded up to several thousand trinucleotides poses a challenge for the introduction of rapid, fully automatic, and simple DNA diagnostic procedures. Here we propose a simple two-step polymerase chain reaction (PCR) protocol for rapid molecular diagnostics of myotonic dystrophy, Huntington's disease, and possibly also other triplet expansion diseases. Standard PCR amplification with target repeat flanking primers is used for the detection of alleles of up to 100 repeats; next, triplet-primed PCR is applied for detection of larger expansions. Automated capillary electrophoresis of amplicons allows rapid discrimination between normal, premutated and expanded (CTG/CAG)(n) alleles. Using the suggested protocol, the expanded allele was successfully detected in all test DNA samples with known genotypes. Our experience demonstrates that the suggested two-step PCR protocol provides high sensitivity, specificity, and reproducibility; is significantly less time-consuming; is easier to perform; and provides a better basis for automation than previous methods requiring Southern analysis. Therefore, it can be used for confirmation of uncertain clinical diagnoses, for prenatal testing in at-risk families, and, generally in research on these diseases.

  4. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zappala, D.; Tavner, P.; Crabtree, C.; Sheng, S.

    2013-01-01

    Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data represent one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.

  5. 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.

  6. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    Science.gov (United States)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  7. Automatic Dent-landmark detection in 3-D CBCT dental volumes.

    Science.gov (United States)

    Cheng, Erkang; Chen, Jinwu; Yang, Jie; Deng, Huiyang; Wu, Yi; Megalooikonomou, Vasileios; Gable, Bryce; Ling, Haibin

    2011-01-01

    Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.

  8. Detecting subtle expressions: older adults demonstrate automatic and controlled positive response bias in emotional perception.

    Science.gov (United States)

    Johnson, Dan R; Whiting, Wythe L

    2013-03-01

    The present study examined age differences in emotional perception for the detection of low-intensity, single-emotion facial expressions. Confirming the "positivity effect," at 60 ms and 2,000 ms presentation rates older adults (age = 61+ years, n = 39) exhibited a response bias favoring happy over neutral responses, whereas younger adults (age = 18-23 years, n = 40) favored neutral responses. Furthermore, older adults favored neutral over fearful responses at the 60 ms presentation rate, relative to younger adults. The finding that age differences in response bias were most pronounced at the 60 ms versus 2,000 ms presentation rate suggests that positivity effects in emotional perception rely partly on automatic processing.

  9. Automatic Detection of Pathologies in The Voice by HOS Based Parameters

    Directory of Open Access Journals (Sweden)

    de Leon José

    2001-01-01

    Full Text Available In the current panorama the conclusive identification of a laryngeal pathology relies inevitably on the observation of the vocal folds by means of laryngoscopical techniques. This inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and, above all, the fact that it is an invasive technique. This paper looks into the possibility of measuring the quality of a voice starting from an audio recording. The existing parameters in current literature ("classic parameters" which allow quantifying the quality of a voice have been studied, and the parameters that present better results have been selected. Also, seven new High Order Statistics (HOS based parameters are proposed to parametrize the voice signal. On the other hand, a software package has been developed which carries out the automatic detection of dysfunction in phonation. A success rate of % has been obtained by using both the classic and the HOS based proposed parameters.

  10. 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.

  11. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  12. 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.

  13. An algorithm to detect low incidence arrhythmic events in electrocardiographic records from ambulatory patients.

    Science.gov (United States)

    Hungenahally, S K; Willis, R J

    1994-11-01

    An algorithm was devised to detect low incidence arrhythmic events in electrocardiograms obtained during ambulatory monitoring. The algorithm incorporated baseline correction and R wave detection. The RR interval was used to identify tachycardia, bradycardia, and premature ventricular beats. Only a few beats before and after the arrhythmic event were stored. The software was evaluated on a prototype hardware system which consisted of an Intel 86/30 single board computer with a suitable analog pre-processor and an analog to digital converter. The algorithm was used to determine the incidence and type of arrhythmia in records from an ambulatory electrocardiogram (ECG) database and from a cardiac exercise laboratory. These results were compared to annotations on the records which were assumed to be correct. Standard criteria used previously to evaluate algorithms designed for arrhythmia detection were sensitivity, specificity, and diagnostic accuracy. Sensitivities ranging from 77 to 100%, specificities from 94 to 100%, and diagnostic accuracies from 92 to 100% were obtained on the different data sets. These results compare favourably with published results based on more elaborate algorithms. By circumventing the need to make a continuous record of the ECG, the algorithm could form the basis for a compact monitoring device for the detection of arrhythmic events which are so infrequent that standard 24-h Holter monitoring is insufficient.

  14. Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images.

    Science.gov (United States)

    Chow, Yew Chung; Gao, Xinting; Li, Huiqi; Lim, Joo Hwee; Sun, Ying; Wong, Tien Yin

    2011-01-01

    Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method.

  15. 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

  16. 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.

  17. Increased incidence of acute kidney injury with aprotinin use during cardiac surgery detected with urinary NGAL

    DEFF Research Database (Denmark)

    Wagener, G.; Gubitosa, G.; Wang, S.;

    2008-01-01

    BACKGROUND: Use of aprotinin has been associated with acute kidney injury after cardiac surgery. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel, very sensitive marker for renal injury. Urinary NGAL may be able to detect renal injury caused by aprotinin. This study determined...... if the use of aprotinin is associated with an increased incidence of acute kidney injury and increased levels of urinary NGAL. METHODS: In this prospective, observational study 369 patients undergoing cardiac surgery were enrolled. 205 patients received aprotinin and 164 received epsilon amino-caproic acid...... intraoperatively. Urinary NGAL was measured before and immediately after cardiac surgery and 3, 18 and 24 h later. The association of aprotinin use with the incidence of acute kidney injury (increase of serum creatinine >0.5 mg/dl) and NGAL levels was determined using logistic and linear regression models. RESULTS...

  18. An automatic damage detection algorithm based on the Short Time Impulse Response Function

    Science.gov (United States)

    Auletta, Gianluca; Carlo Ponzo, Felice; Ditommaso, Rocco; Iacovino, Chiara

    2016-04-01

    Structural Health Monitoring together with all the dynamic identification techniques and damage detection techniques are increasing in popularity in both scientific and civil community in last years. The basic idea arises from the observation that spectral properties, described in terms of the so-called modal parameters (eigenfrequencies, mode shapes, and modal damping), are functions of the physical properties of the structure (mass, energy dissipation mechanisms and stiffness). Damage detection techniques traditionally consist in visual inspection and/or non-destructive testing. A different approach consists in vibration based methods detecting changes of feature related to damage. Structural damage exhibits its main effects in terms of stiffness and damping variation. Damage detection approach based on dynamic monitoring of structural properties over time has received a considerable attention in recent scientific literature. We focused the attention on the structural damage localization and detection after an earthquake, from the evaluation of the mode curvature difference. The methodology is based on the acquisition of the structural dynamic response through a three-directional accelerometer installed on the top floor of the structure. It is able to assess the presence of any damage on the structure providing also information about the related position and severity of the damage. The procedure is based on a Band-Variable Filter, (Ditommaso et al., 2012), used to extract the dynamic characteristics of systems that evolve over time by acting simultaneously in both time and frequency domain. In this paper using a combined approach based on the Fourier Transform and on the seismic interferometric analysis, an useful tool for the automatic fundamental frequency evaluation of nonlinear structures has been proposed. Moreover, using this kind of approach it is possible to improve some of the existing methods for the automatic damage detection providing stable results

  19. 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.

  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. 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.

  2. 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.

  3. Automatic Detection Method of Behavior Change in Dam Monitor Instruments Cause by Earthquakes

    Directory of Open Access Journals (Sweden)

    Fernando Mucio Bando

    2016-02-01

    Full Text Available A hydroelectric power plant consists of a project of great relevance for the social and economic development of a country. However, this kind of construction demands extensive attention because the occurrence of unusual behavior on its structure may result in undesirable consequences. Seismic waves are some of the phenomena which demand attention of one in charge of a dam safety because once it happens can directly affect the structure behavior. The target of this work is to present a methodology to automatically detect which monitoring instruments have gone under any change in pattern and their measurements after the seism. The detection method proposed is based on a neuro/fuzzy/bayesian formulation which is divided in three steps. Firstly, a clustering of points in a time series is developed from a self-organizing Kohonen map. Afterwards a fuzzy set is built to transform the initial time series, with arbitrary distribution, into a new series with beta distribution probability and thus enable the detection of changing points through a Monte Carlo simulation via Markov chains. In order to demonstrate the efficiency of the proposal the methodology has been applied in time series generated by Itaipu power plant building structures measurement instruments, which showed little behavior change after the earthquake in Chile in 2010.

  4. Surgery with computerized virtual reality for the automatic detection of tumors.

    Science.gov (United States)

    Fernández Fernández de Santo; Nieto Llanos, S; Ortiz Aguilar, M; Sánchez Colodrón, E; Tello López, J; Blasco Delgado, O; Galván Pérez, A; Maestu García, M; Guerra Paredes, E

    1999-07-01

    We present a novel and highly accurate system based on informatics engineering capable of automatic detection of tumors directly in the operating field. The system can identify the outlines of the tumor, determine whether it is malignant or not, detect lymphadenopathy and determine whether nodes are metastasized or not. The highly elaborate system, based on artificial vision, has been used in 30 gastric and 5 pancreatic neoplasms, among other tumor types. Images of the surgical field were recorded with a video camera connected to a computer, which was operated by the engineer. Questions asked by the surgeon during the procedure were processed immediately and sent to the virtual reality helmet worn by the surgeon, to the TV monitor in the operating room, or to both. The system is based on purely physical and mathematical processes that work reliably; in this sense it is free from errors and is self-consistent, operator errors or hardware failure excepted. In all cases tested here the system correctly identified the tumor as benign or malignant, revealed the extension of the tumor, and detected lymph node metastases. In every case these results were confirmed by histological examination.

  5. Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

    Directory of Open Access Journals (Sweden)

    R. Anand Raji

    2015-05-01

    Full Text Available Imaging sensors operating in infrared (IR region of electromagnetic spectrum are gaining importance in airborne automatic target recognition (ATR applications due to their passive nature of operation. IR imaging sensors exploit the unintended IR radiation emitted by the targets of interest for detection. The ATR systems based on the passive IR imaging sensors employ a set of signal processing algorithms for processing the image information in real-time. The real-time execution of signal processing algorithms provides the sufficient reaction time to the platform carrying ATR system to react upon the target of interest. These set of algorithms include detection, tracking, and classification of low-contrast, small sized-targets. Paper explained a signal processing framework developed to detect and track moving point targets from the acquired IR image sequences in real-time.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.208-213, DOI: http://dx.doi.org/10.14429/dsj.65.8164

  6. Automatic detection of sea-sky horizon line and small targets in maritime infrared imagery

    Science.gov (United States)

    Kong, Xiangyu; Liu, Lei; Qian, Yunsheng; Cui, Minjie

    2016-05-01

    It is usually difficult but important to extract distant targets from sea clutters and clouds since the targets are small compared to the pixel field of view. In this paper, an algorithm based on wavelet transformation is proposed for automatic detection of small targets under the maritime background. We recognize that the distant small targets generally appear near the sea-sky horizon line and noises lie along the direction of sea-sky horizon line. So the sea-sky horizon is located firstly by examining the approximate image of a Haar wavelet decomposition of the original image. And the equation of the sea-sky horizon is set up, no matter whether the sea-sky horizon is horizontal or not. Since the sea-sky horizon is located, not only the potential area but also the strip direction of noise is got. Then the modified mutual wavelet energy combination algorithm is applied to extract targets with targets being marked by red windows. Computer simulations are shown to validate the great adaptability of the sea-sky horizon line detection and the accuracy of the small targets detection. The algorithm should be useful to engineers and scientists to design precise guidance or maritime monitoring system.

  7. Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

    Science.gov (United States)

    Ghaffarian, S.; Ghaffarian, S.

    2014-08-01

    This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

  8. Improvement of Sample Selection: A Cascade-Based Approach for Lesion Automatic Detection

    Directory of Open Access Journals (Sweden)

    Shofwatul ‘Uyun

    2016-04-01

    Full Text Available Computer-Aided Detection (CADe system has a significant role as a preventative effort in the early detection of breast cancer. There are some phases in developing the pattern recognition on the CADe system, including the availability of a large number of data, feature extraction, selection and use of features, and the selection of the appropriate classification method. Haar cascade classifier has been successfully developed to detect the faces in the multimedia image automatically and quickly. The success of the face detection system must not be separated from the availability of the training data in the large numbers. However, it is not easy to implement on a medical image because of some reasons, including its low quality, the very little gray-value differences, and the limited number of the patches for the examples of the positive data. Therefore, this research proposes an algorithm to overcome the limitation of the number of patches on the region of interest to detect whether the lesion exists or not on the mammogram images based on the Haar cascade classifier. This research uses the mammogram and ultrasonography images from the breast imaging of 60 probands and patients in the Clinic of Oncology, Yogyakarta. The testing of the CADe system is done by comparing the reading result of that system with the mammography reading result validated with the reading of the ultrasonography image by the Radiologist. The testing result of the k-fold cross validation demonstrates that the use of the algorithm for the multiplication of intersection rectangle may improve the system performance with accuracy, sensitivity, and specificity of 76%, 89%, and 63%, respectively.

  9. CRISPR Recognition Tool (CRT: a tool for automatic detection of clustered regularly interspaced palindromic repeats

    Directory of Open Access Journals (Sweden)

    Brown Kyndall

    2007-06-01

    Full Text Available Abstract Background 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. Results 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 proved to be a huge improvement over Patscan. Both CRT and Pilercr were comparable in speed, however CRT was faster for genomes containing large numbers of repeats. Conclusion In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, showed some important improvements over 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.

  10. Digital magnification mammography with matched incident exposure: physical imaging properties and detectability of simulated microcalcifications.

    Science.gov (United States)

    Tanaka, Nobukazu; Naka, Kentaro; Fukushima, Hiroko; Morishita, Junji; Toyofuku, Fukai; Ohki, Masafumi; Higashida, Yoshiharu

    2011-07-01

    Our purpose was to evaluate the usefulness of digital magnification mammography with matched incident exposure by investigating the physical imaging properties and doing an observer performance test. A computed radiography system and a mammographic unit were used in this study. Contact and magnification radiographies of 1.2-1.8 in combination with focal spot sizes of 0.1 mm without grid and 0.3 mm with grid were performed. Physical imaging properties, namely, scatter fraction, total modulation transfer function (MTF) including the presampled MTF and the MTF of focal spot size, and Wiener spectrum (WS), were measured. Detail visibility was evaluated by use of free-response receiver operating characteristic analysis of the detectability of simulated microcalcifications. Scatter fractions decreased considerably as the magnification factor increased without grid technique. In the grid technique, scatter fractions for all magnification techniques were comparable. The total MTFs of magnification techniques with a focal spot size of 0.1 mm improved significantly compared with the conventional contact technique. However, the improvement of the total MTFs of magnification techniques with the combination of 0.3 mm focal spot size was small. The WSs degraded with an increase of the magnification factor compared with the contact technique due to the maintained exposure incident on the object. The observer performance test indicated that the 1.8 magnification technique with the 0.1 mm focal spot size provided higher detectability than did the contact technique. Digital magnification mammography under the same incident exposure conditions improved the detectability of microcalcifications.

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

    Science.gov (United States)

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

    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.

  12. 基于RTMS的高速公路事件检测系统设计%Design of Highway Incident Detection System Based on RTMS

    Institute of Scientific and Technical Information of China (English)

    刘琳娜; 蒋珉; 柴干

    2011-01-01

    从软件设计与实现的角度出发,以满足现代高速公路运行管理对交通事件检测的需求为目标,探讨了基于远程交通微波检测器(RTMS,Remote Transportation Microwave Sensor)的高速公路事件检测系统的设计方案,阐述了系统软件结构及各模块的功能与实现方案,介绍了应崩于该系统的交通事件自动检测算法.系统通过采集RTMS检测到的交通数据,运用自动检测算法检测高速公路上异常事件的发生,配合使用CCTV监控摄像机进行人工核实,可以更加准确、快速地确认事件,以便交管部门进一步采取相应处理措施.%From the standpoint of software design and implementation, the design of highway incident detection system based on RTMS ( Remote Transportation Microwave Sensor) was researched, aiming at meeting the needs of traffic incident detection for today's highway operation and management. Software structure of the system and the function and tealization of each module were illustrated. The AID ( Automatic Incident Detection) algorithm used in this system was introduced. By collecting the data detected with RTMS and detecting exceptional events on highway by AID algorithm and using CCTV monitoring cameras to verify events, the system can confirm events more accurately and more quickly, which guarantees traffic department to take further steps.

  13. Automatic Real-Time Embedded QRS Complex Detection for a Novel Patch-Type Electrocardiogram Recorder.

    Science.gov (United States)

    Saadi, Dorthe B; Tanev, George; Flintrup, Morten; Osmanagic, Armin; Egstrup, Kenneth; Hoppe, Karsten; Jennum, Poul; Jeppesen, Jørgen L; Iversen, Helle K; Sorensen, Helge B D

    2015-01-01

    Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. The design of novel patch-type ECG recorders has increased the accessibility of these long-term recordings. In many applications, it is furthermore an advantage for these devices that the recorded ECGs can be analyzed automatically in real time. The purpose of this study was therefore to design a novel algorithm for automatic heart beat detection, and embed the algorithm in the CE marked ePatch heart monitor. The algorithm is based on a novel cascade of computationally efficient filters, optimized adaptive thresholding, and a refined search back mechanism. The design and optimization of the algorithm was performed on two different databases: The MIT-BIH arrhythmia database ([Formula: see text]%, [Formula: see text]) and a private ePatch training database ([Formula: see text]%, [Formula: see text]%). The offline validation was conducted on the European ST-T database ([Formula: see text]%, [Formula: see text]%). Finally, a double-blinded validation of the embedded algorithm was conducted on a private ePatch validation database ([Formula: see text]%, [Formula: see text]%). The algorithm was thus validated with high clinical performance on more than 300 ECG records from 189 different subjects with a high number of different abnormal beat morphologies. This demonstrates the strengths of the algorithm, and the potential for this embedded algorithm to improve the possibilities of early diagnosis and treatment of cardiovascular diseases.

  14. Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

    Science.gov (United States)

    Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

    2014-01-01

    Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

  15. Attributed graph distance measure for automatic detection of Attention Deficit Hyperactive Disordered subjects

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2014-06-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49% and test data sets (73.55%. Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

  16. Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

    Science.gov (United States)

    Wu, Teng; Hu, Xiangyun; Zhang, Yong; Zhang, Lulin; Tao, Pengjie; Lu, Luping

    2016-11-01

    The automatic extraction of terrain from high-resolution satellite optical images is very difficult under cloudy conditions. Therefore, accurate cloud detection is necessary to fully use the cloud-free parts of images for terrain extraction. This paper addresses automated cloud detection by introducing an image matching based method under a stereo vision framework, and the optimization usage of non-cloudy areas in stereo matching and the generation of digital surface models (DSMs). Given that clouds are often separated from the terrain surface, cloudy areas are extracted by integrating dense matching DSM, worldwide digital elevation model (DEM) (i.e., shuttle radar topography mission (SRTM)) and gray information from the images. This process consists of the following steps: an image based DSM is firstly generated through a multiple primitive multi-image matcher. Once it is aligned with the reference DEM based on common features, places with significant height differences between the DSM and the DEM will suggest the potential cloud covers. Detecting cloud at these places in the images then enables precise cloud delineation. In the final step, elevations of the reference DEM within the cloud covers are assigned to the corresponding region of the DSM to generate a cloud-free DEM. The proposed approach is evaluated with the panchromatic images of the Tianhui satellite and has been successfully used in its daily operation. The cloud detection accuracy for images without snow is as high as 95%. Experimental results demonstrate that the proposed method can significantly improve the usage of the cloudy panchromatic satellite images for terrain extraction.

  17. 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.

  18. Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment.

    Science.gov (United States)

    Jochem, Andreas; Höfle, Bernhard; Rutzinger, Martin; Pfeifer, Norbert

    2009-01-01

    A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m(2).

  19. Automatic Detection of Building Points from LIDAR and Dense Image Matching Point Clouds

    Science.gov (United States)

    Maltezos, E.; Ioannidis, C.

    2015-08-01

    This study aims to detect automatically building points: (a) from LIDAR point cloud using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a point cloud which is extracted applying dense image matching at high resolution colour-infrared (CIR) digital aerial imagery using the stereo method semi-global matching (SGM). At first step, the removal of the vegetation is carried out. At the LIDAR point cloud, two different methods are implemented and evaluated using initially the normals and the roughness values afterwards: (1) the proposed scan line smooth filtering and a thresholding process, and (2) a bilateral filtering and a thresholding process. For the case of the CIR point cloud, a variation of the normalized differential vegetation index (NDVI) is computed for the same purpose. Afterwards, the bare-earth is extracted using a morphological operator and removed from the rest scene so as to maintain the buildings points. The results of the extracted buildings applying each approach at an urban area in northern Greece are evaluated using an existing orthoimage as reference; also, the results are compared with the corresponding classified buildings extracted from two commercial software. Finally, in order to verify the utility and functionality of the extracted buildings points that achieved the best accuracy, the 3D models in terms of Level of Detail 1 (LoD 1) and a 3D building change detection process are indicatively performed on a sub-region of the overall scene.

  20. Automatic microseismic event detection by band-limited phase-only correlation

    Science.gov (United States)

    Wu, Shaojiang; Wang, Yibo; Zhan, Yi; Chang, Xu

    2016-12-01

    Identification and detection of microseismic events is a significant issue in source locations and source mechanism analysis. The number of the records is notably large, especially in the case of some real-time monitoring, and while the majority of microseismic events are highly weak and sparse, automatic algorithms are indispensable. In this study, we introduce an effective method for the identification and detection of microseismic events by judging whether the P-wave phase exists in a local segment from a single three-component microseismic records. The new judging algorithm consists primarily of the following key steps: 1) transform the waveform time series into time-varying spectral representations using the S-transform; 2) calculate the similarity of the frequency content in the time-frequency domain using the phase-only correlation function; and 3) identify the P-phase by the combination analysis between any two components. The proposed algorithm is compared to a similar approach using the cross-correlation in the time domain between any two components and later tested with synthetic microseismic datasets and real field-recorded datasets. The results indicate that the proposed algorithm is able to distinguish similar and dissimilar waveforms, even for low signal noise ratio and emergent events, which is important for accurate and rapid selection of microseismic events from a large number of records. This method can be applied to other geophysical analyses based on the waveform data.

  1. Automatic detection of end-diastole and end-systole from echocardiography images using manifold learning.

    Science.gov (United States)

    Gifani, Parisa; Behnam, Hamid; Shalbaf, Ahmad; Sani, Zahra Alizadeh

    2010-09-01

    The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold. There are three dense regions on the manifold which correspond to the three phases of cardiac cycle ('isovolumetric contraction', 'isovolumetric relaxation', 'reduced filling'), wherein there is no prominent change in ventricular volume. By the fact that the end-systolic and end-diastolic frames are in isovolumic phases of the cardiac cycle, the dense regions can be used to find these frames. By calculating the distance between consecutive points in the manifold, the isovolumic frames are mapped on the three minimums of the distance diagrams which were used to select the corresponding images. The minimum correlation between these images leads to detection of end-systole and end-diastole frames. The results on six healthy volunteers have been validated by an experienced echo cardiologist and depict the usefulness of the presented method.

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

    Science.gov (United States)

    Hill, Edward; Han, David; Dumouchel, Pierre; Dehak, Najim; Quatieri, Thomas; Moehs, Charles; Oscar-Berman, Marlene; Giordano, John; Simpatico, Thomas; Barh, Debmalya; Blum, Kenneth

    2013-01-01

    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.

  3. 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.

  4. Automatic aerial image shadow detection through the hybrid analysis of RGB and HIS color space

    Science.gov (United States)

    Wu, Jun; Li, Huilin; Peng, Zhiyong

    2015-12-01

    This paper presents our research on automatic shadow detection from high-resolution aerial image through the hybrid analysis of RGB and HIS color space. To this end, the spectral characteristics of shadow are firstly discussed and three kinds of spectral components including the difference between normalized blue and normalized red component - BR, intensity and saturation components are selected as criterions to obtain initial segmentation of shadow region (called primary segmentation). After that, within the normalized RGB color space and HIS color space, the shadow region is extracted again (called auxiliary segmentation) using the OTSU operation, respectively. Finally, the primary segmentation and auxiliary segmentation are combined through a logical AND-connection operation to obtain reliable shadow region. In this step, small shadow areas are removed from combined shadow region and morphological algorithms are apply to fill small holes as well. The experimental results show that the proposed approach can effectively detect the shadow region from high-resolution aerial image and in high degree of automaton.

  5. LMD based features for the automatic seizure detection of EEG signals using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2016-09-20

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  6. Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

    Directory of Open Access Journals (Sweden)

    Jiange Liu

    2016-06-01

    Full Text Available A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data.

  7. A Feasibility Study on the Automatic Detection of Atrial Fibrillations using an Unobtrusive Bed-Mounted Sensor

    NARCIS (Netherlands)

    Brueser, C.; Zink, M.D.H.; Winter, S.; Schauerte, P.; Leonhardt, S.

    2012-01-01

    We present a feasibility study on the automatic detection of atrialfibrillations (AF) from a cardiac vibration signal (ballistocardiogram). Signals were recorded by means of an electromechanical foil attached to a bed’s mattress. A clinical study with 10 AF patients wasconducted to assess whether ba

  8. Automatic trip and mode detection with MoveSmarter: first results from the Dutch Mobile Mobility Panel

    NARCIS (Netherlands)

    Geurs, K.T.; Thomas, T.; Bijlsma, M.; Douhou, S.

    2015-01-01

    This paper describes the performance of a smartphone app called MoveSmarter to automatically detect departure and arrival times, trip origins and destinations, transport modes, and travel purposes. The app is used in a three-year smartphone-based prompted-recall panel survey in which about 600 smart

  9. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    OpenAIRE

    Akara Sopharak; Sarah Barman; Bunyarit Uyyanonvara

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse se...

  10. 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.

  11. 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.

  12. 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.

  13. Toward automatic evaluation of defect detectability in infrared images of composites and honeycomb structures

    Science.gov (United States)

    Florez-Ospina, Juan F.; Benitez-Restrepo, H. D.

    2015-07-01

    Non-destructive testing (NDT) refers to inspection methods employed to assess a material specimen without impairing its future usefulness. An important type of these methods is infrared (IR) for NDT (IRNDT), which employs the heat emitted by bodies/objects to rapidly and noninvasively inspect wide surfaces and to find specific defects such as delaminations, cracks, voids, and discontinuities in materials. Current advancements in sensor technology for IRNDT generate great amounts of image sequences. These data require further processing to determine the integrity of objects. Processing techniques for IRNDT data implicitly looks for defect visibility enhancement. Commonly, IRNDT community employs signal to noise ratio (SNR) to measure defect visibility. Nonetheless, current applications of SNR are local, thereby overseeing spatial information, and depend on a-priori knowledge of defect's location. In this paper, we present a general framework to assess defect detectability based on SNR maps derived from processed IR images. The joint use of image segmentation procedures along with algorithms for filling regions of interest (ROI) estimates a reference background to compute SNR maps. Our main contributions are: (i) a method to compute SNR maps that takes into account spatial variation and are independent of a-priori knowledge of defect location in the sample, (ii) spatial background analysis in processed images, and (iii) semi-automatic calculation of segmentation algorithm parameters. We test our approach in carbon fiber and honeycomb samples with complex geometries and defects with different sizes and depths.

  14. 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.

  15. Extending the Dynamic Range in Metabolomics Experiments by Automatic Correction of Peaks Exceeding the Detection Limit.

    Science.gov (United States)

    Lisec, Jan; Hoffmann, Friederike; Schmitt, Clemens; Jaeger, Carsten

    2016-08-02

    Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN ( https://cran.r-project.org/ ) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.

  16. Automatic Detection and Reproduction of Natural Head Position in Stereo-Photogrammetry.

    Directory of Open Access Journals (Sweden)

    Tai-Chiu Hsung

    Full Text Available 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.

  17. Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis

    Science.gov (United States)

    Liu, Chanjuan; van Netten, Jaap J.; van Baal, Jeff G.; Bus, Sicco A.; van der Heijden, Ferdi

    2015-02-01

    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 thermography is a promising modality for such a system. The temperature differences between corresponding areas on contralateral feet are the clinically significant parameters. This asymmetric analysis is hindered by (1) foot segmentation errors, especially when the foot temperature and the ambient temperature are comparable, and by (2) different shapes and sizes between contralateral feet due to deformities or minor amputations. To circumvent the first problem, we used a color image and a thermal image acquired synchronously. Foot regions, detected in the color image, were rigidly registered to the thermal image. This resulted in 97.8%±1.1% sensitivity and 98.4%±0.5% specificity over 76 high-risk diabetic patients with manual annotation as a reference. Nonrigid landmark-based registration with B-splines solved the second problem. Corresponding points in the two feet could be found regardless of the shapes and sizes of the feet. With that, the temperature difference of the left and right feet could be obtained.

  18. Fully automatic lung segmentation and rib suppression methods to improve nodule detection in chest radiographs.

    Science.gov (United States)

    Soleymanpour, Elaheh; Pourreza, Hamid Reza; Ansaripour, Emad; Yazdi, Mehri Sadooghi

    2011-07-01

    Computer-aided Diagnosis (CAD) systems can assist radiologists in several diagnostic tasks. Lung segmentation is one of the mandatory steps for initial detection of lung cancer in Posterior-Anterior chest radiographs. On the other hand, many CAD schemes in projection chest radiography may benefit from the suppression of the bony structures that overlay the lung fields, e.g. ribs. The original images are enhanced by an adaptive contrast equalization and non-linear filtering. Then an initial estimation of lung area is obtained based on morphological operations and then it is improved by growing this region to find the accurate final contour, then for rib suppression, we use oriented spatial Gabor filter. The proposed method was tested on a publicly available database of 247 chest radiographs. Results show that this method outperformed greatly with accuracy of 96.25% for lung segmentation, also we will show improving the conspicuity of lung nodules by rib suppression with local nodule contrast measures. Because there is no additional radiation exposure or specialized equipment required, it could also be applied to bedside portable chest x-rays. In addition to simplicity of these fully automatic methods, lung segmentation and rib suppression algorithms are performed accurately with low computation time and robustness to noise because of the suitable enhancement procedure.

  19. Automatic characteristic frequency association and all-sideband demodulation for the detection of a bearing fault

    Science.gov (United States)

    Firla, Marcin; Li, Zhong-Yang; Martin, Nadine; Pachaud, Christian; Barszcz, Tomasz

    2016-12-01

    This paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The first contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic configuration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate filtering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the filtered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the European Innovation Project 'KAStrion'. This rig simulates a wind turbine drive train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity.

  20. Automatic detection and extraction of ultra-fine bright structure observed with new vacuum solar telescope

    Science.gov (United States)

    Deng, Linhua

    2017-02-01

    Solar magnetic structures exhibit a wealth of different spatial and temporal scales. Presently, solar magnetic element is believed to be the ultra-fine magnetic structure in the lower solar atmospheric layer, and the diffraction limit of the largest-aperture solar telescope (New Vacuum Solar Telescope; NVST) of China is close to the spatial scale of magnetic element. This implies that modern solar observations have entered the era of high resolution better than 0.2 arc-second. Since the year of 2011, the NVST have successfully established and obtained huge observational data. Moreover, the ultra-fine magnetic structure rooted in the dark inter-graunlar lanes can be easily resolved. Studies on the observational characteristics and physical mechanism of magnetic bright points is one of the most important aspects in the field of solar physics, so it is very important to determine the statistical and physical parameters of magnetic bright points with the feature extraction techniques and numerical analysis approaches. For identifying such ultra-fine magnetic structure, an automatically and effectively detection algorithm, employed the Laplacian transform and the morphological dilation technique, is proposed and examined. Then, the statistical parameters such as the typical diameter, the area distribution, the eccentricity, and the intensity contrast are obtained. And finally, the scientific meaning for investigating the physical parameters of magnetic bright points are discussed, especially for understanding the physical processes of solar magnetic energy transferred from the photosphere to the corona.

  1. Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data.

    Science.gov (United States)

    Marghany, Maged

    2014-12-15

    In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey.

  2. Configurable automatic detection and registration of fiducial frames for device-to-image registration in MRI-guided prostate interventions.

    Science.gov (United States)

    Tokuda, Junichi; Song, Sang-Eun; Tuncali, Kemal; Tempany, Clare; Hata, Nobuhiko

    2013-01-01

    We propose a novel automatic fiducial frame detection and registration method for device-to-image registration in MRI-guided prostate interventions. The proposed method does not require any manual selection of markers, and can be applied to a variety of fiducial frames, which consist of multiple cylindrical MR-visible markers placed in different orientations. The key idea is that automatic extraction of linear features using a line filter is more robust than that of bright spots by thresholding; by applying a line set registration algorithm to the detected markers, the frame can be registered to the MRI. The method was capable of registering the fiducial frame to the MRI with an accuracy of 1.00 +/- 0.73 mm and 1.41 +/- 1.06 degrees in a phantom study, and was sufficiently robust to detect the fiducial frame in 98% of images acquired in clinical cases despite the existence of anatomical structures in the field of view.

  3. Automatic detection of contrast injection on fluoroscopy and angiography for image guided trans-catheter aortic valve implantations (TAVI)

    Science.gov (United States)

    Liao, Rui; You, Wei; Yan, Michelle; John, Matthias

    2011-03-01

    Presentation of detailed anatomical structures via 3-D models helps navigation and deployment of the prosthetic valve in TAVI procedures. Fast and automatic contrast detection in the aortic root on X-ray images facilitates a seamless workflow to utilize the 3-D models by triggering 2-D/3-D registration automatically when motion compensation is needed. In this paper, we propose a novel method for automatic detection of contrast injection in the aortic root on fluoroscopic and angiographic sequences. The proposed method is based on histogram analysis and likelihood ratio test, and is robust to variations in the background, the density and volume of the injected contrast, and the size of the aorta. The performance of the proposed algorithm was evaluated on 26 sequences from 5 patients and 3 clinical sites, with 16 out of 17 contrast injections correctly detected and zero false detections. The proposed method is of general form and can be extended for detection of contrast injection in other organs and/or applications.

  4. 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.

  5. 数据融合技术在交通事件检测中的应用综述%A Review of Application of Data Fusion Technology in the Field of Traffic Incident Detection

    Institute of Scientific and Technical Information of China (English)

    姜桂艳; 李琦; 常安德

    2011-01-01

    In order to improve the overall performance of traffic incident detection system and the utilization of multi-source information, based on the analysis of the main problems in current traffic detection methods, the application of data fusion in traffic incident detection system is summarized, analyzed, and classified into two kinds of data fusion application modes, which are data-level fusion of multiple-source information for automatic incident detection and decisionlevel fusion of traffic incident detection methods based on multiple-source information. The major problems in current research and the trends of future research are also pointed out.%为了进一步提高交通事件检测系统的性能,在对基于单源信息的交通事件检测方法进行分析的基础上,从基于多信息源的交通事件自动检测数据级融合和基于多信息源的交通事件检测方法决策级融合2个方面,分析、总结了数据融合技术在交通事件检测中的应用现状,并指出了目前研究存在的主要问题及后续研究的发展趋势.

  6. Automatic detection of asteroids and meteoroids --- a wide-field survey

    Science.gov (United States)

    Vereš, P.; Tóth, J.; Jedicke, R.; Tonry, J.; Denneau, L.; Wainscoat, R.; Kornoš, L.; Šilha, J.

    2014-07-01

    The small Near-Earth Asteroids (NEAs) represent a potential risk but also an easily accessible space resource for future robotic or human in-situ space exploration or commercial activities. However, the population of 1--300 m NEAs is not well understood in terms of size- frequency and orbital distribution. NEAs with diameters below 200 m tend to have much faster spin rates than large objects and they are believed to be monolithic and not rubble-pile like their large counterparts. Moreover, the current surveys do not systematically search for the small NEAs that are mostly overlooked. We propose a low- cost robotic optical survey (ADAM-WFS) aimed at small NEAs based on four state-of-the-art telescopes having extremely wide fields of view. The four Houghton-Terebizh 30-cm astrographs (Fig. left) with 4096×4096 -pixel CCD cameras will acquire 96 square degrees in one exposure with the plate scale of 4.4 arcsec/pixel. In 30 seconds, the system will be able to reach +17.5 mag in unfiltered mode. The survey will be operated on semi-automatic basis, covering the entire night sky three times per night and optimized toward fast moving targets recognition. The advantage of the proposed system is the usage of existing of-the-shelf components and software for the image processing and object identification and linking (Denneau et al., 2013). The one-year simulation of the survey (Fig. right) at the testing location at AGO Modra observatory in Slovakia revealed that we will detect 60--240 NEAs between 1--300 m that get closer than 10 lunar distances from the Earth. The number of detections will rise by a factor of 1.5--2 in case the survey is placed at a superb observing location such as Canary Islands. The survey will also serve as an impact warning system for imminent impactors. Our simulation showed that we have a 20 % chance of finding a 50-m NEA on a direct impact orbit. The survey will provide multiple byproducts from the all-sky scans, such as comet discoveries, sparse

  7. 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

  8. 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.

  9. Establishing Criteria for a Method to Automatically Detect the Onset of Parturition and Dystocia in Breeding Pigs

    OpenAIRE

    Gutierrez, Winson-Montanez; Kim, Dae-Geun; Kim, Dong-Hyeok; Kim, Suk; Le, Seung-Joo; Kim, Byeong-Woo; Hong, Jong-Tae; Yu, Byeong-Ki; Kim, Hyuck-Joo; Oh, Taek-Keun

    2011-01-01

    The aims of the present study were to characterize the farrowing process in gilts and multiparous sows in terms of duration of farrowing, birth intervals, birth weight, piglets born alive, stillbirth, mummified and dystocia by comparing means in terms of parities, and to establish criteria for a method to automatically detect the first birth and dystocia in breeding pigs for a selected farm in South Korea. One hundred nine Yorkshire x Landrace; YL, Landrace x Yorkshire; LY which were mainly r...

  10. An Approach to Automatic Detection and Hazard Risk Assessment of Large Protruding Rocks in Densely Forested Hilly Region

    Science.gov (United States)

    Chhatkuli, S.; Kawamura, K.; Manno, K.; Satoh, T.; Tachibana, K.

    2016-06-01

    Rock-fall along highways or railways presents one of the major threats to transportation and human safety. So far, the only feasible way to detect the locations of such protruding rocks located in the densely forested hilly region is by physically visiting the site and assessing the situation. Highways or railways are stretched to hundreds of kilometres; hence, this traditional approach of determining rock-fall risk zones is not practical to assess the safety throughout the highways or railways. In this research, we have utilized a state-of-the-art airborne LiDAR technology and derived a workflow to automatically detect protruding rocks in densely forested hilly regions and analysed the level of hazard risks they pose. Moreover, we also performed a 3D dynamic simulation of rock-fall to envisage the event. We validated that our proposed technique could automatically detect most of the large protruding rocks in the densely forested hilly region. Automatic extraction of protruding rocks and proper risk zoning could be used to identify the most crucial place that needs the proper protection measures. Hence, the proposed technique would provide an invaluable support for the management and planning of highways and railways safety, especially in the forested hilly region.

  11. Automatic detection of MLC relative position errors for VMAT using the EPID-based picket fence test

    Science.gov (United States)

    Christophides, Damianos; Davies, Alex; Fleckney, Mark

    2016-12-01

    Multi-leaf collimators (MLCs) ensure the accurate delivery of treatments requiring complex beam fluences like intensity modulated radiotherapy and volumetric modulated arc therapy. The purpose of this work is to automate the detection of MLC relative position errors  ⩾0.5 mm using electronic portal imaging device-based picket fence tests and compare the results to the qualitative assessment currently in use. Picket fence tests with and without intentional MLC errors were measured weekly on three Varian linacs. The picket fence images analysed covered a time period ranging between 14-20 months depending on the linac. An algorithm was developed that calculated the MLC error for each leaf-pair present in the picket fence images. The baseline error distributions of each linac were characterised for an initial period of 6 months and compared with the intentional MLC errors using statistical metrics. The distributions of median and one-sample Kolmogorov-Smirnov test p-value exhibited no overlap between baseline and intentional errors and were used retrospectively to automatically detect MLC errors in routine clinical practice. Agreement was found between the MLC errors detected by the automatic method and the fault reports during clinical use, as well as interventions for MLC repair and calibration. In conclusion the method presented provides for full automation of MLC quality assurance, based on individual linac performance characteristics. The use of the automatic method has been shown to provide early warning for MLC errors that resulted in clinical downtime.

  12. Incidence and imaging characteristics of skeletal metastases detected by bone scintigraphy in lung cancer patients

    Directory of Open Access Journals (Sweden)

    Jauković Ljiljana

    2006-01-01

    Full Text Available Background/Aim. Detection of metastatic bone disease by skeletal scintigraphy is a classical application of nuclear medicine in cancer patients. Detection of bone metastases in patients with lung cancer is necessary for an appropriate treatment modality. The aim of this study was to report the frequency and imaging characteristics of bone metastases detected by bone scintigraphy (BS using technetium-99m phosphonates in patients with lung cancer. Methods. We retrospectively analyzed a total of one hundred patients (78 males and 22 females, mean age of 63.3 years, with the diagnosis of lung cancer, who underwent BS during a three-year period (2003−2005. Scintiscans were classified as positive, negative and suspicious with regard to the presence of bone metastases. Results. The incidence of positive, negative and suspicious findings were 57%. 32% and 11%, respectively. Out of 57 patients with bone metastases, 51 had multiple asymmetric foci of increased tracer activity localized in the ribs, spine, extremities, pelvis, sternum, scapula and skull in 72%, 54%, 49%, 37%, 12%, 9% and 5% of scans, respectively. BS revealed solitary metastases in 6 of the patients. The lesions were located in the lower limbs in three patients and in the upper limbs, pelvis and sternum in the remaining three patients. Conclusion. Bone scintigraphy plays a significant role in staging and selecting of patients for curative lung surgery. Due to the fact that metastatic involvment of the extremities was frequently shown, our study suggests that systematic inclusion of the limbs in BS acquisition should be obligatory.

  13. Reference spectral signature selection using density-based cluster for automatic oil spill detection in hyperspectral images.

    Science.gov (United States)

    Liu, Delian; Zhang, Jianqi; Wang, Xiaorui

    2016-04-04

    Reference spectral signature selection is a fundamental work for automatic oil spill detection. To address this issue, a new approach is proposed here, which employs the density-based cluster to select a specific spectral signature from a hyperspectral image. This paper first introduces the framework of oil spill detection from hyperspectral images, indicating that detecting oil spill requires a reference spectral signature of oil spill, parameters of background, and a target detection algorithm. Based on the framework, we give the new reference spectral signature selection approach in details. Then, we demonstrate the estimation of background parameters according to the reflectance of seawater in the infrared bands. Next, the conventional adaptive cosine estimator (ACE) algorithm is employed to achieve oil spill detection. Finally, the proposed approach is tested via several practical hyperspectral images that are collected during the Horizon Deep water oil spill. The experimental results show that this new approach can automatically select the reference spectral signature of oil spills from hyperspectral images and has high detection performance.

  14. Experimental demonstration of tunable homodyne detection of WDM and dual-polarization PSK channels by automatically locking the channels to a local pump laser using nonlinear mixing.

    Science.gov (United States)

    Almaiman, Ahmed; Ziyadi, Morteza; Mohajerin-Ariaei, Amirhossein; Cao, Yinwen; Chitgarha, Mohammad Reza; Liao, Peicheng; Bao, Changjing; Shamee, Bishara; Ahmed, Nisar; Alishahi, Fatemeh; Fallahpour, Ahmad; Akasaka, Youichi; Yang, Jeng-Yuan; Sekiya, Motoyoshi; Touch, Joseph D; Tur, Moshe; Langrock, Carsten; Fejer, Martin M; Willner, Alan E

    2016-06-15

    This Letter proposes a method for tunable automatically locked homodyne detection of wavelength-division multiplexing (WDM) dual-polarization (DP) phase-shift keyed (PSK) channels using nonlinear mixing. Two stages of periodically poled lithium niobate (PPLN) waveguides and an LCoS filter enable automatic phase locking of the channels to a local laser.

  15. 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...... of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi- modal detection...... system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data....

  16. 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

  17. A low cost automatic detection and ranging system for space surveillance in the medium Earth orbit region and beyond.

    Science.gov (United States)

    Danescu, Radu; Ciurte, Anca; Turcu, Vlad

    2014-02-11

    The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth's orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image.

  18. Automatic procedure for mass and charge identification of light isotopes detected in CsI(Tl) of the GARFIELD apparatus

    Science.gov (United States)

    Morelli, L.; Bruno, M.; Baiocco, G.; Bardelli, L.; Barlini, S.; Bini, M.; Casini, G.; D'Agostino, M.; Degerlier, M.; Gramegna, F.; Kravchuk, V. L.; Marchi, T.; Pasquali, G.; Poggi, G.

    2010-08-01

    Mass and charge identification of light charged particles detected with the 180 CsI(Tl) detectors of the GARFIELD apparatus is presented. A "tracking" method to automatically sample the Z and A ridges of "Fast-Slow" histograms is developed. An empirical analytic identification function is used to fit correlations between Fast and Slow, in order to determine, event by event, the atomic and mass numbers of the detected charged reaction products. A summary of the advantages of the proposed method with respect to "hand-based" procedures is reported.

  19. Automatic procedure for mass and charge identification of light isotopes detected in CsI(Tl) of the GARFIELD apparatus

    Energy Technology Data Exchange (ETDEWEB)

    Morelli, L.; Bruno, M.; Baiocco, G. [Dipartimento di Fisica dell' Universita and INFN, Bologna (Italy); Bardelli, L.; Barlini, S.; Bini, M.; Casini, G. [Dipartimento di Fisica dell' Universita and INFN, Firenze (Italy); D' Agostino, M., E-mail: dagostino@bo.infn.i [Dipartimento di Fisica dell' Universita and INFN, Bologna (Italy); Degerlier, M.; Gramegna, F. [INFN, Laboratori Nazionali di Legnaro (Italy); Kravchuk, V.L. [Dipartimento di Fisica dell' Universita and INFN, Bologna (Italy); INFN, Laboratori Nazionali di Legnaro (Italy); Marchi, T. [Dipartimento di Fisica dell' Universita, Padova, ItalyNUCL-EX Collaboration (Italy); INFN, Laboratori Nazionali di Legnaro (Italy); Pasquali, G.; Poggi, G. [Dipartimento di Fisica dell' Universita and INFN, Firenze (Italy)

    2010-08-21

    Mass and charge identification of light charged particles detected with the 180 CsI(Tl) detectors of the GARFIELD apparatus is presented. A 'tracking' method to automatically sample the Z and A ridges of 'Fast-Slow' histograms is developed. An empirical analytic identification function is used to fit correlations between Fast and Slow, in order to determine, event by event, the atomic and mass numbers of the detected charged reaction products. A summary of the advantages of the proposed method with respect to 'hand-based' procedures is reported.

  20. 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...

  1. 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.

  2. Automatic instrument for chemical processing to detect microorganism in biological samples by measuring light reactions

    Science.gov (United States)

    Kelbaugh, B. N.; Picciolo, G. L.; Chappelle, E. W.; Colburn, M. E. (Inventor)

    1973-01-01

    An automated apparatus is reported for sequentially assaying urine samples for the presence of bacterial adenosine triphosphate (ATP) that comprises a rotary table which carries a plurality of sample containing vials and automatically dispenses fluid reagents into the vials preparatory to injecting a light producing luciferase-luciferin mixture into the samples. The device automatically measures the light produced in each urine sample by a bioluminescence reaction of the free bacterial adenosine triphosphate with the luciferase-luciferin mixture. The light measured is proportional to the concentration of bacterial adenosine triphosphate which, in turn, is proportional to the number of bacteria present in the respective urine sample.

  3. Automatic detection of oesophageal intubation based on ventilation pressure waveforms shows high sensitivity and specificity in patients with pulmonary disease

    NARCIS (Netherlands)

    Kalmar, Alain F.; Absalom, Anthony; Rombouts, Pieter; Roets, Jelle; Dewaele, Frank; Verdonck, Pascal; Stemerdink, Arjanne; Zijlstra, Jan G.; Monsieurs, Koenraad G.

    2016-01-01

    Background: Unrecognised endotracheal tube misplacement in emergency intubations has a reported incidence of up to 17%. Current detection methods have many limitations restricting their reliability and availability in these circumstances. There is therefore a clinical need for a device that is small

  4. Automatic landmark detection and face recognition for side-view face images

    NARCIS (Netherlands)

    Santemiz, Pinar; Spreeuwers, Luuk J.; 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

  5. 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

    2011-01-01

    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…

  6. Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images.

    Science.gov (United States)

    Ding, Weiguang; Young, Mei; Bourgault, Serge; Lee, Sieun; Albiani, David A; Kirker, Andrew W; Forooghian, Farzin; Sarunic, Marinko V; Merkur, Andrew B; Beg, Mirza Faisal

    2013-01-01

    Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Subretinal fluid (SRF) and sub-retinal pigment epithelium (sub-RPE) fluid are signs of AMD and can be detected in optical coherence tomography images. However, manual detection and segmentation of SRFs and sub-RPE fluids are laborious and time consuming. In this paper, a novel pipeline is proposed for automatic detection of SRFs and sub-RPE fluids. First, top and bottom layers of retina are segmented using a graph cut method. Then, a Split Bregman-based segmentation method is used to segment dark regions between layers. These segmented regions are considered as potential fluid candidates, on which a set of features are generated. After that, a random forest classifier is trained to distinguish between the true fluid regions from the falsely detected fluid regions. This method shows reasonable performance in a leave-one-out evaluation using a dataset from 21 patients.

  7. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

    Science.gov (United States)

    Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah

    2009-01-01

    Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  8. Automatic Detection of Omega Signals Captured by the Poynting Flux Analyzer (PFX on Board the Akebono Satellite

    Directory of Open Access Journals (Sweden)

    Made Agus Dwi Suarjaya

    2016-10-01

    Full Text Available The Akebono satellite was launched in 1989 to observe the Earth’s magnetosphere and plasmasphere. Omega was a navigation system with 8 ground stations transmitter and had transmission pattern that repeats every 10 s. From 1989 to 1997, the PFX on board the Akebono satellite received signals at 10.2 kHz from these stations. Huge amounts of PFX data became valuable for studying the propagation characteristics of VLF waves in the ionosphere and plasmasphere. In this study, we introduce a method for automatic detection of Omega signals from the PFX data in a systematic way, it involves identifying a transmission station, calculating the delay time, and estimating the signal intensity. We show the reliability of the automatic detection system where we able to detect the omega signal and confirmed its propagation to the opposite hemisphere along the Earth’s magnetic field lines. For more than three years (39 months, we detected 43,734 and 111,049 signals in the magnetic and electric field, respectively, and demonstrated that the proposed method is powerful enough for the statistical analyses.

  9. Automatic Detection of Omega Signals Captured by the Poynting Flux Analyzer (PFX) on Board the Akebono Satellite

    CERN Document Server

    Suarjaya, I Made Agus Dwi; Goto, Yoshitaka

    2016-01-01

    The Akebono satellite was launched in 1989 to observe the Earth's magnetosphere and plasmasphere. Omega was a navigation system with 8 ground stations transmitter and had transmission pattern that repeats every 10 s. From 1989 to 1997, the PFX on board the Akebono satellite received signals at 10.2 kHz from these stations. Huge amounts of PFX data became valuable for studying the propagation characteristics of VLF waves in the ionosphere and plasmasphere. In this study, we introduce a method for automatic detection of Omega signals from the PFX data in a systematic way, it involves identifying a transmission station, calculating the delay time, and estimating the signal intensity. We show the reliability of the automatic detection system where we able to detect the omega signal and confirmed its propagation to the opposite hemisphere along the Earth's magnetic field lines. For more than three years (39 months), we detected 43,734 and 111,049 signals in the magnetic and electric field, respectively, and demons...

  10. Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search

    CERN Document Server

    Giveki, Davar; Bahmanyar, GholamReza; Khademian, Younes

    2012-01-01

    Diabetes is a major health problem in both developing and developed countries and its incidence is rising dramatically. In this study, we investigate a novel automatic approach to diagnose Diabetes disease based on Feature Weighted Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The proposed model consists of three stages: Firstly, PCA is applied to select an optimal subset of features out of set of all the features. Secondly, Mutual Information is employed to construct the FWSVM by weighting different features based on their degree of importance. Finally, since parameter selection plays a vital role in classification accuracy of SVMs, MCS is applied to select the best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy on UCI dataset. The experimental results demonstrate that our method outperforms the previous methods by not only giving more accurate results but also significantly speeding up the classification procedure.

  11. 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.

  12. EVIDENCE BASED INCIDENCE OF TUBAL FACTOR IN SECONDARY INFERTILITY AS DETECTED BY HYSTEROSALPINGOGRAPHY IN WESTERN MAHARASHTRA

    Directory of Open Access Journals (Sweden)

    Anil

    2016-05-01

    Full Text Available BACKGROUND It is documented that 15% of all women experience primary or secondary infertility at one point in time in their reproductive life. Tubal causes of infertility account for 35 to 40% of causes of infertility. HSG is still a commonly used investigation in the evaluation of the female genital tract and the main indication for the HSG is infertility. AIMS  To find out incidence of tubal factor in secondary infertility in Western Maharashtra population.  To establish reliability of Hysterosalpingography in evaluating tubal status. MATERIALS AND METHOD A retrospective study of 464 hysterosalpingographies of women having secondary infertility was done over period of two years. The patients having tubal defects were further studied and statistically analysed. Statistical analysis was performed with the SPSS computer software, version 17.0. Results were presented in tables and graphs. RESULTS  Hysterosalpingography has proved to be an ideal (or ‘gold standard’ test to detect tubal abnormalities in infertile women.  The commonest structural cause of infertility in Western Maharashtra as per this study was bilateral tubal blockage and was commoner in patients with secondary infertility. CONCLUSIONS Evaluation of tubal patency and tubal integrity is a key component of the diagnostic work-up in infertile couples. In conclusion, bilateral tubal occlusion remains the major tubal pathology in female infertility in Western Maharashtra. Tubal blockages with subsequent tubal factor infertility are still common among infertile couples. This may probably be due to chronic pelvic inflammatory disease or pelvic infection following sexually transmitted infections, mismanaged pregnancies and septic abortions, since the majority of the women presented with secondary infertility. Measures to prevent the occurrence of these infections should be paramount.

  13. Sporadic incidence of Fascioliasis detected during Hepatobiliary procedures: A study of 18 patients from Sulaimaniyah governorate

    Directory of Open Access Journals (Sweden)

    Hawramy Tahir Abdullah Hussein

    2012-12-01

    Full Text Available Abstract Background Fascioliasis is an often-neglected zoonotic disease and currently is an emerging infection in Iraq. Fascioliasis has two distinct phases, an acute phase, exhibiting the hepatic migratory stage of the fluke’s life cycle, and a chronic biliary phase manifested with the presence of the parasite in the bile ducts through hepatic tissue. The incidence of Fascioliasis in Sulaimaniyah governorate was unexpected observation. We believe that shedding light on this disease in our locality will increase our physician awareness and experience in early detection, treatment in order to avoid unnecessary surgeries. Findings We retrospectively evaluated this disease in terms of the demographic features, clinical presentations, and managements by reviewing the medical records of 18 patients, who were admitted to the Sulaimani Teaching Hospital and Kurdistan Centre for Gastroenterology and Hepatology. Patients were complained from hepatobiliary and/or upper gastrointestinal symptoms and diagnosed accidentally with Fascioliasis during hepatobiliary surgeries and ERCP by direct visualization of the flukes and stone analysis. Elevated liver enzymes, white blood cells count and eosinophilia were notable laboratory indices. The dilated CBD, gallstones, liver cysts and abscess were found common in radiological images. Fascioliasis diagnosed during conventional surgical CBD exploration and choledochodoudenostomy, open cholecystectomy, surgical drainage of liver abscess, ERCP and during gallstone analysis. Conclusion Fascioliasis is indeed an emerging disease in our locality, but it is often underestimated and ignored. We recommend the differential diagnosis of patients suffering from Rt. Hypochondrial pain, fever and eosinophilia. The watercress ingestion was a common factor in patient’s history.

  14. 自动导航探测机器人设计%Design of automatic navigation tracked detection robot

    Institute of Scientific and Technical Information of China (English)

    杨久红; 王小增; 李明庭; 刘祖强; 黄泽鹏

    2012-01-01

    The main working principle of the GPS automatic navigation tracked detection robot (GANTDR) is illustrated. The mechanical structure and the hardware circuits which is composed of the data acquisition, the data wireless send and receive, the GANTDR motor drive circuit are designed. The realization method of automatic navigation system under the Lab VIEW environment is realized. The result of experiment indicates thai the absolute error of automatic navigation is 1.086 m.and the relative error is 4.34%. The GANTDR can replace humanity to accomplish some danger works.%阐述了GPS自动导航的履带式探测机器人的工作原理,设计并制作了机器人机械结构以及数据采集、数据的无线发送接收、机器人电机驱动电路,给出了基于虚拟仪器环境下的自动导航系统的实现方法.测试结果的绝对误差平均值为1.085 m,相对误差平均值为4.34%.该自动导航探测机器人可以替代人完成一些危险的工作.

  15. Incidence analyses and space-time cluster detection of hepatitis C in Fujian Province of China from 2006 to 2010.

    Directory of Open Access Journals (Sweden)

    Shunquan Wu

    Full Text Available BACKGROUND: There is limited epidemiologic information about the incidence of hepatitis C in China, and few studies have applied space-time scan statistic to detect clusters of hepatitis C and made adjustment for temporal trend and relative risk of regions. METHODOLOGY AND PRINCIPAL FINDINGS: We analyzed the temporal changes and characteristics of incidence of hepatitis C in Fujian Province from 2006 through 2010. The discrete Poisson model of space-time scan statistic was chosen for cluster detection. Data on new cases of hepatitis C were obtained from the Center for Disease Control and Prevention of Fujian Province. Between 2006 and 2010, there was an annualized increase in the incidence of hepatitis C of 23.0 percent, from 928 cases (2.63 per 100,000 persons to 2,180 cases (6.01 per 100,000 persons. The incidence among women increased more rapidly. The cumulative incidence showed that people who were over 60 years had the highest risk to suffer hepatitis C (52.51 per 100,000 persons, and women had lower risk compared to men (OR=0.69. Putian had the highest cumulative incidence among all the regions (86.95 per 100,000 persons. The most likely cluster was identified in Putian during March to August in 2009 without adjustment, but it shifted to three contiguous cities with a two-month duration after adjustment for temporal trend and relative risk of regions. CONCLUSIONS/SIGNIFICANCE: The incidence of hepatitis C is increasing in Fujian Province, and women are at a more rapid pace. The space-time scan statistic is useful as a screening tool for clusters of hepatitis C, with adjustment for temporal trend and relative risk of regions recommended.

  16. Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

    OpenAIRE

    Cuingnet, Rémi; Prevost, Raphaël; Lesage, David; Cohen, Laurent D.; Mory, Benoît; Ardon, Roberto

    2012-01-01

    International audience; Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and elds of view. By combining and re ning state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a co...

  17. Combining contour detection algorithms for the automatic extraction of the preparation line from a dental 3D measurement

    Science.gov (United States)

    Ahlers, Volker; Weigl, Paul; Schachtzabel, Hartmut

    2005-04-01

    Due to the increasing demand for high-quality ceramic crowns and bridges, the CAD/CAM-based production of dental restorations has been a subject of intensive research during the last fifteen years. A prerequisite for the efficient processing of the 3D measurement of prepared teeth with a minimal amount of user interaction is the automatic determination of the preparation line, which defines the sealing margin between the restoration and the prepared tooth. Current dental CAD/CAM systems mostly require the interactive definition of the preparation line by the user, at least by means of giving a number of start points. Previous approaches to the automatic extraction of the preparation line rely on single contour detection algorithms. In contrast, we use a combination of different contour detection algorithms to find several independent potential preparation lines from a height profile of the measured data. The different algorithms (gradient-based, contour-based, and region-based) show their strengths and weaknesses in different clinical situations. A classifier consisting of three stages (range check, decision tree, support vector machine), which is trained by human experts with real-world data, finally decides which is the correct preparation line. In a test with 101 clinical preparations, a success rate of 92.0% has been achieved. Thus the combination of different contour detection algorithms yields a reliable method for the automatic extraction of the preparation line, which enables the setup of a turn-key dental CAD/CAM process chain with a minimal amount of interactive screen work.

  18. TU-G-204-02: Automatic Sclerotic Bone Metastases Detection in the Pelvic Region From Dual Energy CT

    Energy Technology Data Exchange (ETDEWEB)

    Fehr, D; Schmidtlein, C; Hwang, S; Deasy, J; Veeraraghavan, H [Memorial Sloan Kettering Cancer Center, New York, NY (United States)

    2015-06-15

    Purpose: To automatically detect sclerotic bone metastases in the pelvic region using dual energy computed tomography (DECT). Methods: We developed a two stage algorithm to automatically detect sclerotic bone metastases in the pelvis from DECT for patients with multiple bone metastatic lesions and with hip implants. The first stage consists of extracting the bone and marrow regions by using a support vector machine (SVM) classifier. We employed a novel representation of the DECT images using multi-material decomposition, which represents each voxel as a mixture of different physical materials (e.g. bone+water+fat). Following the extraction of bone and marrow, in the second stage, a bi -histogram equalization method was employed to enhance the contrast to reveal the bone metastases. Next, meanshift segmentation was performed to separate the voxels by their intensity levels. Finally, shape-based filtering was performed to extract the possible locations of the metastatic lesions using multiple shape criteria. We used the following shape parameters: area, eccentricity, major and minor axis, perimeter and skeleton. Results: A radiologist with several years of experience with DECT manually labeled 64 regions consisting of metastatic lesions from 10 different patients. However, the patients had many more metastasic lesions throughout the pelvis. Our method correctly identified 46 of the marked 64 regions (72%). In addition, our method also identified several other lesions, which can then be validated by the radiologist. The missed lesions were typically very large elongated regions consisting of several islands of very small (<4mm) lesions. Conclusion: We developed an algorithm to automatically detect sclerotic lesions in the pelvic region from DECT. Preliminary assessment shows that our algorithm generated lesions agreeing with the radiologist generated candidate regions. Furthermore, our method reveals additional lesions that can be inspected by the radiologist, thereby

  19. 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.

  20. Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.

    Science.gov (United States)

    Mariani, Sara; Manfredini, Elena; Rosso, Valentina; Grassi, Andrea; Mendez, Martin O; Alba, Alfonso; Matteucci, Matteo; Parrino, Liborio; Terzano, Mario G; Cerutti, Sergio; Bianchi, Anna M

    2012-04-01

    This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

  1. 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.

  2. Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy.

    Science.gov (United States)

    Wang, Rui; Li, Chao; Wang, Jie; Wei, Xiaoer; Li, Yuehua; Hui, Chun; Zhu, Yuemin; Zhang, Su

    2014-12-01

    White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.

  3. Automatic aorta segmentation and valve landmark detection in C-arm CT: application to aortic valve implantation.

    Science.gov (United States)

    Zheng, Yefeng; John, Matthias; Liao, Rui; Boese, Jan; Kirschstein, Uwe; Georgescu, Bogdan; Zhou, S Kevin; Kempfert, Jörg; Walther, Thomas; Brockmann, Gernot; Comaniciu, Dorin

    2010-01-01

    C-arm CT is an emerging imaging technique in transcatheter aortic valve implantation (TAVI) surgery. Automatic aorta segmentation and valve landmark detection in a C-arm CT volume has important applications in TAVI by providing valuable 3D measurements for surgery planning. Overlaying 3D segmentation onto 2D real time fluoroscopic images also provides critical visual guidance during the surgery. In this paper, we present a part-based aorta segmentation approach, which can handle aorta structure variation in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three aortic hinge points, three commissure points, and two coronary ostia) are also detected automatically in our system. Under the guidance of the detected landmarks, the physicians can deploy the prosthetic valve properly. Our approach is robust under variations of contrast agent. Taking about 1.4 seconds to process one volume, it is also computationally efficient.

  4. Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level

    Directory of Open Access Journals (Sweden)

    Pedro J. Navarro

    2016-07-01

    Full Text Available This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL, based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon’s entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

  5. Ability of automatic detection of conflict between planes in flight simulations with the help of expert system

    Directory of Open Access Journals (Sweden)

    Naděžda Bartošová

    2015-01-01

    Full Text Available This article examines options for applying expert systems for the needs of identification of conflict situations between planes in flight simulations, which are applied during basic training of air traffic controllers. It focuses on the conditions for basic training of military air traffic controllers and presents the use of rule systems to automatic detection of conflict between planes within a basic training polygon. The system of rules is a part of the expert system, consisting of realisation of tasks for identifying optimum resolution of conflict situations in selected types of simulations.

  6. Automatic aorta segmentation and valve landmark detection in C-arm CT for transcatheter aortic valve implantation.

    Science.gov (United States)

    Zheng, Yefeng; John, Matthias; Liao, Rui; Nöttling, Alois; Boese, Jan; Kempfert, Jörg; Walther, Thomas; Brockmann, Gernot; Comaniciu, Dorin

    2012-12-01

    Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.

  7. Poster — Thur Eve — 70: Automatic lung bronchial and vessel bifurcations detection algorithm for deformable image registration assessment

    Energy Technology Data Exchange (ETDEWEB)

    Labine, Alexandre; Carrier, Jean-François; Bedwani, Stéphane [Centre hospitalier de l' Université de Montréal (Canada); Chav, Ramnada; De Guise, Jacques [Laboratoire de recherche en imagerie et d' orthopédie-CRCHUM, École de technologie supérieure (Canada)

    2014-08-15

    Purpose: To investigate an automatic bronchial and vessel bifurcations detection algorithm for deformable image registration (DIR) assessment to improve lung cancer radiation treatment. Methods: 4DCT datasets were acquired and exported to Varian treatment planning system (TPS) EclipseTM for contouring. The lungs TPS contour was used as the prior shape for a segmentation algorithm based on hierarchical surface deformation that identifies the deformed lungs volumes of the 10 breathing phases. Hounsfield unit (HU) threshold filter was applied within the segmented lung volumes to identify blood vessels and airways. Segmented blood vessels and airways were skeletonised using a hierarchical curve-skeleton algorithm based on a generalized potential field approach. A graph representation of the computed skeleton was generated to assign one of three labels to each node: the termination node, the continuation node or the branching node. Results: 320 ± 51 bifurcations were detected in the right lung of a patient for the 10 breathing phases. The bifurcations were visually analyzed. 92 ± 10 bifurcations were found in the upper half of the lung and 228 ± 45 bifurcations were found in the lower half of the lung. Discrepancies between ten vessel trees were mainly ascribed to large deformation and in regions where the HU varies. Conclusions: We established an automatic method for DIR assessment using the morphological information of the patient anatomy. This approach allows a description of the lung's internal structure movement, which is needed to validate the DIR deformation fields for accurate 4D cancer treatment planning.

  8. A remotely controlled, semi-automatic target system for Rutherford backscattering spectrometry and elastic recoil detection analyses of polymeric membrane samples

    Energy Technology Data Exchange (ETDEWEB)

    Attayek, P.J. [Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7431 (United States); Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7575 (United States); Meyer, E.S.; Lin, L. [Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7431 (United States); Rich, G.C.; Clegg, T.B. [Triangle Universities Nuclear Laboratory (TUNL), Durham, NC 27708-0308 (United States); Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coronell, O., E-mail: coronell@unc.edu [Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7431 (United States)

    2012-06-01

    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 Trade-Mark-Sign 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 {sup 4}He{sup 2+} analysis beam are provided.

  9. 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.

  10. Integrated microfluidic system with automatic sampling for permanent molecular and antigen-based detection of CBRNE-related pathogens

    Science.gov (United States)

    Becker, Holger; Schattschneider, Sebastian; Klemm, Richard; Hlawatsch, Nadine; Gärtner, Claudia

    2015-03-01

    The continuous monitoring of the environment for lethal pathogens is a central task in the field of biothreat detection. Typical scenarios involve air-sampling in locations such as public transport systems or large public events and a subsequent analysis of the samples by a portable instrument. Lab-on-a-chip technologies are one of the promising technological candidates for such a system. We have developed an integrated microfluidic system with automatic sampling for the detection of CBRNE-related pathogens. The chip contains a two-pronged analysis strategy, on the one hand an immunological track using antibodies immobilized on a frit and a subsequent photometric detection, on the other hand a molecular biology approach using continuous-flow PCR with a fluorescence end-point detection. The cartridge contains two-component molded rotary valve to allow active fluid control and switching between channels. The accompanying instrument contains all elements for fluidic and valve actuation, thermal control, as well as the two detection modalities. Reagents are stored in dedicated reagent packs which are connected directly to the cartridge. With this system, we have been able to demonstrate the detection of a variety of pathogen species.

  11. Machine learning approach for automatic quality criteria detection of health web pages.

    Science.gov (United States)

    Gaudinat, Arnaud; Grabar, Natalia; Boyer, Célia

    2007-01-01

    The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

  12. Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

    Directory of Open Access Journals (Sweden)

    Yevgeny Beiderman

    2014-12-01

    Full Text Available In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented.

  13. Automatic detection and characterization of pulmonary nodules in thoracic CT scans

    NARCIS (Netherlands)

    Jacobs, C.

    2015-01-01

    Lung cancer is the most deadly cancer in both men and women. This can be largely attributed to the fact that lung cancer is usually detected in a late stage. If the disease is detected in an early stage, the survival rate is much better. Therefore, early detection of lung cancer, in which it is stil

  14. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jie Dou

    2015-04-01

    Full Text Available This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA, a genetic algorithm (GA, and a case-based reasoning (CBR technique. It consists of three main phases: (1 image processing and multi-image segmentation; (2 feature optimization; and (3 detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87 demonstrates higher classification performance than the stand-alone OOIA (0.75 method for detecting landslides. The area under curve (AUC value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

  15. 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.

  16. An improved automatic detection method for earthquake-collapsed buildings from ADS40 image

    Institute of Scientific and Technical Information of China (English)

    GUO HuaDong; LU LinLin; MA JianWen; PESARESI Martino; YUAN FangYan

    2009-01-01

    Earthquake-collapsed building identification is important in earthquake damage assessment and is evidence for mapping seismic intensity. After the May 12th Wenchuan major earthquake occurred,experts from CEODE and IPSC collaborated to make a rapid earthquake damage assessment. A crucial task was to identify collapsed buildings from ADS40 images in the earthquake region. The difficulty was to differentiate collapsed buildings from concrete bridges,dry gravels,and landslide-induced rolling stones since they had a similar gray level range in the image. Based on the IPSC method,an improved automatic identification technique was developed and tested in the study area,a portion of Beichuan County. Final results showed that the technique's accuracy was over 95%. Procedures and results of this experiment are presented in this article. Theory of this technique indicates that it could be applied to collapsed building identification caused by other disasters.

  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 Real-Time Embedded QRS Complex Detection for a Novel Patch-Type Electrocardiogram Recorder

    DEFF Research Database (Denmark)

    Saadi, Dorthe Bodholt; Tanev, George; Flintrup, Morten

    2015-01-01

    Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (ECGs......) obtained from ambulatory long-term recordings. The design of novel patch-type ECG recorders has increased the accessibility of these long-term recordings. In many applications, it is furthermore an advantage for these devices that the recorded ECGs can be analyzed automatically in real time. The purpose....... The design and optimization of the algorithm was performed on two different databases: The MIT-BIH arrhythmia database ( $Se=99.90$ %, $P^{+}=99.87$ ) and a private ePatch training database ( $Se=99.88$ %, $P^{+}=99.37$ %). The offline validation was conducted on the European ST-T database ( $Se=99.84$ %, $P...

  19. Detection of irradiation of meats by HPLC determination for {omicron}-tyrosine using novel LASER fluorometric detection with automatic pre-column reaction

    Energy Technology Data Exchange (ETDEWEB)

    Miyahara, Makoto [National Inst. of Health Sciences, Tokyo (Japan); Ito, Hitoshi; Saito, Akiko; Nagasawa, Taeko; Kariya, Mari; Toyoda, Masatake; Saito, Yukio

    2000-08-01

    An {omicron}-Tyrosine method for detection of irradiation of foods was studied by HPLC using a novel light amplification by stimulated emission of radiation (LASER) fluorometric detection system with pre-column reaction. Sample was prepared and purified by eliminating fat and sugars using a mixture of acetone and chloroform, and then the purified protein was hydrolyzed using hydrochloric acid at 110 deg C for 24 h in a vacuum. The sample was reacted with 4-fluoro-7-nitrobenzofurazan (NBD-F) reagent by an automatic pipetting system and was introduced into the HPLC system. Irradiated chicken, pork, beef, and tuna were examined by irradiating at 0, 1, 5, 10 kGy. Irradiation of chicken and pork irradiated at or over 10 kGy was successfully detected, but that of beef and tuna were more difficult to detect. After 3 months storage at -20 deg C, the irradiation was still detectable in chicken irradiated at 10 kGy. Thus this detection procedure can be used to detect irradiation in some chilled meats irradiated at 10 kGy. Non-irradiated {omicron}-tyrosine formation and reduction of {omicron}-tyrosine by hydroxylation are also discussed. (author)

  20. Full automatic fiducial marker detection on coil arrays for accurate instrumentation placement during MRI guided breast interventions

    Science.gov (United States)

    Filippatos, Konstantinos; Boehler, Tobias; Geisler, Benjamin; Zachmann, Harald; Twellmann, Thorsten

    2010-02-01

    With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

  1. A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996 – 2003

    Directory of Open Access Journals (Sweden)

    Wheeler David C

    2007-03-01

    Full Text Available Abstract Background Spatial cluster detection is an important tool in cancer surveillance to identify areas of elevated risk and to generate hypotheses about cancer etiology. There are many cluster detection methods used in spatial epidemiology to investigate suspicious groupings of cancer occurrences in regional count data and case-control data, where controls are sampled from the at-risk population. Numerous studies in the literature have focused on childhood leukemia because of its relatively large incidence among children compared with other malignant diseases and substantial public concern over elevated leukemia incidence. The main focus of this paper is an analysis of the spatial distribution of leukemia incidence among children from 0 to 14 years of age in Ohio from 1996–2003 using individual case data from the Ohio Cancer Incidence Surveillance System (OCISS. Specifically, we explore whether there is statistically significant global clustering and if there are statistically significant local clusters of individual leukemia cases in Ohio using numerous published methods of spatial cluster detection, including spatial point process summary methods, a nearest neighbor method, and a local rate scanning method. We use the K function, Cuzick and Edward's method, and the kernel intensity function to test for significant global clustering and the kernel intensity function and Kulldorff's spatial scan statistic in SaTScan to test for significant local clusters. Results We found some evidence, although inconclusive, of significant local clusters in childhood leukemia in Ohio, but no significant overall clustering. The findings from the local cluster detection analyses are not consistent for the different cluster detection techniques, where the spatial scan method in SaTScan does not find statistically significant local clusters, while the kernel intensity function method suggests statistically significant clusters in areas of central, southern

  2. 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 Nort...

  3. Automatic player detection and recognition in images using AdaBoost

    NARCIS (Netherlands)

    Mahmood, Zahid; Ali, Tauseef; Khattak, Shadid

    2012-01-01

    In this work we developed an augmented reality sports broadcasting application for enhanced end-user experience. The proposed system consists of three major steps. In the first step each player is detected using AdaBoost Algorithm. In second step, same algorithm is used to detect face in each player

  4. 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…

  5. Automatic Detection and Tracking of CMEs II: Multiscale Filtering of Coronagraph Data

    CERN Document Server

    Byrne, Jason P; Habbal, Shadia R; Gallagher, Peter T; 10.1088/0004-637X/752/2/145

    2012-01-01

    Studying 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 SOHO, STEREO, and future coronagraph missions, also makes manual cataloguing 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 cata- loguing 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 catalogues. It works by first employing the dynamic CME separation technique outlined in a companion paper, and then characterising 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...

  6. Incidence of thromboembolism following detection by trans-oesophageal echocardiography of left atrial thrombus

    Directory of Open Access Journals (Sweden)

    Ciara Mahon

    2015-09-01

    Conclusion: This is the only study to date that has looked at the incidence of ischemic stroke following a confirmed LAA thrombus, LA thrombus or pre-thrombus state. This single centre study found low stroke rates over a six month follow-up period in patients with a confirmed LAA thrombus, LA thrombus or pre-thrombus state and optimization of OAC. Larger studies would be required to confirm these findings.

  7. Vuosaari Harbour Road Tunnel Traffic Management and Incident Detection System Design Issues

    Directory of Open Access Journals (Sweden)

    Caj Holm

    2006-11-01

    Full Text Available Helsinki is constructing in Vuosaari a new modem and effectivecargo harbour. All cargo harbour activities will be concentratedthere. The total project includes the harbour, a logisticsarea, traffic connections (road, railway and fairway and aBusiness Park. The road connection goes through the Porvarinlahtiroad tunnel. The harbour will commence operatingin 2008. This paper gives an oveTView of the tunnel design phasefunctional studies and risk analysis tunnel incident detectionsystem design issues and some specific environmental featuresof the tunnel.

  8. Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2009-03-01

    Full Text Available Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV, positive likelihood ratio (PLR and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

  9. B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

    Science.gov (United States)

    Bueno, G.; Sánchez, S.; Ruiz, M.

    2006-10-01

    Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

  10. Antenna system analysis and design for automatic detection and real-time tracking of electron Bernstein waves in FTU

    Science.gov (United States)

    Bin, W.; Alessi, E.; Bruschi, A.; D'Arcangelo, O.; Figini, L.; Galperti, C.; Garavaglia, S.; Granucci, G.; Moro, A.

    2014-05-01

    The algorithm for the automatic control of the new front steering antenna of the Frascati Tokamak Upgrade device has been improved, in view of forthcoming experiments aimed at testing the mode conversion of electron cyclotron waves at a frequency of 140 GHz. The existing antenna system has been prepared to provide two-point real-time measurements of electron Bernstein waves and to allow real-time tracking of the optimal conversion region. This required an accurate analysis of the antenna to minimize the risk of a mechanical damage of the movable launching mirrors, when accessing the high toroidal launching angles needed for this kind of experiment. A detailed description is presented of the work carried out to safely reach and validate the desired range of steering angles, which include the region of interest, and a technique is proposed to track and chase the correct line of sight for electron Bernstein waves detection during the shot.

  11. 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...

  12. An automatic algorithm for detecting stent endothelialization from volumetric optical coherence tomography datasets

    Science.gov (United States)

    Bonnema, Garret T.; O'Halloran Cardinal, Kristen; Williams, Stuart K.; Barton, Jennifer K.

    2008-06-01

    Recent research has suggested that endothelialization of vascular stents is crucial to reducing the risk of late stent thrombosis. With a resolution of approximately 10 µm, optical coherence tomography (OCT) may be an appropriate imaging modality for visualizing the vascular response to a stent and measuring the percentage of struts covered with an anti-thrombogenic cellular lining. We developed an image analysis program to locate covered and uncovered stent struts in OCT images of tissue-engineered blood vessels. The struts were found by exploiting the highly reflective and shadowing characteristics of the metallic stent material. Coverage was evaluated by comparing the luminal surface with the depth of the strut reflection. Strut coverage calculations were compared to manual assessment of OCT images and epi-fluorescence analysis of the stented grafts. Based on the manual assessment, the strut identification algorithm operated with a sensitivity of 93% and a specificity of 99%. The strut coverage algorithm was 81% sensitive and 96% specific. The present study indicates that the program can automatically determine percent cellular coverage from volumetric OCT datasets of blood vessel mimics. The program could potentially be extended to assessments of stent endothelialization in native stented arteries.

  13. Correlation between automatic detection of malaria on thin film and experts' parasitaemia scores

    Science.gov (United States)

    Sunarko, Budi; Williams, Simon; Prescott, William R.; Byker, Scott M.; Bottema, Murk J.

    2017-03-01

    An algorithm was developed to diagnose the presence of malaria and to estimate the depth of infection by automatically counting individual normal and infected erythrocytes in images of thin blood smears. During the training stage, the parameters of the algorithm were optimized to maximize correlation with estimates of parasitaemia from expert human observers. The correlation was tested on a set of 1590 images from seven thin film blood smears. The correlation between the results from the algorithm and expert human readers was r = 0.836. Results indicate that reliable estimates of parasitaemia may be achieved by computational image analysis methods applied to images of thin film smears. Meanwhile, compared to biological experiments, the algorithm fitted well the three high parasitaemia slides and a mid-level parasitaemia slide, and overestimated the three low parasitaemia slides. To improve the parasitaemia estimation, the sources of the overestimation were identified. Emphasis is laid on the importance of further research in order to identify parasites independently of their erythrocyte hosts

  14. Automatic Meter Reading using Power Line Signaling and Voltage Zero-crossing Detection

    Directory of Open Access Journals (Sweden)

    C.L. Vasu

    2015-06-01

    Full Text Available In India, the electric power transmission and distribution loss is very high, about 7% in transmission and 26% in distribution. Though deployment of automated meter reading system will reduce losses, particularly in distribution, penetration of automated meter reading is low due to high costs involved. World over, the Two-Way Automatic Communications System (TWACS is the most widely used power line communications technology offering two-way communication between substation and end users. The TWACS introduces disturbance on the power system voltage for short durations near zero-crossing to generate the outbound (from substation to end user signal. To generate the inbound (from end user to substation signal, short duration current pulses are introduced, near voltage zero-crossings. Information is generated as a sequential combination of voltage disturbances for the outbound signal and current pulses for the inbound signal. The current study proposes a low-cost modification of the TWACS to reduce voltage and current harmonics. The proposed system has been modelled and simulated using SIMULINK/SIMPOWER Systems. The simulation results show that there is a reduction in voltage harmonics from 0.84 to 0.17% and in current harmonics from 2.07 to 1.10%.

  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. Precise automatic image coregistration tools to enable pixel-level change detection Project

    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...

  17. Automatic Detection of Frontal Face Midline by Chain-coded Merlin-Farber Hough Trasform

    Science.gov (United States)

    Okamoto, Daichi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka

    We propose a novel approach for detection of the facial midline (facial symmetry axis) from a frontal face image. The facial midline has several applications, for instance reducing computational cost required for facial feature extraction (FFE) and postoperative assessment for cosmetic or dental surgery. The proposed method detects the facial midline of a frontal face from an edge image as the symmetry axis using the Merlin-Faber Hough transformation. And a new performance improvement scheme for midline detection by MFHT is present. The main concept of the proposed scheme is suppression of redundant vote on the Hough parameter space by introducing chain code representation for the binary edge image. Experimental results on the image dataset containing 2409 images from FERET database indicate that the proposed algorithm can improve the accuracy of midline detection from 89.9% to 95.1 % for face images with different scales and rotation.

  18. Precise Automatic Image Coregistration Tools to Enable Pixel-Level Change Detection Project

    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...

  19. Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

    OpenAIRE

    Ghaffarian, S.

    2014-01-01

    This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Furth...

  20. A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

    OpenAIRE

    Mehdi Alilou; Vassili Kovalev; Eduard Snezhko; Vahid Taimouri

    2014-01-01

    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 wit...

  1. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.

    Science.gov (United States)

    Casilari, Eduardo; Oviedo-Jiménez, Miguel A

    2015-01-01

    Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of 'false

  2. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    Full Text Available Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone, this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope. In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication. The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements. The proposed system was compared with the cases where only one device (the smartphone or the smartwatch is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio

  3. Automatic Registration of Wide Area Motion Imagery to Vector Road Maps by Exploiting Vehicle Detections.

    Science.gov (United States)

    Elliethy, Ahmed; Sharma, Gaurav

    2016-11-01

    To enrich large-scale visual analytics applications enabled by aerial wide area motion imagery (WAMI), we propose a novel methodology for accurately registering a geo-referenced vector roadmap to WAMI by using the locations of detected vehicles and determining a parametric transform that aligns these locations with the network of roads in the roadmap. Specifically, the problem is formulated in a probabilistic framework, explicitly allowing for spurious detections that do not correspond to on-road vehicles. The registration is estimated via the expectation-maximization (EM) algorithm as the planar homography that minimizes the sum of weighted squared distances between the homography-mapped detection locations and the corresponding closest point on the road network, where the weights are estimated posterior probabilities of detections being on-road vehicles. The weighted distance minimization is efficiently performed using the distance transform with the Levenberg-Marquardt nonlinear least-squares minimization procedure, and the fraction of spurious detections is estimated within the EM framework. The proposed method effectively sidesteps the challenges of feature correspondence estimation, applies directly to different imaging modalities, is robust to spurious detections, and is also more appropriate than feature matching for a planar homography. Results over three WAMI data sets captured by both visual and infrared sensors indicate the effectiveness of the proposed methodology: both visual comparison and numerical metrics for the registration accuracy are significantly better for the proposed method as compared with the existing alternatives.

  4. Semi-automatic detection of Gd-DTPA-saline filled capsules for colonic transit time assessment in MRI

    Science.gov (United States)

    Harrer, Christian; Kirchhoff, Sonja; Keil, Andreas; Kirchhoff, Chlodwig; Mussack, Thomas; Lienemann, Andreas; Reiser, Maximilian; Navab, Nassir

    2008-03-01

    Functional gastrointestinal disorders result in a significant number of consultations in primary care facilities. Chronic constipation and diarrhea are regarded as two of the most common diseases affecting between 2% and 27% of the population in western countries 1-3. Defecatory disorders are most commonly due to dysfunction of the pelvic floor or the anal sphincter. Although an exact differentiation of these pathologies is essential for adequate therapy, diagnosis is still only based on a clinical evaluation1. Regarding quantification of constipation only the ingestion of radio-opaque markers or radioactive isotopes and the consecutive assessment of colonic transit time using X-ray or scintigraphy, respectively, has been feasible in clinical settings 4-8. However, these approaches have several drawbacks such as involving rather inconvenient, time consuming examinations and exposing the patient to ionizing radiation. Therefore, conventional assessment of colonic transit time has not been widely used. Most recently a new technique for the assessment of colonic transit time using MRI and MR-contrast media filled capsules has been introduced 9. However, due to numerous examination dates per patient and corresponding datasets with many images, the evaluation of the image data is relatively time-consuming. The aim of our study was to develop a computer tool to facilitate the detection of the capsules in MRI datasets and thus to shorten the evaluation time. We present a semi-automatic tool which provides an intensity, size 10, and shape-based 11,12 detection of ingested Gd-DTPA-saline filled capsules. After an automatic pre-classification, radiologists may easily correct the results using the application-specific user interface, therefore decreasing the evaluation time significantly.

  5. Portable automatic bioaerosol sampling system for rapid on-site detection of targeted airborne microorganisms.

    Science.gov (United States)

    Usachev, Evgeny V; Pankova, Anna V; Rafailova, Elina A; Pyankov, Oleg V; Agranovski, Igor E

    2012-10-26

    Bioaerosols could cause various severe human and animal diseases and their opportune and qualitative precise detection and control is becoming a significant scientific and technological topic for consideration. Over the last few decades bioaerosol detection has become an important bio-defense related issue. Many types of portable and stationary bioaerosol samplers have been developed and, in some cases, integrated into automated detection systems utilizing various microbiological techniques for analysis of collected microbes. This paper describes a personal sampler used in conjunction with a portable real-time PCR technique. It was found that a single fluorescent dye could be successfully used in multiplex format for qualitative detection of numerous targeted bioaerosols in one PCR tube making the suggested technology a reliable "first alert" device. This approach has been specifically developed and successfully verified for rapid detection of targeted microorganisms by portable PCR devices, which is especially important under field conditions, where the number of microorganisms of interest usually exceeds the number of available PCR reaction tubes. The approach allows detecting targeted microorganisms and triggering some corresponding sanitary and quarantine procedures to localize possible spread of dangerous infections. Following detailed analysis of the sample under controlled laboratory conditions could be used to exactly identify which particular microorganism out of a targeted group has been rapidly detected in the field. It was also found that the personal sampler has a collection efficiency higher than 90% even for small-sized viruses (>20 nm) and stable performance over extended operating periods. In addition, it was found that for microorganisms used in this project (bacteriophages MS2 and T4) elimination of nucleic acids isolation and purification steps during sample preparation does not lead to the system sensitivity reduction, which is extremely

  6. 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.

  7. Automatic Tracking of Active Regions and Detection of Solar Flares in Solar EUV Images

    Science.gov (United States)

    Caballero, C.; Aranda, M. C.

    2014-05-01

    Solar catalogs are frequently handmade by experts using a manual approach or semi-automated approach. The appearance of new tools is very useful because the work is automated. Nowadays it is impossible to produce solar catalogs using these methods, because of the emergence of new spacecraft that provide a huge amount of information. In this article an automated system for detecting and tracking active regions and solar flares throughout their evolution using the Extreme UV Imaging Telescope (EIT) on the Solar and Heliospheric Observatory (SOHO) spacecraft is presented. The system is quite complex and consists of different phases: i) acquisition and preprocessing; ii) segmentation of regions of interest; iii) clustering of these regions to form candidate active regions which can become active regions; iv) tracking of active regions; v) detection of solar flares. This article describes all phases, but focuses on the phases of tracking and detection of active regions and solar flares. The system relies on consecutive solar images using a rotation law to track the active regions. Also, graphs of the evolution of a region and solar evolution are presented to detect solar flares. The procedure developed has been tested on 3500 full-disk solar images (corresponding to 35 days) taken from the spacecraft. More than 75 % of the active regions are tracked and more than 85 % of the solar flares are detected.

  8. 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.

  9. 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.)

  10. Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

    Energy Technology Data Exchange (ETDEWEB)

    Teuton, Jeremy R.; Griswold, Richard L.; Mehdi, Beata L.; Browning, Nigel D.

    2015-09-23

    Precise analysis of both (S)TEM images and video are time and labor intensive processes. As an example, determining when crystal growth and shrinkage occurs during the dynamic process of Li dendrite deposition and stripping involves manually scanning through each frame in the video to extract a specific set of frames/images. For large numbers of images, this process can be very time consuming, so a fast and accurate automated method is desirable. Given this need, we developed software that uses analysis of video compression statistics for detecting and characterizing events in large data sets. This software works by converting the data into a series of images which it compresses into an MPEG-2 video using the open source “avconv” utility [1]. The software does not use the video itself, but rather analyzes the video statistics from the first pass of the video encoding that avconv records in the log file. This file contains statistics for each frame of the video including the frame quality, intra-texture and predicted texture bits, forward and backward motion vector resolution, among others. In all, avconv records 15 statistics for each frame. By combining different statistics, we have been able to detect events in various types of data. We have developed an interactive tool for exploring the data and the statistics that aids the analyst in selecting useful statistics for each analysis. Going forward, an algorithm for detecting and possibly describing events automatically can be written based on statistic(s) for each data type.

  11. Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

    Science.gov (United States)

    Bhatti, Sohail Masood; Khan, Muhammad Salman; Wuth, Jorge; Huenupan, Fernando; Curilem, Millaray; Franco, Luis; Yoma, Nestor Becerra

    2016-09-01

    In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.

  12. Real-time error detection but not error correction drives automatic visuomotor adaptation.

    Science.gov (United States)

    Hinder, Mark R; Riek, Stephan; Tresilian, James R; de Rugy, Aymar; Carson, Richard G

    2010-03-01

    We investigated the role of visual feedback of task performance in visuomotor adaptation. Participants produced novel two degrees of freedom movements (elbow flexion-extension, forearm pronation-supination) to move a cursor towards visual targets. Following trials with no rotation, participants were exposed to a 60 degrees visuomotor rotation, before returning to the non-rotated condition. A colour cue on each trial permitted identification of the rotated/non-rotated contexts. Participants could not see their arm but received continuous and concurrent visual feedback (CF) of a cursor representing limb position or post-trial visual feedback (PF) representing the movement trajectory. Separate groups of participants who received CF were instructed that online modifications of their movements either were, or were not, permissible as a means of improving performance. Feedforward-mediated performance improvements occurred for both CF and PF groups in the rotated environment. Furthermore, for CF participants this adaptation occurred regardless of whether feedback modifications of motor commands were permissible. Upon re-exposure to the non-rotated environment participants in the CF, but not PF, groups exhibited post-training aftereffects, manifested as greater angular deviations from a straight initial trajectory, with respect to the pre-rotation trials. Accordingly, the nature of the performance improvements that occurred was dependent upon the timing of the visual feedback of task performance. Continuous visual feedback of task performance during task execution appears critical in realising automatic visuomotor adaptation through a recalibration of the visuomotor mapping that transforms visual inputs into appropriate motor commands.

  13. Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

    Directory of Open Access Journals (Sweden)

    Kimori Yoshitaka

    2010-07-01

    Full Text Available Abstract Background A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. Results A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Conclusions Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

  14. Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults.

    Science.gov (United States)

    Akl, Ahmad; Snoek, Jasper; Mihailidis, Alex

    2014-01-01

    With a globally aging population, the burden of care of cognitively impaired older adults is becoming increasingly concerning. Instances of Alzheimer's disease and other forms of dementia are becoming ever more frequent. Earlier detection of cognitive impairment offers significant benefits, but remains difficult to do in practice. In this paper, we develop statistical models of the behavior of older adults within their homes using sensor data in order to detect the early onset of cognitive decline. Specifically, we use inhomogenous Poisson processes to model the presence of subjects within different rooms throughout the day in the home using unobtrusive sensing technologies. We compare the distributions learned from cognitively intact and impaired subjects using information theoretic tools and observe statistical differences between the two populations which we believe can be used to help detect the onset of cognitive decline.

  15. Drilling of bone: a robust automatic method for the detection of drill bit break-through.

    Science.gov (United States)

    Ong, F R; Bouazza-Marouf, K

    1998-01-01

    The aim of this investigation is to devise a robust detection method for drill bit break-through when drilling into long bones using an automated drilling system that is associated with mechatronic assisted surgery. This investigation looks into the effects of system compliance and inherent drilling force fluctuation on the profiles of drilling force, drilling force, drilling between successive samples and drill bit rotational speed. It is shown that these effects have significant influences on the bone drilling related profiles and thus on the detection of drill bit break-through. A robust method, based on a Kalman filter, has been proposed. Using a modified Kalman filter, it is possible to convert the profiles of drilling force difference between successive samples and/or the drill bit rotational speed into easily recognizable and more consistent profiles, allowing a robust and repeatable detection of drill bit break-through.

  16. Automatic video shot detection and characterization for content-based video retrieval

    Science.gov (United States)

    Sun, Jifeng; Cui, Songye; Xu, Xing; Luo, Ying

    2001-09-01

    In this paper, firstly, several video shot detection technologies have been discussed. An edited video consists of two kinds of shot boundaries have been known as straight cuts and optical cuts. Experimental result using a variety of videos are presented to demonstrate that moving window detection algorithm and 10-step difference histogram comparison algorithm are effective for detection of both kinds of shot cuts. After shot isolation, methods for shot characterization were investigated. We present a detailed discussion of key-frame extraction and review the visual features, particularly the color feature based on HSV model, of key-frames. Video retrieval methods based on key-frames have been presented at the end of this section. This paper also present an integrated system solution for computer- assisted video parsing and content-based video retrieval. The application software package was programmed on Visual C++ development platform.

  17. 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

    chosen using regular grids or according to administrative constraints. Nowadays, however, the choice can be based on more realistic risk assessment, as it is possible to simulate potential radioactive plumes. To support sensor planning, we developed the DETECT Optimisation Tool (DOT) within the scope...... monitoring network for early detection of radioactive plumes or for the creation of dose maps. The DOT is implemented as a stand-alone easy-to-use JAVA-based application with a graphical user interface and an R backend. Users can run evaluations and optimisations, and display, store and download the results...

  18. An Buffer Overflow Automatic Detection MethodBased on Operation Semantic

    Institute of Scientific and Technical Information of China (English)

    ZHAO Dong-fan; LIU Lei

    2005-01-01

    Buffer overflow is the most dangerous attack method that can be exploited. According to the statistics of Computer Emergency Readiness Team(CERT), buffer overflow accounts for 50% of the current software vulnerabilities, and this ratio is going up. Considering a subset of C language, Mini C, this paper presents an abstract machine model that can realize buffer overflow detection, which is based on operation semantic. Thus the research on buffer overflow detection can be built on strict descriptions of operation semantic. Not only the correctness can be assured, but also the system can be realized and extended easily.

  19. 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...

  20. Automatic stance-swing phase detection from accelerometer data for peroneal nerve stimulation

    NARCIS (Netherlands)

    Willemsen, Antoon Th.M.; Bloemhof, Fedde; Boom, Herman B.K.

    1990-01-01

    The development of implantable peroneal nerve stimulators has increased interest in sensors which can detect the different phases of walking (stance and swing). Accelerometers with a potential for implantation are studied as detectors for the swing phase of walking to replace footswitches. Theoretic

  1. Automatic Detection and Evaluation of Solar Cell Micro-Cracks in Electroluminescence Images Using Matched Filters

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2016-01-01

    A method for detecting micro cracks in solar cell using two dimensional matched filters was developed, derived from the electroluminescence intensity profile of typical microcracks. We describe the image processing steps to obtain a binary map with the location of the micro-cracks. Finally, we sh...

  2. Automatic detection of large pulmonary solid nodules in thoracic CT images

    Energy Technology Data Exchange (ETDEWEB)

    Setio, Arnaud A. A., E-mail: arnaud.arindraadiyoso@radboudumc.nl; Jacobs, Colin; Gelderblom, Jaap [Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA (Netherlands); Ginneken, Bram van [Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA (Netherlands); Fraunhofer MEVIS, Bremen 28359 (Germany)

    2015-10-15

    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 analysis of the slight change image for unsupervised change detection

    Science.gov (United States)

    Yang, Jilian; Sun, Weidong

    2015-01-01

    We propose an unsupervised method for slight change extraction and detection in multitemporal hyperspectral image sequence. To exploit the spectral signatures in hyperspectral images, autoregressive integrated moving average and fitting models are employed to create a prediction of single-band and multiband time series. Minimum mean absolute error index is then applied to obtain the preliminary change information image (PCII), which contains slight change information. After that, feature vectors are created for each pixel in the PCII using block processing and locally linear embedding. The final change detection (CD) mask is obtained by clustering the extracted feature vectors into changed and unchanged classes using k-means clustering algorithm with k=2. Experimental results demonstrate that the proposed method extracts the slight change information efficiently in the hyperspectral image sequence and outperforms the state-of-the-art CD methods quantitatively and qualitatively.

  4. Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles

    CERN Document Server

    Rahaman, G M Atiqur

    2009-01-01

    Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Again, price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an automated defect detection and classification technique has been proposed in this report that can have ensured the better quality of tiles in manufacturing process as well as production rate. Our proposed method plays an important role in ceramic tiles industries to detect the defects and to control the quality of ceramic tiles. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time and also to decide about the recovery process so that the defected tiles may not be mixed with the fresh tiles.

  5. Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech

    OpenAIRE

    2013-01-01

    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 ...

  6. An Automatic Approach to Detect Software Anomalies in Cloud Computing Using Pragmatic Bayes Approach

    Directory of Open Access Journals (Sweden)

    Nethaji V

    2014-06-01

    Full Text Available Software detection of anomalies is a vital element of operations in data centers and service clouds. Statistical Process Control (SPC cloud charts sense routine anomalies and their root causes are identified based on the differential profiling strategy. By automating the tasks, most of the manual overhead incurred in detecting the software anomalies and the analysis time are reduced to a larger extent but detailed analysis of profiling data are not performed in most of the cases. On the other hand, the cloud scheduler judges both the requirements of the user and the available infrastructure to equivalent their requirements. OpenStack prototype works on cloud trust management which provides the scheduler but complexity occurs when hosting the cloud system. At the same time, Trusted Computing Base (TCB of a computing node does not achieve the scalability measure. This unique paradigm brings about many software anomalies, which have not been well studied. This work, a Pragmatic Bayes approach studies the problem of detecting software anomalies and ensures scalability by comparing information at the current time to historical data. In particular, PB approach uses the two component Gaussian mixture to deviations at current time in cloud environment. The introduction of Gaussian mixture in PB approach achieves higher scalability measure which involves supervising massive number of cells and fast enough to be potentially useful in many streaming scenarios. Wherein previous works has been ensured for scheduling often lacks of scalability, this paper shows the superiority of the method using a Bayes per section error rate procedure through simulation, and provides the detailed analysis of profiling data in the marginal distributions using the Amazon EC2 dataset. Extensive performance analysis shows that the PB approach is highly efficient in terms of runtime, scalability, software anomaly detection ratio, CPU utilization, density rate, and computational

  7. 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.

  8. Automatic 3-D Optical Detection on Orientation of Randomly Oriented Industrial Parts for Rapid Robotic Manipulation

    Directory of Open Access Journals (Sweden)

    Liang-Chia Chen

    2012-12-01

    Full Text Available This paper proposes a novel method employing a developed 3-D optical imaging and processing algorithm for accurate classification of an object’s surface characteristics in robot pick and place manipulation. In the method, 3-D geometry of industrial parts can be rapidly acquired by the developed one-shot imaging optical probe based on Fourier Transform Profilometry (FTP by using digital-fringe projection at a camera’s maximum sensing speed. Following this, the acquired range image can be effectively segmented into three surface types by classifying point clouds based on the statistical distribution of the normal surface vector of each detected 3-D point, and then the scene ground is reconstructed by applying least squares fitting and classification algorithms. Also, a recursive search process incorporating the region-growing algorithm for registering homogeneous surface regions has been developed. When the detected parts are randomly overlapped on a workbench, a group of defined 3-D surface features, such as surface areas, statistical values of the surface normal distribution and geometric distances of defined features, can be uniquely recognized for detection of the part’s orientation. Experimental testing was performed to validate the feasibility of the developed method for real robotic manipulation.

  9. 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.

  10. Algorithm for automatic detection of the cardiovascular parameter PR-interval from LDV-velocity signals

    Science.gov (United States)

    Mignanelli, Laura; Rembe, Christian

    2016-06-01

    Laser-Doppler-vibrometry (LDV) is broadly employed in mechanical engineering but it has been demonstrated by several researchers that the technique has also large potential in biomedical applications. In particular, the detection of several vital parameters (heart rate, heart rate variability, respiration period) is known as optical vibrocardiography - VBCG. Recent studies have demonstrated the possibility of a reliable detection of the PR-interval (time between atria and ventricle contractions) and classification of the different types of atrioventricular (AV) blocks from this velocity signals. In this work, an algorithm for the localization of the vibrations generated by atrial contraction for the detection of the PR-interval in VBCG acquired on the thorax is presented. The determination of the time point of a heart beat can be extracted easily because it generates an unambiguous maximal velocity peak in the time data. Extracting the contraction of the atrium is more challenging because it is a characteristic signature with an amplitude at the magnitude of the signal disturbances. We compare different approaches of a cost function for the determination of the time point of the atria-contraction signature as well as different optimization algorithms to find the correct PR-time.

  11. Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images

    CERN Document Server

    Celebi, M Emre; Stoecker, William V; Moss, Randy H; Rabinovitz, Harold S; Argenziano, Giuseppe; Soyer, H Peter; 10.1016/j.compmedimag.2008.08.003

    2010-01-01

    Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white “ground-glass” film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.

  12. 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.

  13. Automatic barcode recognition method based on adaptive edge detection and a mapping model

    Science.gov (United States)

    Yang, Hua; Chen, Lianzheng; Chen, Yifan; Lee, Yong; Yin, Zhouping

    2016-09-01

    An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. AEDM is an edge detection-based method that has three consecutive phases. The first phase extracts the scan lines from a cropped image. The second phase involves detecting the edge points in a scan line. The edge positions are assumed to be the intersecting points between a scan line and a corresponding well-designed reference line. The third phase involves adjusting the preliminary edge positions to more reasonable positions by employing prior information of the coding rules. Thus, a universal edge mapping model is established to obtain the coding positions of each edge in this phase, followed by a decoding procedure. The Levenberg-Marquardt method is utilized to solve this nonlinear model. The computational complexity and convergence analysis of AEDM are also provided. Several experiments were implemented to evaluate the performance of AEDM algorithm. The results indicate that the efficient AEDM algorithm outperforms state-of-the-art methods and adequately addresses multiple issues, such as out-of-focus blur, nonlinear distortion, noise, nonlinear optical illumination, and situations that involve the combinations of these issues.

  14. APASVO: A free software tool for automatic P-phase picking and event detection in seismic traces

    Science.gov (United States)

    Romero, José Emilio; Titos, Manuel; Bueno, Ángel; Álvarez, Isaac; García, Luz; Torre, Ángel de la; Benítez, M.a. Carmen

    2016-05-01

    The accurate estimation of the arrival time of seismic waves or picking is a problem of major interest in seismic research given its relevance in many seismological applications, such as earthquake source location and active seismic tomography. In the last decades, several automatic picking methods have been proposed with the ultimate goal of implementing picking algorithms whose results are comparable to those obtained by manual picking. In order to facilitate the use of these automated methods in the analysis of seismic traces, this paper presents a new free, open source, software graphical tool, named APASVO, which allows picking tasks in an easy and user-friendly way. The tool also provides event detection functionality, where a relatively imprecise estimation of the onset time is sufficient. The application implements the STA-LTA detection algorithm and the AMPA picking algorithm. An autoregressive AIC-based picking method can also be applied. Besides, this graphical tool is complemented with two additional command line tools, an event picking tool and a synthetic earthquake generator. APASVO is a multiplatform tool that works on Windows, Linux and OS X. The application can process data in a large variety of file formats. It is implemented in Python and relies on well-known scientific computing packages such as ObsPy, NumPy, SciPy and Matplotlib.

  15. Automatic Detection of CT Perfusion Datasets Unsuitable for Analysis due to Head Movement of Acute Ischemic Stroke Patients

    Directory of Open Access Journals (Sweden)

    Fahmi Fahmi

    2014-01-01

    Full Text Available Head movement during brain Computed Tomography Perfusion (CTP can deteriorate perfusion analysis quality in acute ischemic stroke patients. We developed a method for automatic detection of CTP datasets with excessive head movement, based on 3D image-registration of CTP, with non-contrast CT providing transformation parameters. For parameter values exceeding predefined thresholds, the dataset was classified as ‘severely moved’. Threshold values were determined by digital CTP phantom experiments. The automated selection was compared to manual screening by 2 experienced radiologists for 114 brain CTP datasets. Based on receiver operator characteristics, optimal thresholds were found of respectively 1.0°, 2.8° and 6.9° for pitch, roll and yaw, and 2.8 mm for z-axis translation. The proposed method had a sensitivity of 91.4% and a specificity of 82.3%. This method allows accurate automated detection of brain CTP datasets that are unsuitable for perfusion analysis.

  16. An automatic, vigorous-injection assisted dispersive liquid-liquid microextraction technique for stopped-flow spectrophotometric detection of boron.

    Science.gov (United States)

    Alexovič, Michal; Wieczorek, Marcin; Kozak, Joanna; Kościelniak, Paweł; Balogh, Ioseph S; Andruch, Vasil

    2015-02-01

    A novel automatic vigorous-injection assisted dispersive liquid-liquid microextraction procedure based on the use of a modified single-valve sequential injection manifold (SV-SIA) was developed and applied for determination of boron in water samples. The major novelties in the procedure are the achieving of efficient dispersive liquid-liquid microextraction by means of single vigorous-injection (250 µL, 900 µL s(-1)) of the extraction solvent (n-amylacetate) into aqueous phase resulting in the effective dispersive mixing without using dispersive solvent and after self-separation of the phases, as well as forwarding of the extraction phase directly to a Z-flow cell (10 mm) without the use of a holding coil for stopped-flow spectrophotometric detection. The calibration working range was linear up to 2.43 mg L(-1) of boron at 426nm wavelength. The limit of detection, calculated as 3s of a blank test (n=10), was found to be 0.003 mg L(-1), and the relative standard deviation, measured as ten replicable concentrations at 0.41 mg L(-1) of boron was determined to be 5.6%. The validation of the method was tested using certified reference material.

  17. D Geological Outcrop Characterization: Automatic Detection of 3d Planes (azimuth and Dip) Using LiDAR Point Clouds

    Science.gov (United States)

    Anders, K.; Hämmerle, M.; Miernik, G.; Drews, T.; Escalona, A.; Townsend, C.; Höfle, B.

    2016-06-01

    Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estima-tion. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Fur-thermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

  18. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and deltaRR intervals.

    Science.gov (United States)

    Tateno, K; Glass, L

    2001-11-01

    The paper describes a method for the automatic detection of atrial fibrillation, an abnormal heart rhythm, based on the sequence of intervals between heartbeats. The RR interval is the interbeat interval, and deltaRR is the difference between two successive RR intervals. Standard density histograms of the RR and deltaRR intervals were prepared as templates for atrial fibrillation detection. As the coefficients of variation of the RR and deltaRR intervals were approximately constant during atrial fibrillation, the coefficients of variation in the test data could be compared with the standard coefficients of variation (CV test). Further, the similarities between the density histograms of the test data and the standard density histograms were estimated using the Kolmogorov-Smirnov test. The CV test based on the RR intervals showed a sensitivity of 86.6% and a specificity of 84.3%. The CV test based on the deltaRR intervals showed that the sensitivity and the specificity are both approximately 84%. The Kolmogorov-Smirnov test based on the RR intervals did not improve on the result of the CV test. In contrast, the Kolmogorov-Smirnov test based on the ARR intervals showed a sensitivity of 94.4% and a specificity of 97.2%.

  19. Automatic method detection of artifacts for control of tomographic uniformity on SPECT; Metodo automatico de dteccion de artefactos para el control de la uniformidad tomografica en SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Reynes Llompart, G.; Puchal, R.

    2013-07-01

    The objective of this work is the find an automatic method for the detection and classification of artifacts produced in tomographic uniformity, extracting the characteristics necessary to apply a classification algorithm using pattern recognition techniques. The method has been trained and validated with synthetic images and tested with real images. (Author)

  20. Semi-automatic Epileptic Hot Spot Detection in ECD brain SPECT images

    Science.gov (United States)

    Papp, Laszlo; Zuhayra, Maaz; Henze, Eberhard

    A method is proposed to process ECD brain SPECT images representing epileptic hot spots inside the brain. For validation 35 ictal —interictal patient image data were processed. The images were registered by a normalized mutual information method, then the separation of the suspicious and normal brain areas were performed by two threshold-based segmentations. Normalization between the images was performed by local normal brain mean values. Based on the validation made by two medical physicians, minimal human intervention in the segmentation parameters was necessary to detect all epileptic spots and minimize the number of false spots inside the brain.

  1. Incidence and causes of inappropriate detection and therapy by implantable defibrillators of cardioversion in patients with ventricular tachyarrhythmia

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Background Implantable cardioverter defibrillator (ICD) is the only effective therapy in patients with life threatening ventricular arrhythmias. Inappropriate detection and therapy by ICDs are the most common causes of side effects that affect the quality of life in ICD recipients. This study evaluated the incidence and causes of inappropriate detection and therapy by ICDs in patients in our hospital.Methods From January 2000 to December 2005, fifty patients who received ICD implantation for ventricular arrhythmias for prevention of sudden cardiac death were evaluated in this study. Each ICD was programmed using clinical arrhythmic and cardiac data of the patient before discharge. Patients were followed up by standard schedule after implantation and all data retrieved from each device were collected and saved for further analysis. Results No arrhythmic event was detected in 12/50 (24%) patients during the period of follow-up. Among the remaining patients, 11 (22%) experienced inappropriate detections and therapies during follow-up in this study. ICD detected 383 ventricular tachyarrhythmia (VT) and 108 ventricular fibrillation (VF) episodes and delivered 678 therapies. In VT group, ICD delivered 413 antitachycardiac pacings (ATPs) and 118 shocks, among which 78 ATPs and 9 shocks were initiated by 55/383 (14.3%) inappropriate detections. In VF group ICD delivered 147 shocks, among which 56 shocks were initiated by 28/108 (26.9%) inappropriate detections. Overall, more than 50% of these episodes were caused by atrial fibrillation (AF) with rapid ventricular response, followed by electromagnetic or myopotential interference. In addition, most inappropriate therapies occurred within one year after ICD implantation.Conclusions About one fifth of patients experienced ICD inappropriate detection and therapy after implantation. The main cause was AF with rapid ventricular response, followed by electromagnetic or myopotential interference.

  2. Cardiovascular risk factors and incident albuminuria in screen-detected type 2 diabetes

    DEFF Research Database (Denmark)

    Webb, D R; Zaccardi, F; Davies, M J

    2016-01-01

    BACKGROUND: It is unclear whether cardiovascular risk factor modification influences the development of renal disease in people with type 2 diabetes identified through screening. We determined predictors of albuminuria five years after a diagnosis of screen-detected diabetes within the ADDITION-E...

  3. 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.

  4. 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.

  5. 交流接触器自动检测系统设计%Design of Automatic Detecting System of AC Contactor

    Institute of Scientific and Technical Information of China (English)

    李林; 强秀华; 邹斌

    2012-01-01

    针对交流接触器传统检测方法存在的缺点,设计了交流接触器自动检测系统.首先,对检测流程和设计要求做了说明;然后,给出了数据采集程序和PLC程序流程图.结果表明,该系统运行可靠,检测精度高,达到了规定的设计目标.%Focused on the defects of the traditional method of detecting for AC contactor, the paper designed the auto-matic detecting system of AC contactor. Firstly detecting process and design requirements were introduced, secondly the software flowchart of data acquisition program and PLC program were given. The results showed that this automatic detec-ting runs reliably and has high accuracy, it achieves the required goals and designed requirements.

  6. Our Incidence of Diaphragmatic Hernia Detected with MDCT in the Past Two Years

    Directory of Open Access Journals (Sweden)

    Nesrin Atcı

    2015-11-01

    Full Text Available Aim: Diaphragmatic hernia develops as a result of extension of the intraabdominal organs to the thorax from a diaphragmatic defect which may be either a congenital fusion defect or subsequently formed defect(iatrojenic or traumatic. The diagnosis of symptomatic or asymptomatic diaphragmatic hernia can be easily done with the cross-sectional imaging, multidetector computed tomography (MDCT devices our aim in this study is to investigate diaphragmatic hernia incidence diagnosed by MDCT retrospectively. Methods: An experienced radiologist retrospectively evaluated MDCT results of 1000 patients to whom thorax and abdominal computed tomography was done due to chest and abdominal discomfort or trauma during the last 2 years. Results: According to our results, out of 1000 patients, 77 (7.7% patients had different types of diaphragmatic hernia the most common herniation was hiatal hernia which was seen in 54 patients. Congenital diaphragmatic hernia (n=21 and traumatic diaphragmatic hernia (n=2 were observed also. Conclusion: Diaphragmatic hernia diagnosis could be made easily with extensive use of MDCT in which multi-planar imaging can be taken.

  7. Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.

    Science.gov (United States)

    Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit

    2015-08-01

    We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field.

  8. ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification

    CERN Document Server

    Tramacere, A; Dubath, P; Kneib, J -P; Courbin, F

    2016-01-01

    We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional ones related to segments of spiral arms. We use two different supervised ensemble classification algorithms, Random Forest,...

  9. Automatic post-picking improves particle image detection from Cryo-EM micrographs

    CERN Document Server

    Norousi, Ramin; Becker, Thomas; Beckmann, Roland; Schmid, Volker J; Tresch, Achim

    2011-01-01

    Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction is extensively used to reveal structural information of macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes acquire thousands of high-quality images. Having collected these data, each single particle must be detected and windowed out. Several fully- or semi-automated approaches have been developed for the selection of particle images from digitized micrographs. However they still require laborious manual post processing, which will become the major bottleneck for next generation of electron microscopes. Instead of focusing on improvements in automated particle selection from micrographs, we propose a post-picking step for classifying small windowed images, which are output by common picking software. A supervised strategy for the classification of windowed micrograph images into particles and non-particles reduces the manual workload by orders of magnitude. The method builds...

  10. A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

    Directory of Open Access Journals (Sweden)

    Marco Fagiani

    2016-08-01

    Full Text Available In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM, Hidden Markov Model (HMM and One-Class Support Vector Machine (OC-SVM, are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds, and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC, reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.

  11. 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.

  12. Automatic detection and rapid determination of earthquake magnitude by wavelet multiscale analysis of the primary arrival

    Science.gov (United States)

    Dando, B.; Simons, F. J.; Allen, R. M.

    2006-12-01

    Earthquake early warning systems save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with unmatched accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave. The curious observation that such brief segments of the seismogram may contain information about the final magnitude even of very large earthquakes, which occur on faults that may rupture over tens of seconds, is central to a debate in the seismological community which we hope to stimulate but cannot attempt to address within the scope of this paper. Wavelet coefficients of the seismogram can be determined extremely rapidly and efficiently by the fast lifting wavelet transform. Extracting amplitudes at individual scales is a very simple procedure, involving a mere handful of lines of computer code. Scale-dependent thresholded amplitudes derived from the wavelet transform of the first 3--4 seconds of an incoming seismic P arrival are predictive of earthquake magnitude, with errors of one magnitude unit for seismograms recorded up to 150 km away from the earthquake source. Our procedure is a simple yet extremely efficient tool for implementation on low-power recording stations. It provides an accurate and precise method of autonomously detecting the incoming P wave and predicting the magnitude of the source from the scale-dependent character of its amplitude well before the arrival of damaging ground motion. Provided a dense array of networked seismometers exists, our procedure should become the tool of choice for earthquake early warning systems worldwide.

  13. Automatic detection of thermal damage in grinding process by artificial neural network

    Directory of Open Access Journals (Sweden)

    Fábio Romano Lofrano Dotto

    2003-12-01

    Full Text Available This work aims to develop an intelligent system for detecting the workpiece burn in the surface grinding process by utilizing a multi-perceptron neural network trained to generalize the process and, in turn, obtnaing the burning threshold. In general, the burning occurrence in grinding process can be detected by the DPO and FKS parameters. However, these ones were not efficient at the grinding conditions used in this work. Acoustic emission and electric power of the grinding wheel drive motor are the input variable and the output variable is the burning occurrence to the neural network. In the experimental work was employed one type of steel (ABNT-1045 annealed and one type of grinding wheel referred to as TARGA model ART 3TG80.3 NVHB.Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. Em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

  14. Potential application of Kanade-Lucas-Tomasi tracker on satellite images for automatic change detection

    Science.gov (United States)

    Ahmed, Tasneem; Singh, Dharmendra; Raman, Balasubramanian

    2016-04-01

    Monitoring agricultural areas is still a very challenging task. Various models and methodologies have been developed for monitoring the agricultural areas with satellite images, but their practical applicability is limited due to the complexity in processing and dependence on a priori information. Therefore, in this paper, an attempt has been made to investigate the utility of the Kanade-Lucas-Tomasi (KLT) tracker, which is generally useful for tracking objects in video images, for monitoring agricultural areas. The KLT tracker was proposed to deal with the problem of image registration, but the use of the KLT tracker in satellite images for land cover monitoring is rarely reported. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data has been used to identify and track the agricultural areas. The tracked pixels were compared with the agriculture pixels obtained from a decision tree algorithm and both results are closely matched. An image differencing change detection technique has been applied after KLT tracker implementation to observe the "change" and "no change" pixels in agricultural areas. It is observed that two kinds of changes are being detected. The areas where agriculture was not there earlier, but now is present, the changes are called positive changes. In the areas where agriculture was present earlier, but now is not present, those changes are referred to as negative changes. Unchanged areas retrieved from both the images are labeled as "no change" pixels. The novelty of the proposed algorithm is that it uses a simplified version of the KLT tracker to efficiently select and track the agriculture features on the basis of their spatial information and does not require a priori information every time.

  15. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks.

    Science.gov (United States)

    Acir, Nurettin; Oztura, Ibrahim; Kuntalp, Mehmet; Baklan, Bariş; Güzeliş, Cüneyt

    2005-01-01

    This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.

  16. Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

    Institute of Scientific and Technical Information of China (English)

    Fang Ye; Zhi-Hua Chen; Jie Chen; Fang Liu; Yong Zhang; Qin-Ying Fan; Lin Wang

    2016-01-01

    Background:In the past decades,studies on infant anemia have mainly focused on rural areas of China.With the increasing heterogeneity of population in recent years,available information on infant anemia is inconclusive in large cities of China,especially with comparison between native residents and floating population.This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing.Methods:As useful methods to build a predictive model,Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia.A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1,2013 to December 31,2014.Results:The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics.The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia.Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy,exclusive breastfeeding in the first 6 months,and floating population,CHAID decision tree analysis also identified the fourth risk factor,the matemal educational level,with higher overall classification accuracy and larger area below the receiver operating characteristic curve.Conclusions:The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners.CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity.Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

  17. Automatic detection Non-proliferative Diabetic Retinopathy using image processing techniques

    Directory of Open Access Journals (Sweden)

    RajuS. Maher

    2016-01-01

    Full Text Available Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than 190 million people with diabetes worldwide. The World Health Organization (WHO estimates that this will rise to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle. Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as our population age. Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating complications like blindness. At present, various analyses on complicated interaction between hereditary and environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic complication has become a major concern regarding the prognosis of diabetic patients. Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure more Singaporeans from falling prey to this disease.

  18. Automatic Detection and Segmentation of Columns in As-Built Buildings from Point Clouds

    Directory of Open Access Journals (Sweden)

    Lucía Díaz-Vilariño

    2015-11-01

    Full Text Available Over the past few years, there has been an increasing need for tools that automate the processing of as-built 3D laser scanner data. Given that a fast and active dimensional analysis of constructive components is essential for construction monitoring, this paper is particularly focused on the detection and segmentation of columns in building interiors from incomplete point clouds acquired with a Terrestrial Laser Scanner. The methodology addresses two types of columns: round cross-section and rectangular cross-section. Considering columns as vertical elements, the global strategy for segmentation involves the rasterization of a point cloud onto the XY plane and the implementation of a model-driven approach based on the Hough Transform. The methodology is tested in two real case studies, and experiments are carried out under different levels of data completeness. The results show the robustness of the methodology to the presence of clutter and partial occlusion, typical in building indoors, even though false positives can be obtained if other elements with the same shape and size as columns are present in the raster.

  19. An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

    Directory of Open Access Journals (Sweden)

    Safat B. Wali

    2015-01-01

    Full Text Available The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%, and processing time (0.43 s. The area under the receiver operating characteristic (ROC curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

  20. ASTErIsM: application of topometric clustering algorithms in automatic galaxy detection and classification

    Science.gov (United States)

    Tramacere, A.; Paraficz, D.; Dubath, P.; Kneib, J.-P.; Courbin, F.

    2016-12-01

    We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm, which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional groups related to segments of spiral arms. We use two different supervised ensemble classification algorithms: Random Forest and Gradient Boosting. Using a sample of ≃24 000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopic redshifts, and we test our classification against the Galaxy Zoo 2 catalogue. We find that features extracted from our pipeline give, on average, an accuracy of ≃93 per cent, when testing on a test set with a size of 20 per cent of our full data set, with features deriving from the angular distribution of density attractor ranking at the top of the discrimination power.

  1. Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data

    Directory of Open Access Journals (Sweden)

    Zhen Zhen

    2016-04-01

    Full Text Available Automated individual tree crown detection and delineation (ITCD using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively. Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests rather than a single forest type although most published ITCD studies still focused on closed softwood (41% or mixed forest (22%. Approximately one-third of studies applied individual tree level (30% assessment, with only a quarter reporting more comprehensive multi-level assessment (23%. Almost one-third of studies (32% that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a

  2. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

    Full Text Available Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

  3. TEXT CLASSIFICATION FOR AUTOMATIC DETECTION OF E-CIGARETTE USE AND USE FOR SMOKING CESSATION FROM TWITTER: A FEASIBILITY PILOT.

    Science.gov (United States)

    Aphinyanaphongs, Yin; Lulejian, Armine; Brown, Duncan Penfold; Bonneau, Richard; Krebs, Paul

    2016-01-01

    Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect e-cigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.94 area under the curve in each category. These promising initial results form the foundation for further studies to realize the ideal surveillance solution.

  4. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques.

    Science.gov (United States)

    Pluk, A; Bahr, C; Poursaberi, A; Maertens, W; van Nuffel, A; Berckmans, D

    2012-04-01

    This paper describes a synchronized measurement system combining image and pressure data to automatically record the angle of the metacarpus and metatarsus bones of the cow with respect to a vertical line, which is useful for lameness detection in dairy cattle. A camera system was developed to record the posture and movement of the cow and the timing and position of hoof placement and release were recorded using a pressure sensitive mat. Experiments with the automatic system were performed continuously on a farm in Ghent (Belgium) for 5 wk in September and October 2009. In total, 2,219 measurements were performed on 75 individual lactating Holstein cows. As a reference for the analysis of the calculated variables, the locomotion of the cows was visually scored from recorded videos by a trained observer into 3 classes of lameness [53.5% were scored with gait score (GS)1, 33.3% were scored with GS2, and 9.3% were scored with GS3]. The contact data of the pressure mat and the camera images recorded by the system were synchronized and combined to measure different angles of the legs of the cows, together with the range of motion of the leg. Significant differences were found between the different gait scores in the release angles of the front hooves, in the range of motion of the front hooves, and in the touch angles of the hind hooves. The contact data of the pressure mat and the camera images recorded by the system were synchronized and combined to measure different angles of the legs of the cows, together with the range of motion of the leg. With respect to the classification of lameness, the range of motion of the front hooves (42.1 and 42.8%) and the release angle of the front hooves (41.7 and 42.0%) were important variables. In 83.3% of the cows, a change in GS led to an increase in within-cow variance for the range of motion or the release angle of the front hooves. In 76.2% of the cows, an increase in GS led to a decrease in range of motion or an increase in

  5. SU-C-201-04: Quantification of Perfusion Heterogeneity Based On Texture Analysis for Fully Automatic Detection of Ischemic Deficits From Myocardial Perfusion Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Fang, Y [National Cheng Kung University, Tainan, Taiwan (China); Huang, H [Chang Gung University, Taoyuan, Taiwan (China); Su, T [Chang Gung Memorial Hospital, Taoyuan, Taiwan (China)

    2015-06-15

    Purpose: Texture-based quantification of image heterogeneity has been a popular topic for imaging studies in recent years. As previous studies mainly focus on oncological applications, we report our recent efforts of applying such techniques on cardiac perfusion imaging. A fully automated procedure has been developed to perform texture analysis for measuring the image heterogeneity. Clinical data were used to evaluate the preliminary performance of such methods. Methods: Myocardial perfusion images of Thallium-201 scans were collected from 293 patients with suspected coronary artery disease. Each subject underwent a Tl-201 scan and a percutaneous coronary intervention (PCI) within three months. The PCI Result was used as the gold standard of coronary ischemia of more than 70% stenosis. Each Tl-201 scan was spatially normalized to an image template for fully automatic segmentation of the LV. The segmented voxel intensities were then carried into the texture analysis with our open-source software Chang Gung Image Texture Analysis toolbox (CGITA). To evaluate the clinical performance of the image heterogeneity for detecting the coronary stenosis, receiver operating characteristic (ROC) analysis was used to compute the overall accuracy, sensitivity and specificity as well as the area under curve (AUC). Those indices were compared to those obtained from the commercially available semi-automatic software QPS. Results: With the fully automatic procedure to quantify heterogeneity from Tl-201 scans, we were able to achieve a good discrimination with good accuracy (74%), sensitivity (73%), specificity (77%) and AUC of 0.82. Such performance is similar to those obtained from the semi-automatic QPS software that gives a sensitivity of 71% and specificity of 77%. Conclusion: Based on fully automatic procedures of data processing, our preliminary data indicate that the image heterogeneity of myocardial perfusion imaging can provide useful information for automatic determination

  6. Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy

    Science.gov (United States)

    Moghimi, Saba; Kushki, Azadeh; Power, Sarah; Guerguerian, Anne Marie; Chau, Tom

    2012-04-01

    Emotional responses can be induced by external sensory stimuli. For severely disabled nonverbal individuals who have no means of communication, the decoding of emotion may offer insight into an individual’s state of mind and his/her response to events taking place in the surrounding environment. Near-infrared spectroscopy (NIRS) provides an opportunity for bed-side monitoring of emotions via measurement of hemodynamic activity in the prefrontal cortex, a brain region known to be involved in emotion processing. In this paper, prefrontal cortex activity of ten able-bodied participants was monitored using NIRS as they listened to 78 music excerpts with different emotional content and a control acoustic stimuli consisting of the Brown noise. The participants rated their emotional state after listening to each excerpt along the dimensions of valence (positive versus negative) and arousal (intense versus neutral). These ratings were used to label the NIRS trial data. Using a linear discriminant analysis-based classifier and a two-dimensional time-domain feature set, trials with positive and negative emotions were discriminated with an average accuracy of 71.94% ± 8.19%. Trials with audible Brown noise representing a neutral response were differentiated from high arousal trials with an average accuracy of 71.93% ± 9.09% using a two-dimensional feature set. In nine out of the ten participants, response to the neutral Brown noise was differentiated from high arousal trials with accuracies exceeding chance level, and positive versus negative emotional differentiation accuracies exceeded the chance level in seven out of the ten participants. These results illustrate that NIRS recordings of the prefrontal cortex during presentation of music with emotional content can be automatically decoded in terms of both valence and arousal encouraging future investigation of NIRS-based emotion detection in individuals with severe disabilities.

  7. SpotMetrics: An Open-Source Image-Analysis Software Plugin for Automatic Chromatophore Detection and Measurement

    Science.gov (United States)

    Hadjisolomou, Stavros P.; El-Haddad, George

    2017-01-01

    Coleoid cephalopods (squid, octopus, and sepia) are renowned for their elaborate body patterning capabilities, which are employed for camouflage or communication. The specific chromatic appearance of a cephalopod, at any given moment, is a direct result of the combined action of their intradermal pigmented chromatophore organs and reflecting cells. Therefore, a lot can be learned about the cephalopod coloration system by video recording and analyzing the activation of individual chromatophores in time. The fact that adult cephalopods have small chromatophores, up to several hundred thousand in number, makes measurement and analysis over several seconds a difficult task. However, current advancements in videography enable high-resolution and high framerate recording, which can be used to record chromatophore activity in more detail and accuracy in both space and time domains. In turn, the additional pixel information and extra frames per video from such recordings result in large video files of several gigabytes, even when the recording spans only few minutes. We created a software plugin, “SpotMetrics,” that can automatically analyze high resolution, high framerate video of chromatophore organ activation in time. This image analysis software can track hundreds of individual chromatophores over several hundred frames to provide measurements of size and color. This software may also be used to measure differences in chromatophore activation during different behaviors which will contribute to our understanding of the cephalopod sensorimotor integration system. In addition, this software can potentially be utilized to detect numbers of round objects and size changes in time, such as eye pupil size or number of bacteria in a sample. Thus, we are making this software plugin freely available as open-source because we believe it will be of benefit to other colleagues both in the cephalopod biology field and also within other disciplines. PMID:28298896

  8. SpotMetrics: An Open-Source Image-Analysis Software Plugin for Automatic Chromatophore Detection and Measurement.

    Science.gov (United States)

    Hadjisolomou, Stavros P; El-Haddad, George

    2017-01-01

    Coleoid cephalopods (squid, octopus, and sepia) are renowned for their elaborate body patterning capabilities, which are employed for camouflage or communication. The specific chromatic appearance of a cephalopod, at any given moment, is a direct result of the combined action of their intradermal pigmented chromatophore organs and reflecting cells. Therefore, a lot can be learned about the cephalopod coloration system by video recording and analyzing the activation of individual chromatophores in time. The fact that adult cephalopods have small chromatophores, up to several hundred thousand in number, makes measurement and analysis over several seconds a difficult task. However, current advancements in videography enable high-resolution and high framerate recording, which can be used to record chromatophore activity in more detail and accuracy in both space and time domains. In turn, the additional pixel information and extra frames per video from such recordings result in large video files of several gigabytes, even when the recording spans only few minutes. We created a software plugin, "SpotMetrics," that can automatically analyze high resolution, high framerate video of chromatophore organ activation in time. This image analysis software can track hundreds of individual chromatophores over several hundred frames to provide measurements of size and color. This software may also be used to measure differences in chromatophore activation during different behaviors which will contribute to our understanding of the cephalopod sensorimotor integration system. In addition, this software can potentially be utilized to detect numbers of round objects and size changes in time, such as eye pupil size or number of bacteria in a sample. Thus, we are making this software plugin freely available as open-source because we believe it will be of benefit to other colleagues both in the cephalopod biology field and also within other disciplines.

  9. Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

    Science.gov (United States)

    Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

    2015-01-01

    This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

  10. Using airborne LiDAR in geoarchaeological contexts: Assessment of an automatic tool for the detection and the morphometric analysis of grazing archaeological structures (French Massif Central).

    Science.gov (United States)

    Roussel, Erwan; Toumazet, Jean-Pierre; Florez, Marta; Vautier, Franck; Dousteyssier, Bertrand

    2014-05-01

    Airborne laser scanning (ALS) of archaeological regions of interest is nowadays a widely used and established method for accurate topographic and microtopographic survey. The penetration of the vegetation cover by the laser beam allows the reconstruction of reliable digital terrain models (DTM) of forested areas where traditional prospection methods are inefficient, time-consuming and non-exhaustive. The ALS technology provides the opportunity to discover new archaeological features hidden by vegetation and provides a comprehensive survey of cultural heritage sites within their environmental context. However, the post-processing of LiDAR points clouds produces a huge quantity of data in which relevant archaeological features are not easily detectable with common visualizing and analysing tools. Undoubtedly, there is an urgent need for automation of structures detection and morphometric extraction techniques, especially for the "archaeological desert" in densely forested areas. This presentation deals with the development of automatic detection procedures applied to archaeological structures located in the French Massif Central, in the western forested part of the Puy-de-Dôme volcano between 950 and 1100 m a.s.l.. These unknown archaeological sites were discovered by the March 2011 ALS mission and display a high density of subcircular depressions with a corridor access. The spatial organization of these depressions vary from isolated to aggregated or aligned features. Functionally, they appear to be former grazing constructions built from the medieval to the modern period. Similar grazing structures are known in other locations of the French Massif Central (Sancy, Artense, Cézallier) where the ground is vegetation-free. In order to develop a reliable process of automatic detection and mapping of these archaeological structures, a learning zone has been delineated within the ALS surveyed area. The grazing features were mapped and typical morphometric attributes

  11. Automatic sequences

    CERN Document Server

    Haeseler, Friedrich

    2003-01-01

    Automatic sequences are sequences which are produced by a finite automaton. Although they are not random they may look as being random. They are complicated, in the sense of not being not ultimately periodic, they may look rather complicated, in the sense that it may not be easy to name the rule by which the sequence is generated, however there exists a rule which generates the sequence. The concept automatic sequences has special applications in algebra, number theory, finite automata and formal languages, combinatorics on words. The text deals with different aspects of automatic sequences, in particular:· a general introduction to automatic sequences· the basic (combinatorial) properties of automatic sequences· the algebraic approach to automatic sequences· geometric objects related to automatic sequences.

  12. Incidence of viral infection detected by PCR and real-time PCR in childhood community-acquired pneumonia: a meta-analysis.

    Science.gov (United States)

    Wang, Min; Cai, Feng; Wu, Xiaodong; Wu, Ting; Su, Xin; Shi, Yi

    2015-04-01

    Several studies examining the incidence of viral infection in childhood community-acquired pneumonia (CAP) utilizing polymerase chain reaction (PCR) or real-time PCR methods have been reported. We systematically searched Pubmed and Embase for studies reporting the incidence of respiratory viral infection in childhood CAP. The pooled incidences of viral infection were calculated with a random-effects model. Sources of heterogeneity were explored by subgroup analysis and a univariant metaregression analysis. We included 21 eligible reports in our study. We found significant heterogeneity on the incidence of viral infection in childhood CAP. The random effects pooled incidence was 57.4% (95% confidence interval (CI): 50.8-64.1). The pooled incidence of mixed infection was 29.3% (95%CI: 23.0-35.6) with considerable heterogeneity. The pooled incidence of mixed infection was 29.3% (95%CI: 23.0-35.6). Rhinovirus, respiratory syncytial virus (RSV) and bocavirus were found to be the three most common viruses in childhood CAP. We also demonstrated that respiratory viruses were detected in 76.1% of patients aged ≤ 1 year, 63.1% of patients aged 2-5 years and 27.9% of patients aged ≥ 6 years. We conclude that respiratory viruses are widely detected in paediatric patients with CAP by PCR or real-time PCR methods. More than half of viral infections are probably concurrent with bacterial infections. Rhinovirus, RSV and bocavirus are the three most frequent viruses identified in childhood CAP; the incidence of viral infection decreased with age.

  13. 箱梁钢筋定位自动检测装置%The automatic detection device for box girder reinforced positioning

    Institute of Scientific and Technical Information of China (English)

    邵云

    2014-01-01

    In order to reduce or eliminate the box girder quality oroblems caused by box girder reinforced orotection layer and ore-stressed oosi-tioning network reinforced deformation and installation not in olace,this oaoer researched the reinforced oositioning of reinforced orotective layer and ore-stressed oositioning network in oost tensioned ore-stressed box beam construction orocess,designed a set of automatic detection each ten-sioned oositioning detection ooint tensioned whether accuracy oositioning or not and disolayed the tensioned oositioning automatic detection de-vice,solved the oroblem of reinforced oositioning detection.%为了减少或消除因箱梁钢筋保护层及预应力定位网钢筋形变和安装不到位而引起箱梁质量问题,对后张法预应力箱梁施工工艺中的钢筋保护层及预应力定位网的钢筋定位进行了研究,设计了一套能自动检测各检测点钢筋是否定位准确并显示的钢筋定位自动检测装置,解决了钢筋定位检测的问题。

  14. The additional yield of a periodic screening programme for open-angle glaucoma : a population-based comparison of incident glaucoma cases detected in regular ophthalmic care with cases detected during screening

    NARCIS (Netherlands)

    Stoutenbeek, R.; de Voogd, S.; Wolfs, R. C. W.; Hofman, A.; de Jong, P. T. V. M.; Jansonius, N. M.

    2008-01-01

    Aim: To study the additional yield of a periodic screening programme for open-angle glaucoma (OAG) by comparing, in a population-based setting, incident OAG (iOAG) cases detected in regular ophthalmic care with those detected during screening. Methods: Participants aged 55 and over from the populati

  15. Comparative evaluation of autofocus algorithms for a real-time system for automatic detection of Mycobacterium tuberculosis.

    Science.gov (United States)

    Mateos-Pérez, José María; Redondo, Rafael; Nava, Rodrigo; Valdiviezo, Juan C; Cristóbal, Gabriel; Escalante-Ramírez, Boris; Ruiz-Serrano, María Jesús; Pascau, Javier; Desco, Manuel

    2012-03-01

    Microscopy images must be acquired at the optimal focal plane for the objects of interest in a scene. Although manual focusing is a standard task for a trained observer, automatic systems often fail to properly find the focal plane under different microscope imaging modalities such as bright field microscopy or phase contrast microscopy. This article assesses several autofocus algorithms applied in the study of fluorescence-labeled tuberculosis bacteria. The goal of this work was to find the optimal algorithm in order to build an automatic real-time system for diagnosing sputum smear samples, where both accuracy and computational time are important. We analyzed 13 focusing methods, ranging from well-known algorithms to the most recently proposed functions. We took into consideration criteria that are inherent to the autofocus function, such as accuracy, computational cost, and robustness to noise and to illumination changes. We also analyzed the additional benefit provided by preprocessing techniques based on morphological operators and image projection profiling.

  16. An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

    OpenAIRE

    Du, Q.; Xie, D; Sun, Y.

    2015-01-01

    The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other o...

  17. Incidence of error in oestrus detection based on secondary oestrus signs in a 24-h tie-stalled dairy herd with low fertility.

    Science.gov (United States)

    Ranasinghe, R M S B K; Nakao, T; Kobayashi, A

    2009-08-01

    Oestrus detection error and conception rates after AI based only on secondary oestrus signs were evaluated in a high yielding, 24-h tie-stalled dairy herd with low fertility, using milk progesterone profiles. Oestrus detection was based on the secondary oestrus signs such as restlessness, swelling, congestion of vulva and clear mucus discharge. Sixty eight AI conducted after observing the secondary oestrus signs in 44 animals were included in the study. Of the 68 AI, 53 (77.9%) were conducted in the follicular phase, and 13 (19.1%) and 2 (2.9%) were carried out in the luteal phase and during pregnancy, respectively. The overall error in oestrus detection based on milk progesterone profiles was 22.1%. The oestrus detection error did not differ significantly among different secondary oestrus signs. None of the AI conducted in the luteal phase resulted in conception, whereas 20.8% of AI conducted in the follicular phase resulted in conception. No significant difference in the conception rates among the groups of cows with different secondary oestrus signs was shown. The high incidence of oestrus detection error in this study might have been caused by the detection of cows in oestrus based only on secondary oestrus signs due to the confinement of animals. In conclusion, there was a high incidence of heat detection error in the 24-h tie-stalled dairy herd and oestrus detection based only on secondary oestrus signs resulted in low conception rate.

  18. 有效的多协议攻击自动化检测系统%An effective automatic detection system for multi-protocol attack

    Institute of Scientific and Technical Information of China (English)

    杨元凉; 马文平; 刘维博; 张笑笑

    2012-01-01

    Since there exists multi-protocol attack when several security protocols are co-executed in a computer network, an automatic detection system for multi-protocol attack (ADMA) is proposed. The system is composed of two parts named protocol search subsystem and attack verification subsystem. According to the consistency condition of the type of encrypted messages between the target protocol and the secondary protocol, the protocol search subsystem can automatically search for the candidate secondary protocols, which may be used to attack the target protocol. By improving the SAT-based model checking, attack verification subsystem can automatically verify whether multi-protocol attack exists between the target protocol and the candidate secondary protocols or not. The experiment results show that ADMA system can implement automatic detection for multi-protocol attack, and some new multi-protocol attacks are found in the detection.%针对当前计算机网络中多个安全协议并行运行时可能出现的多协议攻击问题,提出了一个多协议攻击自动化检测系统(ADMA)。该系统由协议搜索子系统和攻击确认子系统两部分组成,其中协议搜索子系统根据多协议攻击中目标协议与辅助协议加密消息类型一致性条件,自动化搜索可能对目标协议构成威胁的候选辅助协议。攻击确认子系统通过改进的SAT模型检测方法,自动化确认目标协议与候选辅助协议是否存在多协议攻击。试验结果表明,ADMA系统能够实现多协议攻击自动化检测,并且检测中发现了新的多协议攻击。

  19. Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

    Directory of Open Access Journals (Sweden)

    E. Marchetti

    2015-04-01

    Full Text Available Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool for the ambiguity to identify clear signals related to avalanches. We present here a new method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria, which overcome now this limit. The method is based on array derived wave parameters, such as back-azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification considering avalanches as a moving source of infrasound. We validate efficiency of the automatic infrasound detection with continuous observations with Doppler Radar and we show how dynamics parameters such as the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that could thus contribute significantly to avalanche forecast and risk management.

  20. Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks.

    Science.gov (United States)

    Menchón-Lara, Rosa-María; Bastida-Jumilla, María-Consuelo; Morales-Sánchez, Juan; Sancho-Gómez, José-Luis

    2014-02-01

    Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.

  1. Effect of oblique incidence on silver nanomaterials fabricated in water via ultrafast laser ablation for photonics and explosives detection

    Energy Technology Data Exchange (ETDEWEB)

    Krishna Podagatlapalli, G. [Advanced Center of Research in High Energy Materials (ACRHEM), University of Hyderabad, Prof. C. R. Rao Road, Hyderabad 500046 (India); Hamad, Syed [School of Physics, University of Hyderabad, Prof. C. R. Rao Road, Hyderabad 500046 (India); Ahamad Mohiddon, Md. [Centre for Nanotechnology University of Hyderabad, Prof. C. R. Rao Road, Hyderabad 500046 (India); Venugopal Rao, S., E-mail: svrsp@uohyd.ernet.in [Advanced Center of Research in High Energy Materials (ACRHEM), University of Hyderabad, Prof. C. R. Rao Road, Hyderabad 500046 (India)

    2014-06-01

    Highlights: •Effect of non-zero angle of incidence on ps ablation of Ag investigated. •Ag colloids were evaluated by TEM, UV–vis absorption spectra and fs-DFWM. •30° incident angle provided Ag NPs of small size with higher yields. •FESEM, AFM, Raman data revealed the fabrication of Ag nanostructures. •Utility of Ag nanostructures surfaces for multiple SERS studies demonstrated. -- Abstract: Picosecond (ps) laser ablation of silver (Ag) substrate submerged in double distilled water was performed at 800 nm for different angles of incidence of 5°, 15°, 30° and 45°. Prepared colloidal solutions were characterized through transmission electron microscopy, UV absorption spectroscopy to explore their morphologies and surface plasmon resonance (SPR) properties. Third order nonlinear optical (NLO) characterization of colloids was performed using degenerate four wave mixing (DFWM) technique with ∼40 fs laser pulses at 800 nm and the NLO coefficients were obtained. Detailed analysis of the data obtained from colloidal solutions suggested that superior results in terms of yield, sizes of the NPs, SPR peak position were achieved for ablation performed at 30° incident angle. Surface enhanced Raman spectra (SERS) of Rhodamine 6G from nanostructured substrates were investigated using excitation wavelengths of 532 and 785 nm. In both the cases substrates prepared at 30° incident angle exhibited superior enhancement in the Raman signatures with a best enhancement factor achieved being >10{sup 8}. SERS of an explosive molecule 5-amino, 3-nitro, -1H-1,2,4-nitrozole (ANTA) was also demonstrated from these nanostructured substrates. Multiple usage of Ag nanostructures for SERS studies revealed that structures prepared at 30° incident angle provided superior performance amongst all.

  2. Automatic analysis of change detection of multi-temporal ERS-2 SAR images by using two-threshold EM and MRF algorithms

    Institute of Scientific and Technical Information of China (English)

    CHEN Fei; LUO Lin; JIN Yaqiu

    2004-01-01

    To automatically detect and analyze the surface change in the urban area from multi-temporal SAR images, an algorithm of two-threshold expectation maximum (EM) and Markov random field (MRF) is developed. Difference of the SAR images demonstrates variation of backscattering caused by the surface change all over the image pixels. Two thresholds are obtained by the EM iterative process and categorized to three classes: enhanced scattering, reduced scattering and unchanged regimes. Initializing from the EM result, the iterated conditional modes (ICM) algorithm of the MRF is then used to analyze the detection of contexture change in the urban area. As an example, two images of the ERS-2 SAR in 1996 and 2002 over the Shanghai City are studied.

  3. Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Ramin Nateghi

    2014-01-01

    Full Text Available This paper introduces a computer-assisted diagnosis (CAD system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO. The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A. In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

  4. Incidence and distribution of congenital malformations clinically detected at birth: a prospective study at tertiary care hospital

    Directory of Open Access Journals (Sweden)

    Mohammad K. Gandhi

    2016-04-01

    Conclusions: From present study we conclude that incidence of congenital anomalies of CNS was highest amongst all types of congenital anomalies (meningomyelocele being the commonest. More emphasis should be given on prevention by regular antenatal care and avoidance of known teratogens and probable teratogenic agents. [Int J Res Med Sci 2016; 4(4.000: 1136-1139

  5. Posterior diaphragmatic defect detected on chest CT: the incidence according to age and the lateral chest radiographic appearances

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Son Youl; Choi, Yo Won; Jeon, Seok Chol; Heo, Jeong Nam; Park, Choong Ki [College of Medicine, Hanyang University, Seoul (Korea, Republic of)

    2007-03-15

    We wanted to investigate the incidence of posterior diaphragmatic defect on chest CT in various age groups and its lateral chest radiographic appearances. The chest CT scans of 78 patients of various ages with posterior diaphragmatic defect were selected among 1,991 patients, and they were analyzed for the incidence of defect in various age groups, the defect location and the herniated contents. Their lateral chest radiographs were analyzed for the shape of the posterior diaphragm and the posterior costophrenic sulcus. The patients' ages ranged from 34 to 87 with the tendency of a higher incidence in the older patients. The defect most frequently involved the medial two thirds (n = 49, 50.4%) and middle one third (n = 36, 37%) of the posterior diaphragm. The retroperitoneal fat was herniated into the thorax through the defect in all patients, and sometimes with the kidney (n = 8). Lateral chest radiography showed a normal diaphragmatic contour (n = 51, 49.5%), blunting of the posterior costophrenic sulcus (n = 41, 39.8%), focal humping of the posterior diaphragm (n = 7, 6.8%), or upward convexity (n = 4, 3.9%) of the posterior costophrenic sulcus on the affected side. The posterior diaphragmatic defect discovered in asymptomatic patients who are without a history of peridiaphragmatic disease is most likely acquired, and this malady increases in incidence according to age. An abnormal contour of the posterior diaphragm or the costophrenic sulcus on a lateral chest radiograph may be a finding of posterior diaphragmatic defect.

  6. Incidence and Interrelated Factors in Patients With Congenital Hypothyroidism as Detected by Newborn Screening in Guangxi, China

    Directory of Open Access Journals (Sweden)

    Xin Fan MD

    2015-01-01

    Full Text Available Background. A newborn screening program (NSP for congenital hypothyroidism (CH was carried out in Guangxi in order to understand the incidence of CH and the factors interrelated to major types of CH in this region of China. Methods. During 2009 to 2013, data from 930 612 newborns attending NSP in Guangxi were collected. Patients were classified with either permanent CH (PCH or transient CH (TCH after 2 years of progressive study. Results. A total of 1210 patients were confirmed with CH with an incidence of 1/769, including 68 PCH and 126 TCH cases with incidences of 1/6673 and 1/3385, respectively. The frequency of thyroid stimulating hormone values greater than 5 mIU/L was 7.2%, which, based on WHO guidelines, suggests that the population was mildly iodine deficient. Conclusions. The incidence of CH was high in Guangxi. Approximately two thirds of CH patients were TCH, which may be due to a deficiency in iodine within the population.

  7. MaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles

    Science.gov (United States)

    Henrique Castelo Branco, Luiz; César Lima Segantine, Paulo

    2015-09-01

    Intelligent Transportation Systems - ITS is a set of integrated technologies (Remote Sensing, Image Processing, Communications Systems and others) that aim to offer services and advanced traffic management for the several transportation modes (road, air and rail). Collect data on the characteristics and conditions of the road surface and keep them update is an important and difficult task that needs to be currently managed in order to reduce accidents and vehicle maintenance costs. Nowadays several roads and highways are paved, but usually there is insufficient updated data about current condition and status. There are different types of pavement defects on the roads and to keep them in good condition they should be constantly monitored and maintained according to pavement management strategy. This paper presents a methodology to obtain, automatically, information about the conditions of the highway asphalt pavement. Data collection was done through remote sensing using an UAV (Unmanned Aerial Vehicle) and the image processing and pattern recognition techniques through Geographic Information System.

  8. An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

    Science.gov (United States)

    Du, Q.; Xie, D.; Sun, Y.

    2015-06-01

    The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other one is Position and Orientation System (POS) data supported aerotriangulation. The high precision registration points are selected as Ground Control Points (GCPs) instead of measuring GCPs manually during aerotriangulation. The registration experiments indicate that the method which registering aerial images and LiDAR points has a great advantage in higher automation and precision compare with manual registration.

  9. Impact of lower- vs. upper-hemifield presentation on automatic colour-deviance detection: a visual mismatch negativity study.

    Science.gov (United States)

    Müller, Dagmar; Roeber, Urte; Winkler, István; Trujillo-Barreto, Nelson; Czigler, István; Schröger, Erich

    2012-09-07

    The automatic processing of deviances from the temporal context of the visual environment has become an important topic in visual cognitive sciences, which is often investigated using the visual mismatch negativity (vMMN). This event-related potential (ERP) component is elicited by an irregular stimulus (e.g., a red disc) presented in a series of stimuli (e.g., green discs) comprising a temporal regularity (e.g., colour repetition). We determined the influence of lower- vs. upper-hemifield presentation of the irregular stimulus on the vMMN while using whole-field stimulus displays controlling for sustained shifts in spatial attention. Deviances presented in the lower hemifield elicited a larger vMMN than the ones presented in the upper hemifield at a latency of 200-280ms. However, this asymmetry was preceded by deviance-related hemifield effects already emerging at an earlier latency (110-150ms), where upper-hemifield deviances elicited a positive potential but lower-hemifield deviances did not. With variable resolution electromagnetic tomography (VARETA) early deviance-related activity was localised to retinotopically organised regions of the visual cortex (BA 17/18) and vMMN-sources were localised to the middle/superior occipital gyrus, to higher areas along the temporal visual stream, but also to BA 17/18. We argue that the upper/lower-hemifield vMMN asymmetry relies at least partially on the hemifield-dependent differential sensitivity of early deviance-related activity generated in retinotopically organised regions of the visual cortex. However, a superior automatic processing of deviances presented in the lower visual hemifield may also contribute to the effect.

  10. Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Kalman filter approaches.

    Science.gov (United States)

    Kaakinen, M; Huttunen, S; Paavolainen, L; Marjomäki, V; Heikkilä, J; Eklund, L

    2014-01-01

    Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.

  11. Automatic Detecting a Bunch of Cash Based on Machine Vision Systems%基于机器视觉的智能卡把系统

    Institute of Scientific and Technical Information of China (English)

    张海宁; 许飞; 冯晓岗

    2012-01-01

    卡把是指对捆钱的把数进行数目的核定.目前的卡把操作是人工的,不仅费时,而且可能出现误判.基于机器视觉的卡把系统通过摄像机取像,将被摄取的目标信号转换成图像信号,并将此图像信号传送给专用的图像处理系统,抽取图像的特征,进而根据判别的结果来控制智能设备的执行相应的操作.本系统能够自动进行智能卡把操作,并对不符合规范的钱捆进行报警和处理.不仅提高了卡把速度,而且极大降低了卡把的误判率.%Detecting a bunch of cash is refers to approve the number of money. Recently, this operation is manual, which not only takes time, but also appears a miscalculation. Automatic detecting a bunch of cash based on machine vision system gets the picture through the camera, makes the target signal convert into image signal, and takes the image signals lo the dedicated image processing system, extracts the image characteristics, and then controls the equipment to do the corresponding operation according to the result of discrimination. Tliis system can be automatically detected a bunch of cash, and alarms and handles money which is not up to standard, which not only improves the speed, and greatly reduces the false rate.

  12. Automatic non-destructive three-dimensional acoustic coring system for in situ detection of aquatic plant root under the water bottom

    Directory of Open Access Journals (Sweden)

    Katsunori Mizuno

    2016-05-01

    Full Text Available Digging is necessary to detect plant roots under the water bottom. However, such detection is affected by the transparency of water and the working skills of divers, usually requires considerable time for high-resolution sampling, and always damages the survey site. We developed a new automatic non-destructive acoustic measurement system that visualizes the space under the water bottom, and tested the system in the in situ detection of natural plant roots. The system mainly comprises a two-dimensional waterproof stage controlling unit and acoustic measurement unit. The stage unit was electrically controlled through a notebook personal computer, and the space under the water bottom was scanned in a two-dimensional plane with the stage unit moving in steps of 0.01 m (±0.0001 m. We confirmed a natural plant root with diameter of 0.025–0.030 m in the reconstructed three-dimensional acoustic image. The plant root was at a depth of about 0.54 m and the propagation speed of the wave between the bottom surface and plant root was estimated to be 1574 m/s. This measurement system for plant root detection will be useful for the non-destructive assessment of the status of the space under the water bottom.

  13. Using chi-Squared Automatic Interaction Detection (CHAID) modelling to identify groups of methadone treatment clients experiencing significantly poorer treatment outcomes.

    Science.gov (United States)

    Murphy, Emma L; Comiskey, Catherine M

    2013-10-01

    In times of scarce resources it is important for services to make evidence based decisions when identifying clients with poor outcomes. chi-Squared Automatic Interaction Detection (CHAID) modelling was used to identify characteristics of clients experiencing statistically significant poor outcomes. A national, longitudinal study recruited and interviewed, using the Maudsley Addiction Profile (MAP), 215 clients starting methadone treatment and 78% were interviewed one year later. Four CHAID analyses were conducted to model the interactions between the primary outcome variable, used heroin in the last 90 days prior to one year interview and variables on drug use, treatment history, social functioning and demographics. Results revealed that regardless of these other variables, males over 22 years of age consistently demonstrated significantly poorer outcomes than all other clients. CHAID models can be easily applied by service providers to provide ongoing evidence on clients exhibiting poor outcomes and requiring priority within services.

  14. Automatic detection of flaws in polymer insulators using 3D industrial tomography; Deteccao automatica de vazios em isoladores polimericos por tomografia industrial 3D

    Energy Technology Data Exchange (ETDEWEB)

    Godoi, Walmor Cardoso; Swinka-Filho, Vitoldo [Instituto de Tecnologia para o Desenvolvimento (LACTEC), Curitiba, PR (Brazil)], Emails: walmor@lactec.org.br, vitoldo@lactec.org.br; Geus, Klaus de [Companhia Paranaense de Energia (Copel), Curitiba, PR (Brazil)], Email: klaus@copel.com; Silva, Romeu Ricardo da [SENAI-RJ Solda (CTS/Solda), Rio de Janeiro, RJ (Brazil). Centro de Tecnologia], Email: rrdsilva@firjan.org.br

    2009-10-15

    This work presents a methodology for the automatic detection of flaws in polymer insulators using three-dimensional industrial computed tomography (CT), as well as results obtained in the context of power distribution networks. The CT slices were reconstructed using 180 digital radiographs (projections) acquired by a high resolution system (pixel dimension of 50 {mu}m x 50 {mu}m, a-Si). For the reconstruction of 3D CT, 50 {mu}m wide bit map slices were used. The Marching Cubes algorithm was used to perform the 3D reconstruction, using the Visualization Tool kit (VTK) library and the Java programming language (64 Bits Linux platform). Nine features were obtained from the reconstructed three-dimensional objects for the neural networks training. Results showed to be satisfactory. (author)

  15. An automatic method for fast and accurate liver segmentation in CT images using a shape detection level set method

    Science.gov (United States)

    Lee, Jeongjin; Kim, Namkug; Lee, Ho; Seo, Joon Beom; Won, Hyung Jin; Shin, Yong Moon; Shin, Yeong Gil

    2007-03-01

    Automatic liver segmentation is still a challenging task due to the ambiguity of liver boundary and the complex context of nearby organs. In this paper, we propose a faster and more accurate way of liver segmentation in CT images with an enhanced level set method. The speed image for level-set propagation is smoothly generated by increasing number of iterations in anisotropic diffusion filtering. This prevents the level-set propagation from stopping in front of local minima, which prevails in liver CT images due to irregular intensity distributions of the interior liver region. The curvature term of shape modeling level-set method captures well the shape variations of the liver along the slice. Finally, rolling ball algorithm is applied for including enhanced vessels near the liver boundary. Our approach are tested and compared to manual segmentation results of eight CT scans with 5mm slice distance using the average distance and volume error. The average distance error between corresponding liver boundaries is 1.58 mm and the average volume error is 2.2%. The average processing time for the segmentation of each slice is 5.2 seconds, which is much faster than the conventional ones. Accurate and fast result of our method will expedite the next stage of liver volume quantification for liver transplantations.

  16. Incidence and correlates of HIV-1 RNA detection in the breast milk of women receiving HAART for the prevention of HIV-1 transmission.

    Directory of Open Access Journals (Sweden)

    Jennifer A Slyker

    Full Text Available BACKGROUND: The incidence and correlates of breast milk HIV-1 RNA detection were determined in intensively sampled women receiving highly active antiretroviral therapy (HAART for the prevention of mother-to-child HIV-1 transmission. METHODS: Women initiated HAART at 34 weeks of pregnancy. Breast milk was collected every 2-5 days during 1 month postpartum for measurements of cell-associated HIV DNA and cell-free HIV RNA. Plasma and breast milk were also collected at 2 weeks, 1, 3 and 6 months for concurrent HIV-1 RNA and DNA measurements. Regression was used to identify cofactors for breast milk HIV-1 RNA detection. RESULTS: Of 259 breast milk specimens from 25 women receiving HAART, 34 had detectable HIV-1 RNA (13%, incidence 1.4 episodes/100 person-days 95% CI = 0.97-1.9. Fourteen of 25 (56% women had detectable breast milk HIV-1 RNA [mean 2.5 log(10 copies/ml (range 2.0-3.9] at least once. HIV-1 DNA was consistently detected in breast milk cells despite HAART, and increased slowly over time, at a rate of approximately 1 copy/10(6 cells per day (p = 0.02. Baseline CD4, plasma viral load, HAART duration, and frequency of breast problems were similar in women with and without detectable breast milk HIV-1 RNA. Women with detectable breast milk HIV-1 RNA were more likely to be primiparous than women without (36% vs 0%, p = 0.05. Plasma HIV-1 RNA detection (OR = 9.0, 95%CI = 1.8-44 and plasma HIV-1 RNA levels (OR = 12, 95% CI = 2.5-56 were strongly associated with concurrent detection of breast milk HIV-1 RNA. However, no association was found between breast milk HIV-1 DNA level and concurrent breast milk HIV-1 RNA detection (OR = 0.96, 95%CI = 0.54-1.7. CONCLUSIONS: The majority of women on HAART had episodic detection of breast milk HIV-1 RNA. Breast milk HIV-1 RNA detection was associated with systemic viral burden rather than breast milk HIV-1 DNA.

  17. SU-E-T-310: Targeting Safety Improvements Through Analysis of Near-Miss Error Detection Points in An Incident Learning Database

    Energy Technology Data Exchange (ETDEWEB)

    Novak, A; Nyflot, M; Sponseller, P; Howard, J; Logan, W; Holland, L; Jordan, L; Carlson, J; Ermoian, R; Kane, G; Ford, E; Zeng, J [University of Washington, Seattle, WA (United States)

    2014-06-01

    Purpose: Radiation treatment planning involves a complex workflow that can make safety improvement efforts challenging. This study utilizes an incident reporting system to identify detection points of near-miss errors, in order to guide our departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or their patterns. Methods: 1377 incidents were analyzed from a departmental nearmiss error reporting system from 3/2012–10/2013. All incidents were prospectively reviewed weekly by a multi-disciplinary team, and assigned a near-miss severity score ranging from 0–4 reflecting potential harm (no harm to critical). A 98-step consensus workflow was used to determine origination and detection points of near-miss errors, categorized into 7 major steps (patient assessment/orders, simulation, contouring/treatment planning, pre-treatment plan checks, therapist/on-treatment review, post-treatment checks, and equipment issues). Categories were compared using ANOVA. Results: In the 7-step workflow, 23% of near-miss errors were detected within the same step in the workflow, while an additional 37% were detected by the next step in the workflow, and 23% were detected two steps downstream. Errors detected further from origination were more severe (p<.001; Figure 1). The most common source of near-miss errors was treatment planning/contouring, with 476 near misses (35%). Of those 476, only 72(15%) were found before leaving treatment planning, 213(45%) were found at physics plan checks, and 191(40%) were caught at the therapist pre-treatment chart review or on portal imaging. Errors that passed through physics plan checks and were detected by therapists were more severe than other errors originating in contouring/treatment planning (1.81 vs 1.33, p<0.001). Conclusion: Errors caught by radiation treatment therapists tend to be more severe than errors caught earlier in the workflow, highlighting the importance of safety

  18. No effects of a single 3G UMTS mobile phone exposure on spontaneous EEG activity, ERP correlates, and automatic deviance detection.

    Science.gov (United States)

    Trunk, Attila; Stefanics, Gábor; Zentai, Norbert; Kovács-Bálint, Zsófia; Thuróczy, György; Hernádi, István

    2013-01-01

    Potential effects of a 30 min exposure to third generation (3G) Universal Mobile Telecommunications System (UMTS) mobile phone-like electromagnetic fields (EMFs) were investigated on human brain electrical activity in two experiments. In the first experiment, spontaneous electroencephalography (sEEG) was analyzed (n = 17); in the second experiment, auditory event-related potentials (ERPs) and automatic deviance detection processes reflected by mismatch negativity (MMN) were investigated in a passive oddball paradigm (n = 26). Both sEEG and ERP experiments followed a double-blind protocol where subjects were exposed to either genuine or sham irradiation in two separate sessions. In both experiments, electroencephalograms (EEG) were recorded at midline electrode sites before and after exposure while subjects were watching a silent documentary. Spectral power of sEEG data was analyzed in the delta, theta, alpha, and beta frequency bands. In the ERP experiment, subjects were presented with a random series of standard (90%) and frequency-deviant (10%) tones in a passive binaural oddball paradigm. The amplitude and latency of the P50, N100, P200, MMN, and P3a components were analyzed. We found no measurable effects of a 30 min 3G mobile phone irradiation on the EEG spectral power in any frequency band studied. Also, we found no significant effects of EMF irradiation on the amplitude and latency of any of the ERP components. In summary, the present results do not support the notion that a 30 min unilateral 3G EMF exposure interferes with human sEEG activity, auditory evoked potentials or automatic deviance detection indexed by MMN.

  19. Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

    Science.gov (United States)

    Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

    2016-12-01

    Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

  20. Simultaneous Automatic Electrochemical Detection of Zinc, Cadmium, Copper and Lead Ions in Environmental Samples Using a Thin-Film Mercury Electrode and an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jiri Kudr

    2014-12-01

    Full Text Available In this study a device for automatic electrochemical analysis was designed. A three electrodes detection system was attached to a positioning device, which enabled us to move the electrode system from one well to another of a microtitre plate. Disposable carbon tip electrodes were used for Cd(II, Cu(II and Pb(II ion quantification, while Zn(II did not give signal in this electrode configuration. In order to detect all mentioned heavy metals simultaneously, thin-film mercury electrodes (TFME were fabricated by electrodeposition of mercury on the surface of carbon tips. In comparison with bare electrodes the TMFEs had lower detection limits and better sensitivity. In addition to pure aqueous heavy metal solutions, the assay was also performed on mineralized rock samples, artificial blood plasma samples and samples of chicken embryo organs treated with cadmium. An artificial neural network was created to evaluate the concentrations of the mentioned heavy metals correctly in mixture samples and an excellent fit was observed (R2 = 0.9933.

  1. Effects of automatic tube potential selection on radiation dose index, image quality, and lesion detectability in pediatric abdominopelvic CT and CTA: a phantom study

    Energy Technology Data Exchange (ETDEWEB)

    Brinkley, Michael F.; Choudhury, Kingshuk Roy; Frush, Donald P. [Duke University School of Medicine, Department of Radiology, DUMC Box 3808, Durham, NC (United States); Ramirez-Giraldo, Juan C. [Siemens Healthcare, Malvern (United States); Samei, Ehsan; Wilson, Joshua M.; Christianson, Olav I. [Duke University School of Medicine, Clinical Imaging Physics Group, Department of Radiology, Durham, NC (United States); Frush, Daniel J. [Duke University School of Medicine, Medical Physics, Durham, NC (United States)

    2016-01-15

    To assess the effect of automatic tube potential selection (ATPS) on radiation dose, image quality, and lesion detectability in paediatric abdominopelvic CT and CT angiography (CTA). A paediatric modular phantom with contrast inserts was examined with routine pitch (1.4) and high pitch (3.0) using a standard abdominopelvic protocol with fixed 120 kVp, and ATPS with variable kVp in non-contrast, contrast-enhanced, and CTA mode. The volume CT dose index (CTDI{sub vol}), contrast-to-noise ratio (CNR) and lesion detectability index (d') were compared between the standard protocol and ATPS examinations. CTDI{sub vol} was reduced in all routine pitch ATPS examinations, with dose reductions of 27-52 % in CTA mode (P < 0.0001), 15-33 % in contrast-enhanced mode (P = 0.0003) and 8-14 % in non-contrast mode (P = 0.03). Iodine and soft tissue insert CNR and d' were improved or maintained in all ATPS examinations. kVp and dose were reduced in 25 % of high pitch ATPS examinations and in none of the full phantom examinations obtained after a single full phantom localizer. ATPS reduces radiation dose while maintaining image quality and lesion detectability in routine pitch paediatric abdominopelvic CT and CTA, but technical factors such as pitch and imaging range must be considered to optimize ATPS benefits. (orig.)

  2. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset

    NARCIS (Netherlands)

    Aung, Min S.H.; Kaltwang, Sebastian; Romera-Paredes, Bernardino; Martinez, Brais; Singh, Aneesha; Cella, Matteo; Valstar, Michel; Meng, Hongying; Kemp, Andrew; Shafizadeh, Moshen; Elkins, Aaron C.; Kanakam, Natalie; Rothschild, de Amschel; Tyler, Nick; Watson, Paul J.; C. Williams, de Amanda C.; Pantic, Maja; Bianchi-Berthouze, Nadia

    2016-01-01

    Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through litera

  3. Advancing satellite-based solar power forecasting through integration of infrared channels for automatic detection of coastal marine inversion layer

    Energy Technology Data Exchange (ETDEWEB)

    Kostylev, Vladimir; Kostylev, Andrey; Carter, Chris; Mahoney, Chad; Pavlovski, Alexandre; Daye, Tony [Green Power Labs Inc., Dartmouth, NS (Canada); Cormier, Dallas Eugene; Fotland, Lena [San Diego Gas and Electric Co., San Diego, CA (United States)

    2012-07-01

    The marine atmospheric boundary layer is a layer or cool, moist maritime air with the thickness of a few thousand feet immediately below a temperature inversion. In coastal areas as moist air rises from the ocean surface, it becomes trapped and is often compressed into fog above which a layer of stratus clouds often forms. This phenomenon is common for satellite-based solar radiation monitoring and forecasting. Hour ahead satellite-based solar radiation forecasts are commonly using visible spectrum satellite images, from which it is difficult to automatically differentiate low stratus clouds and fog from high altitude clouds. This provides a challenge for cloud motion tyracking and cloud cover forecasting. San Diego Gas and Electric {sup registered} (SDG and E {sup registered}) Marine Layer Project was undertaken to obtain information for integration with PV forecasts, and to develop a detailed understanding of long-term benefits from forecasting Marine Layer (ML) events and their effects on PV production. In order to establish climatological ML patterns, spatial extent and distribution of marine layer, we analyzed visible and IR spectrum satellite images (GOES WEST) archive for the period of eleven years (2000 - 2010). Historical boundaries of marine layers impact were established based on the cross-classification of visible spectrum (VIS) and infrared (IR) images. This approach is successfully used by us and elsewhere for evaluating cloud albedo in common satellite-based techniques for solar radiation monitoring and forecasting. The approach allows differentiation of cloud cover and helps distinguish low laying fog which is the main consequence of marine layer formation. ML occurrence probability and maximum extent inland was established for each hour and day of the analyzed period and seasonal/patterns were described. SDG and E service area is the most affected region by ML events with highest extent and probability of ML occurrence. Influence of ML was the

  4. The incidence of metabolic syndrome in obese Czech children: the importance of early detection of insulin resistance using homeostatic indexes HOMA-IR and QUICKI.

    Science.gov (United States)

    Pastucha, D; Filipčíková, R; Horáková, D; Radová, L; Marinov, Z; Malinčíková, J; Kocvrlich, M; Horák, S; Bezdičková, M; Dobiáš, M

    2013-01-01

    Common alimentary obesity frequently occurs on a polygenic basis as a typical lifestyle disorder in the developed countries. It is associated with characteristic complex metabolic changes, which are the cornerstones for future metabolic syndrome development. The aims of our study were 1) to determine the incidence of metabolic syndrome (based on the diagnostic criteria defined by the International Diabetes Federation for children and adolescents) in Czech obese children, 2) to evaluate the incidence of insulin resistance according to HOMA-IR and QUICKI homeostatic indexes in obese children with and without metabolic syndrome, and 3) to consider the diagnostic value of these indexes for the early detection of metabolic syndrome in obese children. We therefore performed anthropometric and laboratory examinations to determine the incidence of metabolic syndrome and insulin resistance in the group of 274 children with obesity (128 boys and 146 girls) aged 9-17 years. Metabolic syndrome was found in 102 subjects (37 %). On the other hand, the presence of insulin resistance according to QUICKI 3.16 in 53 % of obese subjects. This HOMA-IR limit was exceeded by 70 % children in the MS(+) group, but only by 43 % children in the MS(-) group (pchildren without metabolic syndrome raises a question whether the existing diagnostic criteria do not falsely exclude some cases of metabolic syndrome. On the basis of our results we suggest to pay a preventive attention also to obese children with insulin resistance even if they do not fulfill the actual diagnostic criteria for metabolic syndrome.

  5. Study on rice chalkiness automatic detection algorithm for sorting processing%分选加工中稻米垩白自动检测算法

    Institute of Scientific and Technical Information of China (English)

    刘璎瑛; 丁为民; 李毅念; 陈建伟; 谢琴

    2013-01-01

    The rice chalky portion is defined as the opaque white portion in rice endosperm. Chalky rice not only affects its appearance quality, but also affects its cooking and taste quality, and then reduces the rice commodity price. Therefore, picking chalky grain in the processing of rice sorting has important practical value and economic value. In this paper, different rice combination images appearing in the sorting process was researched, and the rice kernels’ chalky portions were segmented automatically using image processing technology. According to the national standard requirements, chalky degree and chalky rice rate as rice chalky indexes were determined. First, the background image of the multi-grain rice image was segmented automatically in I color channel using an Otsu algorithm. Then, the segmented binary image and the original image were phased to get the rice image while removing the background. Viewing the rice transparent part as background and the rice chalky part as the foreground, the image was automatically segmented again using a Chebyshev approximation algorithm. The fake chalky areas in the image were removed using the area threshold method in a twice segmentation process. In this paper, a rice chalky portion automatic recognition algorithm and a chalky rice index detection algorithm were given and experimentally analyzed from their robustness, accuracy, and time-consuming aspects. The results showed that the algorithm could implement adaptive threshold selection, and realize the chalkiness complete segmentation of a combination image especially an image including yellow rice and rice with impurities, so the algorithm robustness was strong. According to the national standard requirements, one hundred rice kernels with 40%chalky rice rate were selected and different rice kernel images with a random combination were segmented to verify the accuracy and time-consuming of the algorithm. The results were that the chalky rice rate accuracy was 95%and

  6. A NEW APPROACH BASED ON THE DETECTION OF OPINION BY SENTIWORDNET FOR AUTOMATIC TEXT SUMMARIES BY EXTRACTION

    Directory of Open Access Journals (Sweden)

    Reda Mohamed HAMOU

    2015-11-01

    Full Text Available In this paper, we propose a new approach based on the detection of opinion by the SentiWordNet for the production of text summarization by using the scoring extraction technique adapted to detecting of opinion. The texts are decomposed into sentences then represented by a vector of scores of opinion of this sentences. The summary will be done by elimination of sentences whose opinion is different from the original text. This difference is expressed by a threshold opinion. The following hypothesis: "textual units that do not share the same opinion of the text are ideas used for the development or comparison and their absences have no vocation to reach the semantics of the abstract" Has been verified by the statistical measure of Chi_2 which we used it to calculate a dependence between the unit textual and the text. Finally we found an opinion threshold interval which generate the optimal assessments.

  7. Far cortex automatic detection aimed for partial or full bone drilling by a robot system in orthopaedic surgery

    Directory of Open Access Journals (Sweden)

    Tony Boiadjiev

    2017-01-01

    Full Text Available Far cortex detection during the bone-drilling process is a specific task in orthopaedic surgery. Any errors in its execution could damage the cortex wall from the inside, which often causes additional trauma even with a fatal result. Here we present some functionality enhancements of the drilling orthopaedic robot ODRO concerning the solution of the far cortex detection problem. The solution is based on software control of the thrust force applied to the bone during the drilling process. A new algorithm is created and its software realisation is provided. Experimental results are presented which verify and confirm the new functional characteristics of the robot. The risk of far cortex damage may be avoided by robot application and such precise operations may guarantee better success.

  8. Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

    Energy Technology Data Exchange (ETDEWEB)

    Aykac, Deniz [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK); Fox, Karen [Delta Health Alliance; Garg, Seema [University of North Carolina; Giancardo, Luca [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Nichols, Trent L [ORNL; Tobin Jr, Kenneth William [ORNL

    2011-01-01

    A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.

  9. Suggestions for automatic quantitation of endoscopic image analysis to improve detection of small intestinal pathology in celiac disease patients.

    Science.gov (United States)

    Ciaccio, Edward J; Bhagat, Govind; Lewis, Suzanne K; Green, Peter H

    2015-10-01

    Although many groups have attempted to develop an automated computerized method to detect pathology of the small intestinal mucosa caused by celiac disease, the efforts have thus far failed. This is due in part to the occult presence of the disease. When pathological evidence of celiac disease exists in the small bowel it is visually often patchy and subtle. Due to presence of extraneous substances such as air bubbles and opaque fluids, the use of computerized automation methods have only been partially successful in detecting the hallmarks of the disease in the small intestine-villous atrophy, fissuring, and a mottled appearance. By using a variety of computerized techniques and assigning a weight or vote to each technique, it is possible to improve the detection of abnormal regions which are indicative of celiac disease, and of treatment progress in diagnosed patients. Herein a paradigm is suggested for improving the efficacy of automated methods for measuring celiac disease manifestation in the small intestinal mucosa. The suggestions are applicable to both standard and videocapsule endoscopic imaging, since both methods could potentially benefit from computerized quantitation to improve celiac disease diagnosis.

  10. Automatic detection of lung nodules in computed tomography images: training and validation of algorithms using public research databases

    Science.gov (United States)

    Camarlinghi, Niccolò

    2013-09-01

    Lung cancer is one of the main public health issues in developed countries. Lung cancer typically manifests itself as non-calcified pulmonary nodules that can be detected reading lung Computed Tomography (CT) images. To assist radiologists in reading images, researchers started, a decade ago, the development of Computer Aided Detection (CAD) methods capable of detecting lung nodules. In this work, a CAD composed of two CAD subprocedures is presented: , devoted to the identification of parenchymal nodules, and , devoted to the identification of the nodules attached to the pleura surface. Both CADs are an upgrade of two methods previously presented as Voxel Based Neural Approach CAD . The novelty of this paper consists in the massive training using the public research Lung International Database Consortium (LIDC) database and on the implementation of new features for classification with respect to the original VBNA method. Finally, the proposed CAD is blindly validated on the ANODE09 dataset. The result of the validation is a score of 0.393, which corresponds to the average sensitivity of the CAD computed at seven predefined false positive rates: 1/8, 1/4, 1/2, 1, 2, 4, and 8 FP/CT.

  11. Automatic remote sensing detection of the convective boundary layer structure over flat and complex terrain using the novel PathfinderTURB algorithm

    Science.gov (United States)

    Poltera, Yann; Martucci, Giovanni; Hervo, Maxime; Haefele, Alexander; Emmenegger, Lukas; Brunner, Dominik; Henne, stephan

    2016-04-01

    We have developed, applied and validated a novel algorithm called PathfinderTURB for the automatic and real-time detection of the vertical structure of the planetary boundary layer. The algorithm has been applied to a year of data measured by the automatic LIDAR CHM15K at two sites in Switzerland: the rural site of Payerne (MeteoSwiss station, 491 m, asl), and the alpine site of Kleine Scheidegg (KSE, 2061 m, asl). PathfinderTURB is a gradient-based layer detection algorithm, which in addition makes use of the atmospheric variability to detect the turbulent transition zone that separates two low-turbulence regions, one characterized by homogeneous mixing (convective layer) and one above characterized by free tropospheric conditions. The PathfinderTURB retrieval of the vertical structure of the Local (5-10 km, horizontal scale) Convective Boundary Layer (LCBL) has been validated at Payerne using two established reference methods. The first reference consists of four independent human-expert manual detections of the LCBL height over the year 2014. The second reference consists of the values of LCBL height calculated using the bulk Richardson number method based on co-located radio sounding data for the same year 2014. Based on the excellent agreement with the two reference methods at Payerne, we decided to apply PathfinderTURB to the complex-terrain conditions at KSE during 2014. The LCBL height retrievals are obtained by tilting the CHM15K at an angle of 19 degrees with respect to the horizontal and aiming directly at the Sphinx Observatory (3580 m, asl) on the Jungfraujoch. This setup of the CHM15K and the processing of the data done by the PathfinderTURB allows to disentangle the long-transport from the local origin of gases and particles measured by the in-situ instrumentation at the Sphinx Observatory. The KSE measurements showed that the relation amongst the LCBL height, the aerosol layers above the LCBL top and the gas + particle concentration is all but

  12. Automatic detection of inundation-related change areas in TerraSAR-X data using Markov image modeling on irregular graphs

    Science.gov (United States)

    Martinis, Sandro; Twele, André

    2010-05-01

    The worldwide increasing occurrence of flooding and the short-time monitoring capability of the new generation of high resolution synthetic aperture radar (SAR) sensors (TerraSAR-X, COSMO-SkyMed) require accurate and automatic methods for the detection of flood dynamics. This is especially important for operational rapid mapping purposes where the near-real time provision of precise information about the extent of a disaster and its spatio-temporal evolution is of key importance to support decision makers and humanitarian relief organizations. A split based parametric thresholding approach under the generalized Gaussian assumption is developed on normalized change index data to automatically solve the three-class change detection problem in large-size images with small class a priori probabilities. The thresholding result is used for the initialization of a hybrid Markov model which integrates both scale-dependent and spatial context into the classification process by combining hierarchical with noncausal Markov image modeling on irregular graphs. Hierarchical Markov modeling is accomplished by hierarchical maximum a posteriori (HMAP) estimation using Markov Chains in scale. Since this method requires only one bottom-up and one top-down pass on the graph, it offers high computational performance. To reduce the computational demand of the iterative optimization process related to noncausal Markov image models, we define a partial Markov Random Field (MRF) approach, which is applied on a restricted region of the lowest level of the graph. The selection of this region is based on a confidence map generated by combining the HMAP labeling result from the different graph levels. The proposed unsupervised change detection method is applied on a bi-temporal TerraSAR-X StripMap data set (3 m pixel spacing) of a real flood event. The effectiveness of the hybrid Markov image model in comparison to the sole application of the HMAP estimation is evaluated. Additionally, the

  13. A Large Scale Automatic Earthquake Location Catalog in the San Jacinto Fault Zone Area Using An Improved Shear-Wave Detection Algorithm

    Science.gov (United States)

    White, M. C. A.; Ross, Z.; Vernon, F.; Ben-Zion, Y.

    2015-12-01

    UC San Diego's ANZA network began archiving event-triggered data in 1982. As a result of improved recording technology, continuous waveform data archives are available starting in 1998. This continuous dataset, from 1998-present, represents a wealth of potential insight into spatio-temporal seismicity patterns, earthquake physics and mechanics of the San Jacinto Fault Zone. However, the volume of data renders manual analysis costly. In order to investigate the characteristics of the data in space and time, an automatic earthquake location catalog is needed. To this end, we apply standard earthquake signal processing techniques to the continuous data to detect first-arriving P-waves in combination with a recently developed S-wave detection algorithm. The resulting dataset of arrival time observations are processed using a grid association algorithm to produce initial absolute locations which are refined using a location inversion method that accounts for 3-D velocity heterogeneities. Precise relative locations are then derived from the refined absolute locations using the HypoDD double-difference algorithm. Moment magnitudes for the events are estimated from multi-taper spectral analysis. A >650% increase in the S:P pick ratio is achieved using the updated S-wave detection algorithm, when compared to the currently available catalog for the ANZA network. The increased number of S-wave observations leads to improved earthquake location accuracy and reliability (ie. less false event detections). Various aspects of spatio-temporal seismicity patterns and size distributions are investigated. Updated results will be presented at the meeting.

  14. Automatic Reading

    Institute of Scientific and Technical Information of China (English)

    胡迪

    2007-01-01

    <正>Reading is the key to school success and,like any skill,it takes practice.A child learns to walk by practising until he no longer has to think about how to put one foot in front of the other.The great athlete practises until he can play quickly,accurately and without thinking.Ed- ucators call it automaticity.

  15. 航天发动机多余物自动检测系统%An Automatic Loose Particle Detection System for Aerospace Engines

    Institute of Scientific and Technical Information of China (English)

    戚乐; 赵国强; 陈金豹; 翟国富; 梁安生; 邓智

    2014-01-01

    Aimed at the shortcomings of conventional loose particle detection systems such as low efficiency and low precision, an automatic loose particle detection system for aerospace engines was de-veloped. It was composed of swivel stand driven by motor, driver control circuit, data conditioning and acquisition circuit. With the algorithms of threshold comparison and accumulated average energy compari-son, the computer gives results of particle size and location.%针对现有多余物检测装置检测效率低、精度低等问题,研制了一种航天发动机多余物自动检测系统,系统包括电机驱动转台的机械装置、驱动控制电路和信号调理采集电路。本文采用阈值比较和累积平均能量比较方法,实现多余物的粒径识别和空间定位。

  16. Dynamic contrast-enhanced MRI for automatic detection of foci @]@of residual or recurrent disease after prostatectomy

    Energy Technology Data Exchange (ETDEWEB)

    Parra, N.A.; Orman, Amber; Abramowitz, Matthew; Pollack, Alan; Stoyanova, Radka [University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, FL (United States); Padgett, Kyle [University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, FL (United States); University of Miami Miller School of Medicine, Department of Radiology, Miami, FL (United States); Casillas, Victor [University of Miami Miller School of Medicine, Department of Radiology, Miami, FL (United States); Punnen, Sanoj [University of Miami Miller School of Medicine, Department of Urology, Miami, FL (United States)

    2017-01-15

    This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the ''tumor'' pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0,.., DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. Principal component analysis determined 2-4 significant patterns in patients' DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). K{sup trans} for DCE2.5 was statistically higher than the GTV's K{sup trans} (p < 0.05), indicating that the automatic volume better captures areas of malignancy. A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system. (orig.) [German] Entwicklung eines automatischen Analyseverfahrens, um nach Prostatektomie mittels dynamischer kontrastmittelverstaerkter

  17. Technology and Application of the Automatic Detection in Testing Liquid Transport Properties of Textiles%织物液态水传递性能的自动检测技术及应用

    Institute of Scientific and Technical Information of China (English)

    詹永娟; 谢维斌; 姜晓云; 陈宝瑞; 虞树荣; 周小红

    2013-01-01

    Based on the vertical wicking measurement and image processing technology, this thesis introduces a kind of automatic detection device to test the liquid transport properties of textiles. According to the experiment, it shows that the deviations of the testing results obtained by the automatic detection device and the traditional test method are little, and the former method has high precision and simple operation , which can characterize the performance of water transportation through fabric well; and the automatic detection device with adjustable LED light is suitable for the automatic detection of liquid transport properties of fabrics from light-colored fabrics to deep-colored fabrics.%基于垂直芯吸法和图像处理技术,介绍了一种织物液态水传递性能的自动检测装置.并利用该装置进行了测试实验,结果表明:该自动检测装置得到的检测结果与传统检测结果偏差较小,测试精度高,操作简单方便,能较好地表征织物液态水传递特征;采用可调LED光源的织物湿传递自动检测装置适合浅色以及深色织物液态水传递性能的自动检测.

  18. The Design on Automatic Detection System of Ventilating and Leaking of Disposable Infusion Tube Based on PLC%基于PLC的一次性输液管通、漏气自动检测系统设计

    Institute of Scientific and Technical Information of China (English)

    夏链; 李福根; 韩春明

    2011-01-01

    一次性输液管是医疗行业不可或缺用来给病人进行静脉输液的器件.对其通气和漏气质量检测是其生产过程中极为重要的环节.通过调研发现,国内主要采用手工进行检测,自动检测装置很少,生产效率低.本文设计了一种基于PLC控制的自动检测装置.经过试验,能够实现自动检测,与手工检测相比,大大提高了工作效率和检测质量的可靠性.%Disposable infusion tube is essential for the medical industry,which is the device of intravenous infusion for the patient. The quality detecting of ventilating and leaking is an important part of the Production process. Through research, it has been found that most detecting is manual in our country and automatic detection devices are few,thus the production efficiency is low. A kind of automatic detection device based on PLC controlled has been designed. After testing, the results show that it can achieve automatic detection. Compared with the manual detecting,the efficiency and reliability of quality detecting greatly improve.

  19. Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

    Science.gov (United States)

    Barquero-Pérez, Óscar; Santiago-Mozos, Ricardo; Lillo-Castellano, José M.; García-Viruete, Beatriz; Goya-Esteban, Rebeca; Caamaño, Antonio J.; Rojo-Álvarez, José L.; Martín-Caballero, Carlos

    2017-01-01

    Accurate identification of Perinatal Hypoxia from visual inspection of Fetal Heart Rate (FHR) has been shown to have limitations. An automated signal processing method for this purpose needs to deal with time series of different lengths, recording interruptions, and poor quality signal conditions. We propose a new method, robust to those issues, for automated detection of perinatal hypoxia by analyzing the FHR during labor. Our system consists of several stages: (a) time series segmentation; (b) feature extraction from FHR signals, including raw time series, moments, and usual heart rate variability indices; (c) similarity calculation with Normalized Compression Distance, which is the key element for dealing with FHR time series; and (d) a simple classification algorithm for providing the hypoxia detection. We analyzed the proposed system using a database with 32 fetal records (15 controls). Time and frequency domain and moment features had similar performance identifying fetuses with hypoxia. The final system, using the third central moment of the FHR, yielded 92% sensitivity and 85% specificity at 3 h before delivery. Best predictions were obtained in time intervals more distant from delivery, i.e., 4–3 h and 3–2 h.

  20. Hybrid Taguchi-Objective Function optimization approach for automatic cave bird detection from terrestrial laser scanning intensity image

    Directory of Open Access Journals (Sweden)

    Mohammed O. Idrees

    2016-09-01

    Full Text Available This paper proposes an optimized Taguchi-objective function segmentation-based image analysis to detect bird nests in a cave from high resolution terrestrial laser scanning intensity images. First, the Taguchi orthogonal array was used to design 25 experiments with three segmentation parameters: scale, shape, and compactness, each having five variable factor levels. Then, a plateau objective function was computed for each experiment using their respective level combinations. A merger of the factor level combination in the orthogonal array and the computed plateau objective function values was used to generate main effects and interaction plots for signal-to-noise ratios, which provided a measure of robustness for scale, shape, and compactness factors. The optimized parameters were used in the segmentation process in eCognition. The image object was then classified into nest and cave-wall on the basis of laser return intensity and area index using knowledge-based rule sets, and the detection accuracy was evaluated. The result produced area under ROC curve of 0.93 with P2value of 5.10% at 95% confidence interval, respectively. This shows that the method is consistent with non-significant difference among the trials.