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. 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. PMID:27139389

  4. 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.

  5. 基于D-S理论的城市快速路交通事件自动检测算法%Urban Expressway Automatic Incident Detection Algorithm Based on D-S Theory

    Institute of Scientific and Technical Information of China (English)

    翁剑成; 赵晓娟; 荣建

    2011-01-01

    为有效提高快速路交通事件检测的覆盖率,解决算法误判率较高的问题,本文通过分析交通事件发生时交通流参数在时间维与空间维的变化,分别提出了基于固定检测器的多参数判别算法与基于浮动车的时空二维判别算法.当两个数据源算法同时满足检测条件时,研究以D-S理论为基础,将两个子算法有效地结合,实现事件的综合检测.最后,研究利用北京市快速路上采集的交通事件数据、固定检测器数据和浮动车数据对算法性能进行了检验.结果表明,基于固定检测器的多参数判别算法和基于浮动车的时空二维判别算法的检测率、误判率都达到了较好的效果,可以满足系统应用的需要.基于D-S理论的综合检测算法,具有比其他经典判别算法与两个子算法更低的算法误判率.%Two detection algorithms based on fixed detector data and floating car data were proposed separately according to the change of traffic parameters in the temporal and spatial dimensions to effectively improve the coverage of expressway automatic incident detection and to reduce false alarm rate ( FAR). Then, the two algorithms were integrated effectively according to the D-S theory to detect the accidents when both algorithms satisfy the detection condition simultaneously. Finally, the algorithms were verified with actual traffic data including the fixed detector data, the floating car data and the event data collected from Beijing expressway. The results show that both single data source based incident detection algorithms could meet the basic needs of practical application at detection rate ( DR.) and false alarm rate, and the false alarm rate (FAR) of the integrated detection algorithm based on D-S theory is lower than those of the (FAR) two sub-algorithms and other classical algorithms.

  6. 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...

  7. Computer systems for automatic earthquake detection

    Science.gov (United States)

    Stewart, S.W.

    1974-01-01

    U.S Geological Survey seismologists in Menlo park, California, are utilizing the speed, reliability, and efficiency of minicomputers to monitor seismograph stations and to automatically detect earthquakes. An earthquake detection computer system, believed to be the only one of its kind in operation, automatically reports about 90 percent of all local earthquakes recorded by a network of over 100 central California seismograph stations. The system also monitors the stations for signs of malfunction or abnormal operation. Before the automatic system was put in operation, all of the earthquakes recorded had to be detected by manually searching the records, a time-consuming process. With the automatic detection system, the stations are efficiently monitored continuously. 

  8. 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.

  9. 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...

  10. Automatic Hazard Detection for Landers

    Science.gov (United States)

    Huertas, Andres; Cheng, Yang; Matthies, Larry H.

    2008-01-01

    Unmanned planetary landers to date have landed 'blind'; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain,which in turn constrains the scientific agenda of missions. The state of the art Entry, Descent, and Landing (EDL) technology can land a spacecraft on Mars somewhere within a 20-100km landing ellipse.Landing ellipses are very likely to contain hazards such as craters, discontinuities, steep slopes, and large rocks, than can cause mission-fatal damage. We briefly review sensor options for landing hazard detection and identify a perception approach based on stereo vision and shadow analysis that addresses the broadest set of missions. Our approach fuses stereo vision and monocular shadow-based rock detection to maximize spacecraft safety. We summarize performance models for slope estimation and rock detection within this approach and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach can reduce the probability of failed landing by at least a factor of 4 in any given terrain. We also describe a rock detector/mapper applied to large-high-resolution images from the Mars Reconnaissance Orbiter (MRO) for landing site characterization and selection for Mars missions.

  11. Automatic Prosodic Break Detection and Feature Analysis

    Institute of Scientific and Technical Information of China (English)

    Chong-Jia Ni; Ai-Ying Zhang; Wen-Ju Liu; Bo Xu

    2012-01-01

    Automatic prosodic break detection and annotation are important for both speech understanding and natural speech synthesis.In this paper,we discuss automatic prosodic break detection and feature analysis.The contributions of the paper are two aspects.One is that we use classifier combination method to detect Mandarin and English prosodic break using acoustic,lexical and syntactic evidence.Our proposed method achieves better performance on both the Mandarin prosodic annotation corpus — Annotated Speech Corpus of Chinese Discourse and the English prosodic annotation corpus —Boston University Radio News Corpus when compared with the baseline system and other researches' experimental results.The other is the feature analysis for prosodic break detection.The functions of different features,such as duration,pitch,energy,and intensity,are analyzed and compared in Mandarin and English prosodic break detection.Based on the feature analysis,we also verify some linguistic conclusions.

  12. Automatic event detection for tennis broadcasting

    OpenAIRE

    Enebral González, Javier

    2011-01-01

    Within the image digital processing framework, this thesis is situated in the automatic content indexation field. Specifically during the project, different methods and techniques will be developed in order to achieve event detection for broadcasting tennis videos. Audiovisual indexation consists in the generation of descriptive tags based on the existing audiovisual data. All these tags are used to search the desired material in an efficient way. Televisions and other entities are l...

  13. 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.

  14. Automatic Time Skew Detection and Correction

    OpenAIRE

    Korchagin, Danil

    2011-01-01

    In this paper, we propose a new approach for the automatic time skew detection and correction for multisource audiovisual data, recorded by different cameras/recorders during the same event. All recorded data are successfully tested for potential time skew problem and corrected based on ASR-related features. The core of the algorithm is based on perceptual time-quefrency analysis with a precision of 10 ms. The results show correct time skew detection and elimination in 100% of cases for a rea...

  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. Automatic landslides detection on Stromboli volcanic Island

    Science.gov (United States)

    Silengo, Maria Cristina; Delle Donne, Dario; Ulivieri, Giacomo; Cigolini, Corrado; Ripepe, Maurizio

    2016-04-01

    Landslides occurring in active volcanic islands play a key role in triggering tsunami and other related risks. Therefore, it becomes vital for a correct and prompt risk assessment to monitor landslides activity and to have an automatic system for a robust early-warning. We then developed a system based on a multi-frequency analysis of seismic signals for automatic landslides detection occurring at Stromboli volcano. We used a network of 4 seismic 3 components stations located along the unstable flank of the Sciara del Fuoco. Our method is able to recognize and separate the different sources of seismic signals related to volcanic and tectonic activity (e.g. tremor, explosions, earthquake) from landslides. This is done using a multi-frequency analysis combined with a waveform patter recognition. We applied the method to one year of seismic activity of Stromboli volcano centered during the last 2007 effusive eruption. This eruption was characterized by a pre-eruptive landslide activity reflecting the slow deformation of the volcano edifice. The algorithm is at the moment running off-line but has proved to be robust and efficient in picking automatically landslide. The method provides also real-time statistics on the landslide occurrence, which could be used as a proxy for the volcano deformation during the pre-eruptive phases. This method is very promising since the number of false detections is quite small (detection as an improving tool for early warnings of tsunami-genic landslide activity. We suggest that a similar approach could be also applied to other unstable non-volcanic also slopes.

  18. 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.

  19. Automatic system for detecting pornographic images

    Science.gov (United States)

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

    2002-09-01

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

  20. Automatic Detect and Trace of Solar Filaments

    Science.gov (United States)

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

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

  1. Automatic Encoding and Language Detection in the GSDL

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2014-10-01

    Full Text Available Automatic detection of encoding and language of the text is part of the Greenstone Digital Library Software (GSDL for building and distributing digital collections. It is developed by the University of Waikato (New Zealand in cooperation with UNESCO. The automatic encoding and language detection in Slavic languages is difficult and it sometimes fails. The aim is to detect cases of failure. The automatic detection in the GSDL is based on n-grams method. The most frequent n-grams for Czech are presented. The whole process of automatic detection in the GSDL is described. The input documents to test collections are plain texts encoded in ISO-8859-1, ISO-8859-2 and Windows-1250. We manually evaluated the quality of automatic detection. To the causes of errors belong the improper language model predominance and the incorrect switch to Windows-1250. We carried out further tests on documents that were more complex.

  2. 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.

  3. Automatic detection of moving objects in video surveillance

    OpenAIRE

    Guezouli, Larbi; Belhani, Hanane

    2016-01-01

    This work is in the field of video surveillance including motion detection. The video surveillance is one of essential techniques for automatic video analysis to extract crucial information or relevant scenes in video surveillance systems. The aim of our work is to propose solutions for the automatic detection of moving objects in real time with a surveillance camera. The detected objects are objects that have some geometric shape (circle, ellipse, square, and rectangle).

  4. 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

  5. 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

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

    NARCIS (Netherlands)

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

    2007-01-01

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

  7. Automatic Detection of Wild-type Mouse Cranial Sutures

    DEFF Research Database (Denmark)

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

    , automatic detection of the cranial sutures becomes important. We have previously built a craniofacial, wild-type mouse atlas from a set of 10 Micro CT scans using a B-spline-based nonrigid registration method by Rueckert et al. Subsequently, all volumes were registered nonrigidly to the atlas. Using......, the observer traced the sutures on each of the mouse volumes as well. The observer outperforms the automatic approach by approximately 0.1 mm. All mice have similar errors while the suture error plots reveal that suture 1 and 2 are cumbersome, both for the observer and the automatic approach. These sutures can...

  8. Automatic detection of 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. PMID:25227013

  9. ALGORITHM FOR AUTOMATIC DETECTION OF ECG WAVES

    OpenAIRE

    Dib, Nabil; Bereksi-Reguig, Fethi

    2011-01-01

    An accurate measurement of the different electrocardiogram (ECG) intervals is dependent on the accurate identification of the beginning and the end of the P, QRS, and T waves. Available commercial systems provide a good QRS detection accuracy. However, the detection of the P and T waves remains a serious challenge due to their widely differing morphologies in normal and abnormal beats. In this paper, a new algorithm for the detection of the QRS complex as well as for P and T waves identificat...

  10. Automatic 3D video format detection

    Science.gov (United States)

    Zhang, Tao; Wang, Zhe; Zhai, Jiefu; Doyen, Didier

    2011-03-01

    Many 3D formats exist and will probably co-exist for a long time even if 3D standards are today under definition. The support for multiple 3D formats will be important for bringing 3D into home. In this paper, we propose a novel and effective method to detect whether a video is a 3D video or not, and to further identify the exact 3D format. First, we present how to detect those 3D formats that encode a pair of stereo images into a single image. The proposed method detects features and establishes correspondences between features in the left and right view images, and applies the statistics from the distribution of the positional differences between corresponding features to detect the existence of a 3D format and to identify the format. Second, we present how to detect the frame sequential 3D format. In the frame sequential 3D format, the feature points are oscillating from frame to frame. Similarly, the proposed method tracks feature points over consecutive frames, computes the positional differences between features, and makes a detection decision based on whether the features are oscillating. Experiments show the effectiveness of our method.

  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 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. The...... combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial...

  13. 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.

  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. Development of optical automatic positioning and wafer defect detection system

    International Nuclear Information System (INIS)

    The data of a wafer with defects can provide engineers with very important information and clues to improve the yield rate and quality in manufacturing. This paper presents a microscope automatic positioning and wafer detection system with human-machine interface based on image processing and fuzzy inference algorithms. In the proposed system, a XY table is used to move the position of each die on 6 inch or 8 inch wafers. Then, a high-resolution CCD and one set of two-axis optical linear encoder are used to accurately measure the position on the wafer. Finally, the developed human-machine interface is used to display the current position of an actual wafer in order to complete automatic positioning, and a wafer map database can be created. In the process of defect detection, CCD is used for image processing, and during preprocessing, it is required to filter noise, acquire the defect characteristics, define the defective template, and then take the characteristic points of the defective template as the reference input for fuzzy inference. A high-accuracy optical automatic positioning and wafer defect detection system is thus constructed. This study focused on automatic detection of spots, scratches, and bruises, and attempted to reduce the time to detect defective die and improve the accuracy of determining the defects of semiconductor devices. (paper)

  16. 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.

  17. 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.

  18. 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

  19. Examination techniques of the automatics fire detection monitoring system

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Yon Woo [Korea Atomic Energy Research Institute, Taejon (Korea)

    1999-04-01

    The variety of the automatic fire detection monitoring systems has been developed because the multistory buildings were constructed and the various structural materials were used. To stop the spread of the fire and minimize the damage of human life and properties of the facility, it should be informed precisely to all the members of the facility. (author). 12 refs., 28 figs.

  20. Automatically detecting auditory P300 in several trials

    Institute of Scientific and Technical Information of China (English)

    莫少锋; 汤井田; 陈洪波

    2015-01-01

    A method was demonstrated based on Infomax independent component analysis (Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component (IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.

  1. Automatic Fracture Detection Using Classifiers- A Review

    Directory of Open Access Journals (Sweden)

    S.K.Mahendran

    2011-11-01

    Full Text Available X-Ray is one the oldest and frequently used devices, that makes images of any bone in the body, including the hand, wrist, arm, elbow, shoulder, foot, ankle, leg (shin, knee, thigh, hip, pelvis or spine. A typical bone ailment is the fracture, which occurs when bone cannot withstand outside force like direct blows, twisting injuries and falls. Fractures are cracks in bones and are defined as a medical condition in which there is a break in the continuity of the bone. Detection and correct treatment of fractures are considered important, as a wrong diagnosis often lead to ineffective patient management, increased dissatisfaction and expensive litigation. The main focus of this paper is a review study that discusses about various classification algorithms that can be used to classify x-ray images as normal or fractured.

  2. Detection of incidents and events in urban networks

    NARCIS (Netherlands)

    Thomas, T.; Berkum, van E.C.

    2009-01-01

    Events and incidents are relatively rare, but they often have a negative impact on traffic. Reliable travel demand predictions during events and incident detection algorithms are thus essential. The authors study link flows that were collected throughout the Dutch city of Almelo. We show that reliab

  3. @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.

  4. Automatic detection of AutoPEEP during controlled mechanical ventilation

    Directory of Open Access Journals (Sweden)

    Nguyen Quang-Thang

    2012-06-01

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

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Yuan, Bo; He, Xiangqing; Liu, Ying

    2013-12-01

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

  8. A Novel Cascade Classifier for Automatic Microcalcification Detection.

    Science.gov (United States)

    Shin, Seung Yeon; Lee, Soochahn; Yun, Il Dong; Jung, Ho Yub; Heo, Yong Seok; Kim, Sun Mi; Lee, Kyoung Mu

    2015-01-01

    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. PMID:26630496

  9. 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.

  10. A Novel Cascade Classifier for Automatic Microcalcification Detection.

    Directory of Open Access Journals (Sweden)

    Seung Yeon Shin

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

  11. Automatic 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

  12. Automatic, non-intrusive, flame detection in pipelines

    Energy Technology Data Exchange (ETDEWEB)

    Morgan, M.D.; Mehta, S.A.; Moore, R.G. [Calgary Univ., AB (Canada). Dept. of Chemical and Petroleum Engineering; Al-Himyary, T.J. [Al-Himyary Consulting Inc., Calgary, AB (Canada)

    2004-07-01

    Flames have been known to occur within small diameter pipes operating under conditions of high turbulent flow. Although there are several methods of flame detection, few offer remote, non-line-of-site detection. In particular, combustion cannot be detected in cases where flammable mixtures are carried in flare lines, storage tank vents, air drilling or improperly designed purging operations. Combustion noise is being examined as a means to address this problem. A study was conducted in which flames within a small diameter tube were automatically detected using high speed pressure measurements and a newly developed algorithm. Commercially available, high-pressure, dynamic-pressure transducers were used for the measurements. The results of an experimental study showed that combustion noise can be distinguished from other sources of noise by its inverse power law relationship with frequency. This paper presented a newly developed algorithm which provides early detection of flames when combined with high-speed pressure measurements. The algorithm can also separate combustion noise automatically from other sources of noise when combined with other filters. In this study, the noise generated by a fluttering check valve was attenuated using a stop band filter. This detection method was found to be very reliable under the conditions tests, as long as there was no flow restriction between the sensor and the flame. A flow restriction would have resulted in the detection of only the strongest flame noise. It was shown that acoustic flame detection can be applied successfully in flare stacks, industrial burners and turbine combustors. It can be 15 times more sensitive than optical or electrical methods in diagnosing combustion problems with lean burning combustors. It may also be the only method available in applications that require remote, non-line-of-sight detection. 11 refs., 3 tabs., 15 figs.

  13. 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.

  14. 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.

  15. Automatic Medical Image Classification and Abnormality Detection Using KNearest Neighbour

    Directory of Open Access Journals (Sweden)

    Dr. R. J. Ramteke , Khachane Monali Y.

    2012-12-01

    Full Text Available This research work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction, Classification, and Post processing. Statistical texture feature set is derived from normal and abnormal images. We used the KNN classifier for classifying image. The KNN classifier performance compared with kernel based SVM classifier (Linear and RBF. The confusion matrix computed and result shows that KNN obtain 80% classification rate which is more than SVM classification rate. So we choose KNN algorithm for classification of images. If image classified as abnormal then post processing step applied on the image and abnormal region is highlighted on the image. The system has been tested on the number of real CT scan brain images.

  16. 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...... 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...... 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...

  17. Skin-contact sensor for automatic fall detection.

    Science.gov (United States)

    Narasimhan, Ravi

    2012-01-01

    This paper describes an adhesive sensor system worn on the skin that automatically detects human falls. The sensor, which consists of a tri-axial accelerometer, a microcon-troller and a Bluetooth Low Energy transceiver, can be worn anywhere on a subject's torso and in any orientation. In order to distinguish easily between falls and activities of daily living (ADL), a possible fall is detected only if an impact is detected and if the subject is horizontal shortly afterwards. As an additional criterion to reduce false positives, a fall is confirmed if the user activity level several seconds after a possible fall is below a threshold. Intentional falls onto a gymnastics mat were performed by 10 volunteers (total of 297 falls); ADL were performed by 15 elderly volunteers (total of 315 ADL). The fall detection algorithm provided a sensitivity of 99% and a specificity of 100%. PMID:23366814

  18. 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.

  19. An Investigation of Automatic Change Detection for Topographic Map Updating

    Science.gov (United States)

    Duncan, P.; Smit, J.

    2012-08-01

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

  20. Automatic detection of sudden commencements using neural networks

    Science.gov (United States)

    Segarra, A.; Curto, J. J.

    2013-07-01

    The aim of this work is to develop an automatic system to detect sudden commencements (SCs). SCs are produced by a sudden increase of solar wind dynamic pressure and are detected simultaneously everywhere on the ground (Araki, 1994). Since 1975, Ebro Observatory is responsible to elaborate the list of SC, following the morphological rules given by Mayaud (1973). Nowadays, this task is still done manually and presents some difficulties; the most worrying one is the decreasing number of observatories who collaborate with this task because most of them opted for the installation of unattended observatories. Hence, the necessity of an alternative method to continue the service becomes a urgency. The automatic method presented in this work is based on neural network analysis. To succeed with neural networks, we did a previous work of characterization and parameterization of SCs by statistical analysis. In this way, we focused on the determination of the appropriate parameters to be used as the inputs of the network which resulted to be: slope, change of magnetic activity and difference of the levels before and after the jump. We worked with X component and also with Y component. An important criteria introduced in this work is the necessary coherence of the results obtained with this new automatic method with those obtained with the manual method and reported in the old list of SC. Finally, the neural network is able to recognize the SC pattern successfully, but now this is achieved in a non-manned way. A robust quasi-real-time detection can be undertaken in the future.

  1. 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).

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Automatic verbal aggression detection for Russian and American imageboards

    OpenAIRE

    Gordeev, Denis

    2016-01-01

    The problem of aggression for Internet communities is rampant. Anonymous forums usually called imageboards are notorious for their aggressive and deviant behaviour even in comparison with other Internet communities. This study is aimed at studying ways of automatic detection of verbal expression of aggression for the most popular American (4chan.org) and Russian (2ch.hk) imageboards. A set of 1,802,789 messages was used for this study. The machine learning algorithm word2vec was applied to de...

  7. Automatic detection of infantry trenches based on terrain height maps

    OpenAIRE

    JUROŠ, BOR

    2015-01-01

    Remains of trenches used by infantry during World War 1 play an important role in understanding and reconstructing the happenings during one of the darkest eras of human history. They allow us to look at and simulate the course of war in Europe. With developments of Lidar systems and progress in the areas of mapping and topology, we can now observe the terrain previously hidden from our view. In our thesis we present an algorithm for automatic detection of trenches on non-uniform terrain. The...

  8. 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...... hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity...

  9. A fast automatic target detection method for detecting ships in infrared scenes

    Science.gov (United States)

    Özertem, Kemal Arda

    2016-05-01

    Automatic target detection in infrared scenes is a vital task for many application areas like defense, security and border surveillance. For anti-ship missiles, having a fast and robust ship detection algorithm is crucial for overall system performance. In this paper, a straight-forward yet effective ship detection method for infrared scenes is introduced. First, morphological grayscale reconstruction is applied to the input image, followed by an automatic thresholding onto the suppressed image. For the segmentation step, connected component analysis is employed to obtain target candidate regions. At this point, it can be realized that the detection is defenseless to outliers like small objects with relatively high intensity values or the clouds. To deal with this drawback, a post-processing stage is introduced. For the post-processing stage, two different methods are used. First, noisy detection results are rejected with respect to target size. Second, the waterline is detected by using Hough transform and the detection results that are located above the waterline with a small margin are rejected. After post-processing stage, there are still undesired holes remaining, which cause to detect one object as multi objects or not to detect an object as a whole. To improve the detection performance, another automatic thresholding is implemented only to target candidate regions. Finally, two detection results are fused and post-processing stage is repeated to obtain final detection result. The performance of overall methodology is tested with real world infrared test data.

  10. Automatic behavior sensing for a bomb-detecting dog

    Science.gov (United States)

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

    2015-05-01

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

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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. PMID:24977182

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

    Science.gov (United States)

    Peng, Xiaoling; Hou, Wenguang; Ding, Mingyue

    2009-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Ole Green

    2012-06-01

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

  18. Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system.

    Science.gov (United States)

    Shieh, Wann-Yun; Huang, Ju-Chin

    2012-09-01

    For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. PMID:22154761

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

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

  1. Automatic 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.

  2. Automatic Detection of Atrial Fibrillation for Mobile Devices

    Science.gov (United States)

    Kaiser, Stefanie; Kirst, Malte; Kunze, Christophe

    Two versions of a new detector for automatic real-time detection of atrial fibrillation in non-invasive ECG signals are introduced. The methods are based on beat to beat variability, tachogram analysis and simple signal filtering. The implementation on mobile devices is made possible due to the low demand on computing power of the employed analysis procedures. The proposed algorithms correctly identified 436 of 440 five minute episodes of atrial fibrillation or flutter and also correctly identified up to 302 of 342 episodes of no atrial fibrillation, including normal sinus rhythm as well as other cardiac arrhythmias. These numbers correspond to a sensitivity of 99.1 % and a specificity of 88.3%.

  3. 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.

  4. Automatic detection of informative frames from wireless capsule endoscopy images.

    Science.gov (United States)

    Bashar, M K; Kitasaka, T; Suenaga, Y; Mekada, Y; Mori, K

    2010-06-01

    Wireless capsule endoscopy (WCE) is a new clinical technology permitting visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the excessive amount of time required for video diagnosis. We therefore propose a method for generating smaller videos by detecting informative frames from original WCE videos. This method isolates useless frames that are highly contaminated by turbid fluids, faecal materials and/or residual foods. These materials and fluids are presented in a wide range of colors, from brown to yellow, and/or have bubble-like texture patterns. The detection scheme therefore consists of two steps: isolating (Step-1) highly contaminated non-bubbled (HCN) frames and (Step-2) significantly bubbled (SB) frames. Two color representations, viz., local color moments in Ohta space and the HSV color histogram, are attempted to characterize HCN frames, which are isolated by a support vector machine (SVM) classifier in Step-1. The rest of the frames go to Step-2, where a Gauss Laguerre transform (GLT) based multiresolution texture feature is used to characterize the bubble structures in WCE frames. GLT uses Laguerre Gauss circular harmonic functions (LG-CHFs) to decompose WCE images into multiresolution components. An automatic method of segmentation was designed to extract bubbled regions from grayscale versions of the color images based on the local absolute energies of their CHF responses. The final informative frames were detected by using a threshold on the segmented regions. An automatic procedure for selecting features based on analyzing the consistency of the energy-contrast map is also proposed. Three experiments, two of which use 14,841 and 37,100 frames from three videos and the rest uses 66,582 frames from six videos, were conducted for justifying the proposed method. The two combinations of the proposed color and texture features showed excellent average detection accuracies (86

  5. 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

  6. 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.

  7. 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 ...

  8. 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.

  9. 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.

  10. Rapid and automatic detection of brain tumors in MR images

    Science.gov (United States)

    Wang, Zhengjia; Hu, Qingmao; Loe, KiaFock; Aziz, Aamer; Nowinski, Wieslaw L.

    2004-04-01

    An algorithm to automatically detect brain tumors in MR images is presented. The key concern is speed in order to process efficiently large brain image databases and provide quick outcomes in clinical setting. The method is based on study of asymmetry of the brain. Tumors cause asymmetry of the brain, so we detect brain tumors in 3D MR images using symmetry analysis of image grey levels with respect to the midsagittal plane (MSP). The MSP, separating the brain into two hemispheres, is extracted using our previously developed algorithm. By removing the background pixels, the normalized grey level histograms are calculated for both hemispheres. The similarity between these two histograms manifests the symmetry of the brain, and it is quantified by using four symmetry measures: correlation coefficient, root mean square error, integral of absolute difference (IAD), and integral of normalized absolute difference (INAD). A quantitative analysis of brain normality based on 42 patients with tumors and 55 normals is presented. The sensitivity and specificity of IAD and INAD were 83.3% and 89.1%, and 85.7% and 83.6%, respectively. The running time for each symmetry measure for a 3D 8bit MR data was between 0.1 - 0.3 seconds on a 2.4GHz CPU PC.

  11. 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.

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

    International Nuclear Information System (INIS)

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

  13. Fast automatic algorithm for bifurcation detection in vascular CTA scans

    Science.gov (United States)

    Brozio, Matthias; Gorbunova, Vladlena; Godenschwager, Christian; Beck, Thomas; Bernhardt, Dominik

    2012-02-01

    Endovascular imaging aims at identifying vessels and their branches. Automatic vessel segmentation and bifurcation detection eases both clinical research and routine work. In this article a state of the art bifurcation detection algorithm is developed and applied on vascular computed tomography angiography (CTA) scans to mark the common iliac artery and its branches, the internal and external iliacs. In contrast to other methods our algorithm does not rely on a complete segmentation of a vessel in the 3D volume, but evaluates the cross-sections of the vessel slice by slice. Candidates for vessels are obtained by thresholding, following by 2D connected component labeling and prefiltering by size and position. The remaining candidates are connected in a squared distanced weighted graph. With Dijkstra algorithm the graph is traversed to get candidates for the arteries. We use another set of features considering length and shape of the paths to determine the best candidate and detect the bifurcation. The method was tested on 119 datasets acquired with different CT scanners and varying protocols. Both easy to evaluate datasets with high resolution and no apparent clinical diseases and difficult ones with low resolution, major calcifications, stents or poor contrast between the vessel and surrounding tissue were included. The presented results are promising, in 75.7% of the cases the bifurcation was labeled correctly, and in 82.7% the common artery and one of its branches were assigned correctly. The computation time was on average 0.49 s +/- 0.28 s, close to human interaction time, which makes the algorithm applicable for time-critical applications.

  14. 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.

  15. Automatic detection of confusion in elderly users of a web-based health instruction video

    NARCIS (Netherlands)

    Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

    2015-01-01

    BACKGROUND: Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare

  16. 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

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

    International Nuclear Information System (INIS)

    Highlights: • An oil platform located 70 km from the coast of Louisiana sank on Thursday. • Oil spill has backscatter values of −25 dB in RADARSAT-2 SAR. • Oil spill is portrayed in SCNB mode by shallower incidence angle. • Ideal detection of oil spills in SAR images requires moderate wind speeds. • Genetic algorithm is excellent tool for automatic detection of oil spill in RADARSAT-2 SAR data. - Abstract: 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

  18. 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

  19. 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.

  20. 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 ...

  1. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

    Science.gov (United States)

    Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior. PMID:27170879

  2. Objectification of magnetic particle crack detection by means of automatic evaluation of the indications

    Energy Technology Data Exchange (ETDEWEB)

    1981-07-15

    A description is given of an electronic device for the automatic detection of flaw indicators as well as the results of practical testing of the equipment and its limitations. 6 references, 10 figures.

  3. Automatic Eye Detection Error as a Predictor of Face Recognition Performance

    OpenAIRE

    Dutta, Abhishek; Veldhuis, Raymond; Spreeuwers, Luuk

    2014-01-01

    Various facial image quality parameters like pose, illumination, noise, resolution, etc are known to be a predictor of face recognition performance. However, there still remain many other properties of facial images that are not captured by the existing quality parameters. In this paper, we propose a novel image quality parameter called the Automatic Eye Detection Error (AEDE) which measures the difference between manually located and automatically detected eye coordinates. Our experiment res...

  4. [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.

  5. 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.

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

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    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...... must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian - they follow a multivariate t-distribution - hence, a dedicated generalized likelihood...

  7. 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.

  8. 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......Automatic detections of paroxysms in patients with childhood absence epilepsy have been neglected for several years. We acquire reliable detections using only a single-channel brainwave monitor, allowing for unobtrusive monitoring of antiepileptic drug effects. Ultimately we seek to obtain optimal...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. THE CLINICAL APPLICATIONS FOR AUTOMATIC DETECTION OF EXUDATES

    OpenAIRE

    K. Wisaeng; N. Hiransakolwong; E. Pothiruk

    2014-01-01

    Nowadays, the retinal imaging technology has been widely used for segmenting and detecting the exudates in diabetic retinopathy patients. Unfortunately, the retinal images in Thailand are poor-quality images. Therefore, detecting of exudates in a large number by screening programs, are very expensive in professional time and may cause human error. In this study, the clinical applications for detection of exudates from the poor quality retinal image are presented. An application incorporating ...

  16. 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.

  17. 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.

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

    NARCIS (Netherlands)

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

    2000-01-01

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

  19. 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.

  20. Relevance vector machine for automatic detection of clustered microcalcifications.

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M; Wernick, Miles N; Edwards, Alexandra

    2005-10-01

    Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms. PMID:16229415

  1. 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.

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

    Science.gov (United States)

    Liu, Tongran; Xiao, Tong; Shi, Jiannong

    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 recruited to complete an emotional oddball task featuring on happy and one fearful condition. The measurement of event-related potential was carried out via electroencephalography and electrooculography recording, to detect visual mismatch negativity (vMMN) with regard to the automatic detection of changes 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 adolescent’s posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information. PMID:27065927

  3. 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.

  4. 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.

  5. 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.

  6. 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. PMID:27344136

  7. 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.

  8. THE CLINICAL APPLICATIONS FOR AUTOMATIC DETECTION OF EXUDATES

    Directory of Open Access Journals (Sweden)

    K. Wisaeng

    2014-01-01

    Full Text Available Nowadays, the retinal imaging technology has been widely used for segmenting and detecting the exudates in diabetic retinopathy patients. Unfortunately, the retinal images in Thailand are poor-quality images. Therefore, detecting of exudates in a large number by screening programs, are very expensive in professional time and may cause human error. In this study, the clinical applications for detection of exudates from the poor quality retinal image are presented. An application incorporating function, including retinal color normalization, contrast enhancement, noise removal, color space selection and removal of the optic disc, was also designed to standardize the workflow of retinal analysis. Afterward, detection of exudate based on optimal global thresholding and improved adaptive Otsu’s algorithm was applied. Two experiments were conducted to validate the detection performance with local databases and a publicly available DIARETDB1 database. The first experiment showed the average sensitivity, specificity and accuracy of 93.8, 95.3 and 94.9%, respectively. The cross validation results of the second experiment, 60% (53 of the retinal images were used for training and 40% (36 for testing, the sensitivity, specificity and accuracy are 84.2, 85.9 and 85.2%, respectively. This result indicates the proposed clinical application provides an effective tool in the screening of exudates.

  9. 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.

  10. System for Automatic Detection of Clustered Microcalcifications in Digital Mammograms

    Science.gov (United States)

    Bazzani, A.; Bollini, D.; Brancaccio, R.; Campanini, R.; Lanconelli, N.; Romani, D.; Bevilacqua, A.

    In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.

  11. Automatic Fatigue Detection of Drivers through Yawning Analysis

    Science.gov (United States)

    Azim, Tayyaba; Jaffar, M. Arfan; Ramzan, M.; Mirza, Anwar M.

    This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The focus of the paper is on how to detect yawning which is an important cue for determining driver's fatigue. Initially, the face is located through Viola-Jones face detection method in a video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. The degree of mouth openness is extracted on the basis of mouth features, to determine driver's yawning state. If the yawning state of the driver persists for several consecutive frames, the system concludes that the driver is non-vigilant due to fatigue and is thus warned through an alarm. The system reinitializes when occlusion or misdetection occurs. Experiments were carried out using real data, recorded in day and night lighting conditions, and with users belonging to different race and gender.

  12. 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.

  13. Automatic detection and analysis of nuclear plant malfunctions

    International Nuclear Information System (INIS)

    In this paper a system is proposed, which performs dynamically the detection and analysis of malfunctions in a nuclear plant. The proposed method was developed and implemented on a Reactor Simulator, instead of on a real one, thus allowing a wide range of tests. For all variables under control, a simulation module was identified and implemented on the reactor on-line computer. In the malfunction identification phase all modules run separately, processing plant input variables and producing their output variable in Real-Time; continuous comparison of the computed variables with plant variables allows malfunction's detection. At this moment the second phase can occur: when a malfunction is detected, all modules are connected, except the module simulating the wrong variable, and a fast simulation is carried on, to analyse the consequences. (author)

  14. Automatic Detection of Exudates in Diabetic Retinopathy Images

    Directory of Open Access Journals (Sweden)

    Ekkarat Pothiruk

    2012-01-01

    Full Text Available Problem statement: Diabetic Retinopathy (DR is globally the primary cause of visual impairment and blindness in diabetic patients. Retinal image is essential and crucial for ophthalmologists to diagnose diseases. Many of technique can achieve good performance on retinal feature are clearly visible. Unfortunately, it is a normal situation that the retinal images in Thailand are low-quality images. The existing algorithm cannot detect low-quality image. Therefore, this study is part of a larger effort to develop a new method for detection of exudates in low quality retinal image. Approach: In this study, we presented a new method towards the development for detecting exudates pathologies of DR. The color retinal images are segmented using Fuzzy C-Means (FCM clustering and morphological methods and following key preprocessing step, i.e., color normalization, contrast enhancement, remove noise and color space selection. This enables its difference in our methods compared to other approach and the algorithm can achieve good performance even on low-quality retinal images. Result/Conclusion: The result shows that accuracy values increase when the FCM clustering is combined with morphological methods techniques. If any applications need to detect maximum number of exudates pixels or require execution speed, the FCM clustering technique could be used in isolation. However, if the applications require higher accuracy, the FCM clustering combined with morphological methods should be chosen. This system intends to help ophthalmologists in DR screening process to detect symptoms faster and more easily. This is not a final result application but it can be a preliminary diagnosis tool or decision support system for ophthalmologists. Human ophthalmologists are still needed for the cases where detection results are not very obvious.

  15. Fully automatic vertebra detection in x-ray images based on multi-class SVM

    Science.gov (United States)

    Lecron, Fabian; Benjelloun, Mohammed; Mahmoudi, Saïd

    2012-02-01

    Automatically detecting vertebral bodies in X-Ray images is a very complex task, especially because of the noise and the low contrast resulting in that kind of medical imagery modality. Therefore, the contributions in the literature are mainly interested in only 2 medical imagery modalities: Computed Tomography (CT) and Magnetic Resonance (MR). Few works are dedicated to the conventional X-Ray radiography and propose mostly semi-automatic methods. However, vertebra detection is a key step in many medical applications such as vertebra segmentation, vertebral morphometry, etc. In this work, we develop a fully automatic approach for the vertebra detection, based on a learning method. The idea is to detect a vertebra by its anterior corners without human intervention. To this end, the points of interest in the radiograph are firstly detected by an edge polygonal approximation. Then, a SIFT descriptor is used to train an SVM-model. Therefore, each point of interest can be classified in order to detect if it belongs to a vertebra or not. Our approach has been assessed by the detection of 250 cervical vertebræ on radiographs. The results show a very high precision with a corner detection rate of 90.4% and a vertebra detection rate from 81.6% to 86.5%.

  16. 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.

  17. A novel algorithm for automatic arrays detection in a layout

    Science.gov (United States)

    Shafee, Marwah; Park, Jea-Woo; Aslyan, Ara; Torres, Andres; Madkour, Kareem; ElManhawy, Wael

    2013-03-01

    Integrated circuits suffer from serious layout printability issues associated to the lithography manufacturing process. Regular layout designs are emerging as alternative solutions to help reducing these systematic sub-wavelength lithography variations. From CAD point of view, regular layouts can be treated as repeated patterns that are arranged in arrays. In most modern mask synthesis and verification tools, cell based hierarchical processing has been able to identify repeating cells by analyzing the design's cell placement; however, there are some routing levels which are not inside the cell and yet they create an array-like structure because of the underlying topologies which could be exploited by detecting repeated patterns in layout thus reducing simulation run-time by simulating only the representing cells and then restore all the simulation results in their corresponding arrays. The challenge is to make the array detection and restoration of the results a very lightweight operation to fully realize the benefits of the approach. A novel methodology for detecting repeated patterns in a layout is proposed. The main idea is based on translating the layout patterns into string of symbols and construct a "Symbolic Layout". By finding repetitions in the symbolic layout, repeated patterns in the drawn layout are detected. A flow for layout reduction based on arrays-detection followed by pattern-matching is discussed. Run time saving comes from doing all litho simulations on the base-patterns only. The pattern matching is then used to restore all the simulation results over the arrays. The proposed flow shows 1.4x to 2x run time enhancement over the regular litho simulation flow. An evaluation for the proposed flow in terms of coverage and run-time is drafted.

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

    Science.gov (United States)

    Maquet, Pierre

    2016-01-01

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

  19. 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.

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

    International Nuclear Information System (INIS)

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

  1. A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

    International Nuclear Information System (INIS)

    The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations

  2. 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.

  3. 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.

  4. 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.

  5. Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images

    OpenAIRE

    Liu, Sicong

    2015-01-01

    The increasing availability of the new generation remote sensing satellite hyperspectral images provides an important data source for Earth Observation (EO). Hyperspectral images are characterized by a very detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range. This important property makes it possible the monitoring of the land-cover dynamic and environmental evolution at a fine spectral scale. This also allows one to potentially detect subtle...

  6. Automatic detection of architectural violations in evolutionary systems

    OpenAIRE

    Albuquerque, Diego de Lara e

    2014-01-01

    Software applications evolve over the years at a cost: their architecture modularity tends to be degraded. This happens mainly because software application maintenance often leads to architectural degradation. In this context, software architects need to elaborate strategies for detecting architectural degradation symptoms and thus maintaining the software architectural quality. The elaborations of these strategies often rely on tools with domain-specific languages (DSLs), which help them to ...

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

    OpenAIRE

    Nualsawat HIRANSAKOLWONG; Ekkarat POTHIRUK; Kittipol WISAENG

    2013-01-01

    Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. Unfortunately, it is a normal situation that retinal images in Thailand are poor...

  8. Automatic Error Detection in Part of Speech Tagging

    CERN Document Server

    Elworthy, D

    1994-01-01

    A technique for detecting errors made by Hidden Markov Model taggers is described, based on comparing observable values of the tagging process with a threshold. The resulting approach allows the accuracy of the tagger to be improved by accepting a lower efficiency, defined as the proportion of words which are tagged. Empirical observations are presented which demonstrate the validity of the technique and suggest how to choose an appropriate threshold.

  9. Automatic burst detection for the EEG of the preterm infant

    International Nuclear Information System (INIS)

    To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29–34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment

  10. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

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

  11. 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.

  12. Automatic road surface defect detection from grayscale images

    Science.gov (United States)

    Ghanta, Sindhu; Birken, Ralf; Dy, Jennifer

    2012-04-01

    Video health monitoring of large road networks requires the repeated collection of surface images to detect the defects and their changes over time. Vehicle mounted video equipment can easily collect the data, but the amount of data that can be collected in a single day prohibits interactive or semi-automated processing schemes as they would also not be cost-effective. A new approach that is fully automated to detect road surface defects from large amounts of highresolution grayscale images is presented. The images are collected with a vehicle-mounted rear-facing 5MP video camera complemented by GPS based positioning information. Our algorithm starts by correcting the images for radial and angular distortion to get a bird's-eye view image. This results in images with known dimensions (consistent in width per pixel) which allow data to be accurately placed on geo-referenced maps. Each of the pixels in the image is labeled as crack or non-crack using a Markov Random Field (MRF) approach. The data used for testing and training are disjoint sets of images collected from the streets of Boston, MA, USA. We compare our road surface defect detection results with other techniques/algorithms described in the literature for accuracy and robustness.

  13. Automatic detection of service initiation signals used in bars

    Directory of Open Access Journals (Sweden)

    Sebastian eLoth

    2013-08-01

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

  14. 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.

  15. 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

    Rhine- Westphalia, Germany. A link to an example with ASTER data to detect change with the same method after the 2005 Kashmir earthquake is given. The method is also used to automatically normalize multitemporal, multispectral Landsat ETM+ data radiometrically. IDL/ENVI, Python and Matlab software...

  16. Automatic ultrasonic system for flaw detection and dimensional measurement of precision tubes

    International Nuclear Information System (INIS)

    This paper describes a system, which is installed at Nuclear Fuel Complex, Hyderabad. It is a tube rotation fixed probe type of system designed for fully automatic operation at high speed using immersion technique for ultrasonic flaw detection and dimensional measurement of precision of zirconium alloy seamless tubes used in fuel bundles for nuclear reactors

  17. Automatic audio-visual fusion for aggression detection using meta-information

    NARCIS (Netherlands)

    Lefter, I.; Burghouts, G.J.; Rothkrantz, L.J.M.

    2012-01-01

    We propose a new method for audio-visual sensor fusion and apply it to automatic aggression detection. While a variety of definitions of aggression exist, in this paper we see it as any kind of behavior that has a disturbing effect on others. We have collected multi- and unimodal assessments by huma

  18. Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

    OpenAIRE

    Shiota, Sayaka; Villavicencio, Fernando; Yamagishi, Junichi; Ono, Nobutaka; Echizen, Isao; Matsui, Tomoko

    2015-01-01

    This paper proposes a novel countermeasure framework to detect spoofing attacks to reduce the vulnerability of automatic speaker verification (ASV) systems. Recently, ASV systems have reached equivalent performances equivalent to those of other biometric modalities. However, spoofing techniques against these systems have also progressed drastically. Experimentation using advanced speech synthesis and voice conversion techniques has showed unacceptable false acceptance rates and several new co...

  19. 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.

  20. Automatic detection of flats on the rolling stock wheels

    Directory of Open Access Journals (Sweden)

    J. Madejski

    2006-04-01

    Full Text Available Purpose: The goal of this work was increasing safety of tram, metro and trams operation.Design/methodology/approach: The accelerometers were fixed directly to the rail to provide the system with the best defect signal quality. Analysis of the acoustic signals collected using microphones proved that too much of the background noise limited their usefulness.Findings: It has been proven that all wheel geometry defects can be reliably detected and classified according to the experimentally established defect categories.Research limitations/implications: Exact measurements of the wheel defect geometry may be possible only after collecting huge signals time series along with the wheel measurements taken with other methods to reveal the relationships between them. This goal is hard to achieve, as the system performs already very well, and such experiments would be very costly and time consuming.Practical implications: Integrated wheel geometrical data collected from the wheel ovality, flat spots, and build-up detection system along with the wheel profile information have eliminated all derailments due to faulty wheel geometry.Originality/value: The system presented is the first wheel monitoring application in Poland, its unique feature is that it can be used at low speeds, like those allowed in depots.

  1. Edge Detection Techniques for Automatic Location of Spectra

    Science.gov (United States)

    Zarate, N.; Labrie, K.

    2012-09-01

    To improve the processing of multi-object or cross-dispersed spectroscopic data, especially for systems resulting in curved 2-D spectra, we have implemented in Python edge detection techniques widely used in the photo processing and remote sensing world. The software uses the discontinuity found in a spectral image to precisely locate each dispersed 2-D spectrum on the pixel array. A valid spectrum image edge is defined as continuous and sharp. To this end the best input data is a well illuminated flat field. The algorithm applies a discontinuity detection filter to the image. We find that a 3 × 3 Sobel kernel reliably produces easily traceable edges on our data. Some instruments produce data with large background noise. In those cases, a mild smoothing filter is first applied to reduce noise spikes that would otherwise confuse the edge tracing algorithm. The edges highlighted by the filtering are traced using the SciPy function label. Each edge is represented by a second degree polynomial that follows each slit edge. Currently the software assumes that the spectra are nearly horizontal or nearly vertical. This constraint can easily be lifted with the choice of a different convolution kernel.

  2. AUTOMATIC PCB DEFECT DETECTION USING IMAGE SUBTRACTION METHOD

    Directory of Open Access Journals (Sweden)

    Sonal Kaushik

    2012-10-01

    Full Text Available A printed circuit board, or (PCB is used to mechanically supportand electrically connect electronic components using conductivepathways, track or signal traces etched from copper sheetslaminated onto anon conductive substrate. The automaticinspection of PCBs serves a purpose which is traditional incomputer technology. The purpose is to relieve human inspectorsof the tedious and inefficient task of looking for those defects inPCBs which could lead to electric failure. In this project MachineVision PCB Inspection System is applied at the first step ofmanufacturing, i.e., the making of bare PCB. We first compare aPCB standard image with a PCB image, using a simple subtractionalgorithm that can highlight the main problem-regions. We havealso seen the effect of noise in a PCB image that at what level thismethod is suitable to detect the faulty image. Our focus is to detectdefects on printed circuit boards & to see the effect of noise.Typical defects that can be detected are over etchings (opens,under-etchings (shorts, holes etc.

  3. 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.

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

    DEFF Research Database (Denmark)

    Willersrud, Anders; Imsland, Lars; Blanke, Mogens;

    2015-01-01

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

  5. 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.

  6. Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data

    International Nuclear Information System (INIS)

    Synthetic aperture radar (SAR) is a precious foundation of oil spill detection, surveying and monitoring that improves oil spill detection by various approaches. The main objective of this work is to design automatic detection procedures for oil spill in synthetic aperture radar (SAR) satellite data. In doing so the Entropy algorithm tool was designed to investigate the occurrence of oil spill in Gulf of Mexico using RADARSAT-2 SAR satellite data. The study shows that entropy algorithm provides accurate pattern of oil slick in SAR data. This shown by 90% for oil spill, 3% look-alike and 7% for sea roughness using the receiver -operational characteristics (ROC) curve. It can therefore be concluded Entropy algorithm can be used as automatic tool for oil spill detection in RADARSAT-2 SAR data

  7. 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.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Nualsawat HIRANSAKOLWONG

    2013-02-01

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

  10. 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

  11. A Survey on Automatic Fall Detection in the Context of Ambient Assisted Living Systems

    Directory of Open Access Journals (Sweden)

    Velislava Spasova

    2014-03-01

    Full Text Available Ambient Assisted Living (AAL systems are a relatively new and expanding area of research. Due to current demographic trends towards gentrification of the population AAL systems are bound to become more important in todays and near future’s societies. Fall detection is an important component of AAL systems which could provide better safety and higher independency of the elderly. This paper presents a survey on automatic fall detection in the context of AAL systems.

  12. Automatic Urban Illegal Building Detection Using Multi-Temporal Satellite Images and Geospatial Information Systems

    Science.gov (United States)

    Khalili Moghadam, N.; Delavar, M. R.; Hanachee, P.

    2015-12-01

    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.

  13. Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel

    Science.gov (United States)

    Petrillo, Antonella; Fusco, Roberta; Filice, Salvatore; Granata, Vincenza; Catalano, Orlando; Vallone, Paolo; Di Bonito, Maurizio; D’Aiuto, Massimiliano; Rinaldo, Massimo; Capasso, Immacolata; Sansone, Mario

    2016-01-01

    Purpose To obtain breast vascular map and to assess correlation between predominant feeding vessel and tumor location with a semi-automatic method compared to conventional radiologic reading. Methods 148 malignant and 75 benign breast lesions were included. All patients underwent bilateral MR imaging. Written informed consent was obtained from the patients before MRI. The local ethics committee granted approval for this study. Semi-automatic breast vascular map and predominant vessel detection was performed on MRI, for each patient. Semi-automatic detection (depending on grey levels threshold manually chosen by radiologist) was compared with results of two expert radiologists; inter-observer variability and reliability of semi-automatic approach were assessed. Results Anatomic analysis of breast lesions revealed that 20% of patients had masses in internal half, 50% in external half and the 30% in subareolar/central area. As regards the 44 tumors in internal half, based on radiologic consensus, 40 demonstrated a predominant feeding vessel (61% were supplied by internal thoracic vessels, 14% by lateral thoracic vessels, 16% by both thoracic vessels and 9% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 38 tumors demonstrated a predominant feeding vessel (66% were supplied by internal thoracic vessels, 11% by lateral thoracic vessels, 9% by both thoracic vessels and 14% had no predominant feeding vessel—p<0.01). As regards the 111 tumors in external half, based on radiologic consensus, 91 demonstrated a predominant feeding vessel (25% were supplied by internal thoracic vessels, 39% by lateral thoracic vessels, 18% by both thoracic vessels and 18% had no predominant feeding vessel—p<0.01), based on semi-automatic detection, 94 demonstrated a predominant feeding vessel (27% were supplied by internal thoracic vessels, 45% by lateral thoracic vessels, 4% by both thoracic vessels and 24% had no predominant feeding vessel—p<0.01). An

  14. 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.

  15. Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

    Directory of Open Access Journals (Sweden)

    Harri Kaartinen

    2010-04-01

    Full Text Available There is currently high interest in developing automated methods to assist the updating of map databases. This study presents methods for automatic detection of buildings and changes in buildings from airborne laser scanner and digital aerial image data and shows the potential usefulness of the methods with thorough experiments in a 5 km2 suburban study area. 96% of buildings larger than 60 m2 were correctly detected in the building detection. The completeness and correctness of the change detection for buildings larger than 60 m2 were about 85% (including five classes. Most of the errors occurred in small or otherwise problematic buildings.

  16. Long baseline stereovision for automatic detection and ranging of moving objects in the night sky.

    Science.gov (United States)

    Danescu, Radu; Oniga, Florin; Turcu, Vlad; Cristea, Octavian

    2012-01-01

    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. PMID:23201979

  17. Aircraft noise effects on sleep: a systematic comparison of EEG awakenings and automatically detected cardiac activations

    International Nuclear Information System (INIS)

    Polysomnography is the gold standard for investigating noise effects on sleep, but data collection and analysis are sumptuous and expensive. We recently developed an algorithm for the automatic identification of cardiac activations associated with cortical arousals, which uses heart rate information derived from a single electrocardiogram (ECG) channel. We hypothesized that cardiac activations can be used as estimates for EEG awakenings. Polysomnographic EEG awakenings and automatically detected cardiac activations were systematically compared using laboratory data of 112 subjects (47 male, mean ± SD age 37.9 ± 13 years), 985 nights and 23 855 aircraft noise events (ANEs). The probability of automatically detected cardiac activations increased monotonically with increasing maximum sound pressure levels of ANEs, exceeding the probability of EEG awakenings by up to 18.1%. If spontaneous reactions were taken into account, exposure–response curves were practically identical for EEG awakenings and cardiac activations. Automatically detected cardiac activations may be used as estimates for EEG awakenings. More investigations are needed to further validate the ECG algorithm in the field and to investigate inter-individual differences in its ability to predict EEG awakenings. This inexpensive, objective and non-invasive method facilitates large-scale field studies on the effects of traffic noise on sleep

  18. 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.

  19. 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.

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

    Science.gov (United States)

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-01-01

    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. PMID:27347978

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

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

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

  2. 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......, an automatic computerized REM detection algorithm has been implemented, using wavelet packet combined with artificial neural network. Results: When using the EEG, EOG and EMG modalities, it was possible to correctly classify REM sleep with an average Area Under Curve (AUC) equal to 0:900:03 for normal subjects...

  3. 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.

  4. 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.

  5. 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%.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Automatic molecular collection and detection by using fuel-powered microengines

    Science.gov (United States)

    Han, Di; Fang, Yangfu; Du, Deyang; Huang, Gaoshan; Qiu, Teng; Mei, Yongfeng

    2016-04-01

    We design and fabricate a simple self-powered system to collect analyte molecules in fluids for surface-enhanced Raman scattering (SERS) detection. The system is based on catalytic Au/SiO/Ti/Ag-layered microengines by employing rolled-up nanotechnology. Pronounced SERS signals are observed on microengines with more carrier molecules compared with the same structure without automatic motions.We design and fabricate a simple self-powered system to collect analyte molecules in fluids for surface-enhanced Raman scattering (SERS) detection. The system is based on catalytic Au/SiO/Ti/Ag-layered microengines by employing rolled-up nanotechnology. Pronounced SERS signals are observed on microengines with more carrier molecules compared with the same structure without automatic motions. Electronic supplementary information (ESI) available: Experimental procedures, characterization, SERS enhancement factor calculation and videos. See DOI: 10.1039/c6nr00117c

  12. 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.

  13. 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.

  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. Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients

    Science.gov (United States)

    Liu, Jiamin; Hua, Jeremy; Chellappa, Vivek; Petrick, Nicholas; Sahiner, Berkman; Farooqui, Mohammed; Marti, Gerald; Wiestner, Adrian; Summers, Ronald M.

    2012-03-01

    Patients with chronic lymphocytic leukemia (CLL) have an increased frequency of axillary lymphadenopathy. Pretreatment CT scans can be used to upstage patients at the time of presentation and post-treatment CT scans can reduce the number of complete responses. In the current clinical workflow, the detection and diagnosis of lymph nodes is usually performed manually by examining all slices of CT images, which can be time consuming and highly dependent on the observer's experience. A system for automatic lymph node detection and measurement is desired. We propose a computer aided detection (CAD) system for axillary lymph nodes on CT scans in CLL patients. The lung is first automatically segmented and the patient's body in lung region is extracted to set the search region for lymph nodes. Multi-scale Hessian based blob detection is then applied to detect potential lymph nodes within the search region. Next, the detected potential candidates are segmented by fast level set method. Finally, features are calculated from the segmented candidates and support vector machine (SVM) classification is utilized for false positive reduction. Two blobness features, Frangi's and Li's, are tested and their free-response receiver operating characteristic (FROC) curves are generated to assess system performance. We applied our detection system to 12 patients with 168 axillary lymph nodes measuring greater than 10 mm. All lymph nodes are manually labeled as ground truth. The system achieved sensitivities of 81% and 85% at 2 false positives per patient for Frangi's and Li's blobness, respectively.

  16. 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

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

    OpenAIRE

    Borja Rodríguez-Cuenca; Maria C. Alonso

    2014-01-01

    Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and L...

  18. Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

    OpenAIRE

    Javier Estornell; Recio, Jorge A.; Txomin Hermosilla; Ruiz, Luis A.

    2011-01-01

    In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral re...

  19. 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.

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

    OpenAIRE

    Bohui Zhu; Yongsheng Ding; Kuangrong Hao

    2013-01-01

    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 ...

  1. Automatic Detection and Classi cation of Objects in Point Clouds using multi-stage Semantics

    OpenAIRE

    Truong, Hung; Hmida, Helmi Ben; Boochs, Frank; Habed, Adlane; Cruz, Christophe; Voisin, Yvon; Nicolle, Christophe

    2013-01-01

    International audience Due to the increasing availability of large unstructured point clouds from lasers scanning and photogrammetry, there is a growing demand for automatic evaluation methods. Given the complexity of the underlying problems, several new methods resort to using semantic knowledge in particular for object detection and classification support. In this paper, we present a novel approach, which makes use of advanced algorithms, and benefits from intelligent knowledge managemen...

  2. Automatic detection and elimination of periodic pulse shaped interferences in partial discharge measurements

    OpenAIRE

    Nagesh, V.; Gururaj, BI

    1994-01-01

    The interferences present in partial discharge (PD) measurement can be classified as narrow-band and broad-band, the latter being pulsed shaped. The pulse shaped interferences can be periodic or random with respect to power frequency, the former being very common and strong. The paper describes an algorithm for automatic detection and elimination of periodic pulse shaped interferences in PD measurements. The algorithm is developed on lines similar to that used in decomposing an electromyogram...

  3. Automatic Music Boundary Detection Using Short Segmental Acoustic Similarity in a Music Piece

    OpenAIRE

    Tanaka Kazuyo; Lee Shi-Wook; Itoh Yoshiaki; Iwabuchi Akira; Kojima Kazunori; Ishigame Masaaki

    2008-01-01

    The present paper proposes a new approach for detecting music boundaries, such as the boundary between music pieces or the boundary between a music piece and a speech section for automatic segmentation of musical video data and retrieval of a designated music piece. The proposed approach is able to capture each music piece using acoustic similarity defined for short-term segments in the music piece. The short segmental acoustic similarity is obtained by means of a new algorithm called segmen...

  4. 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.

  5. Detection of microarray protein biomolecules by oblique-incidence reflectivity difference technique without labelling agents

    Institute of Scientific and Technical Information of China (English)

    Zhang Hong-Yan; Lu Heng; Li Wei; Liang Ru-Qiang; Jin Kui-Juan; Zhou Yue-Liang; Ruan Kang-Cheng; Yang Guo-Zhen

    2008-01-01

    This paper reports that the detection to the protein in microarray format is carried out by oblique-incidence reflectivity difference (OI-RD) analysis without any labelling agents. The OI-RD intensities not only depend on the protein structure, but also vary with the protein concentration. The results indicate that this method should have potential application in detection of biochemical processes. The high throughout and in situ detection can be achieved by this method with further improving of the experimental system.

  6. An Automatic Impact-based Delamination Detection System for Concrete Bridge Decks

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Gang; Harichandran, Ronald S.; Ramuhalli, Pradeep

    2012-01-02

    Delamination of concrete bridge decks is a commonly observed distress in corrosive environments. In traditional acoustic inspection methods, delamination is assessed by the "hollowness" of the sound created by impacting the bridge deck with a hammer or bar or by dragging a chain where the signals are often contaminated by ambient traffic noise and the detection is highly subjective. In the proposed method, a modified version of independent component analysis (ICA) is used to filter the traffic noise. To eliminate subjectivity, Mel-frequency cepstral coefficients (MFCC) are used as features for detection and the delamination is detected by a radial basis function (RBF) neural network. Results from both experimental and field data suggest that the proposed methods id noise robust and has satisfactory performance. The methods can also detect the delamination of repair patches and concrete below the repair patches. The algorithms were incorporated into an automatic impact-bases delamination detection (AIDD) system for field application.

  7. 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.

  8. Automatic detection and quantification of the Agatston coronary artery calcium score on contrast computed tomography angiography.

    Science.gov (United States)

    Ahmed, Wehab; de Graaf, Michiel A; Broersen, Alexander; Kitslaar, Pieter H; Oost, Elco; Dijkstra, Jouke; Bax, Jeroen J; Reiber, Johan H C; Scholte, Arthur J

    2015-01-01

    Potentially, Agatston coronary artery calcium (CAC) score could be calculated on contrast computed tomography coronary angiography (CTA). This will make a separate non-contrast CT scan superfluous. This study aims to assess the performance of a novel fully automatic algorithm to detect and quantify the Agatston CAC score in contrast CTA images. From a clinical registry, 20 patients were randomly selected for each CAC category (i.e. 0, 1-99, 100-399, 400-999, ≥1,000). The Agatston CAC score on non-contrast CT was calculated manually, while the novel algorithm was used to automatically detect and quantify Agatston CAC score in contrast CTA images. The resulting Agatston CAC scores were validated against the non-contrast images. A total of 100 patients (60 ± 11 years, 63 men) were included. The median CAC score on non-contrast CT was 145 (IQR 5-760), whereas the contrast CTA CAC score was 170 (IQR 23-594) (P = 0.004). The automatically computed CAC score showed a high correlation (R = 0.949; P < 0.001) and intra-class correlation (R = 0.863; P < 0.001) with non-contrast CT CAC score. Moreover, agreement within CAC categories was good (κ 0.588). Fully automatic detection of Agatston CAC score on contrast CTA is feasible and showed high correlation with non-contrast CT CAC score. This could imply a radiation dose reduction and time saving by omitting the non-contrast scan. PMID:25159031

  9. 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....

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

    Directory of Open Access Journals (Sweden)

    David Hewson

    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.

  11. 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%.

  12. 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.)

  13. 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.

  14. 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.

  15. 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...... are assessed and optimized using data-based learning to obtainthresholds for hypothesis testing. Data from a 1400 m horizontal ow loop isused to test the method, and successful diagnosis of the incidents drillstringwashout (pipe leakage), lost circulation, gas in ux, and drill bit plugging aredemonstrated....

  16. 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.

  17. Automatic cell detection in bright-field microscopy for microbeam irradiation studies

    International Nuclear Information System (INIS)

    Automatic cell detection in bright-field illumination microscopy is challenging due to cells’ inherent optical properties. Applications including individual cell microbeam irradiation demand minimisation of additional cell stressing factors, so contrast-enhancing fluorescence microscopy should be avoided. Additionally, the use of optically non-homogeneous substrates amplifies the problem. This research focuses on the design of a method for automatic cell detection on polypropylene substrate, suitable for microbeam irradiation. In order to fulfil the relative requirements, the Harris corner detector was employed to detect apparent cellular features. These features-corners were clustered based on a dual-clustering technique according to the density of their distribution across the image. Weighted centroids were extracted from the clusters of corners and constituted the targets for irradiation. The proposed method identified more than 88% of the 1,738 V79 Chinese hamster cells examined. Moreover, a processing time of 2.6 s per image fulfilled the requirements for a near real-time cell detection-irradiation system. (paper)

  18. Automatic cell detection in bright-field microscopy for microbeam irradiation studies

    Science.gov (United States)

    Georgantzoglou, A.; Merchant, M. J.; Jeynes, J. C. G.; Wéra, A.-C.; Kirkby, K. J.; Kirkby, N. F.; Jena, R.

    2015-08-01

    Automatic cell detection in bright-field illumination microscopy is challenging due to cells’ inherent optical properties. Applications including individual cell microbeam irradiation demand minimisation of additional cell stressing factors, so contrast-enhancing fluorescence microscopy should be avoided. Additionally, the use of optically non-homogeneous substrates amplifies the problem. This research focuses on the design of a method for automatic cell detection on polypropylene substrate, suitable for microbeam irradiation. In order to fulfil the relative requirements, the Harris corner detector was employed to detect apparent cellular features. These features-corners were clustered based on a dual-clustering technique according to the density of their distribution across the image. Weighted centroids were extracted from the clusters of corners and constituted the targets for irradiation. The proposed method identified more than 88% of the 1,738 V79 Chinese hamster cells examined. Moreover, a processing time of 2.6 s per image fulfilled the requirements for a near real-time cell detection-irradiation system.

  19. 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.

  20. 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.

  1. 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.

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

    International Nuclear Information System (INIS)

    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 × 106 copies

  3. Optimization of Doppler velocity echocardiographic measurements using an automatic contour detection method.

    Science.gov (United States)

    Gaillard, E; Kadem, L; Pibarot, P; Durand, L-G

    2009-01-01

    Intra- and inter-observer variability in Doppler velocity echocardiographic measurements (DVEM) is a significant issue. Indeed, imprecisions of DVEM can lead to diagnostic errors, particularly in the quantification of the severity of heart valve dysfunction. To minimize the variability and rapidity of DVEM, we have developed an automatic method of Doppler velocity wave contour detection, based on active contour models. To validate our new method, results obtained with this method were compared to those obtained manually by an experienced echocardiographer on Doppler echocardiographic images of left ventricular outflow tract and transvalvular flow velocity signals recorded in 30 patients, 15 with aortic stenosis and 15 with mitral stenosis. We focused on three essential variables that are measured routinely by Doppler echocardiography in the clinical setting: the maximum velocity, the mean velocity and the velocity-time integral. Comparison between the two methods has shown a very good agreement (linear correlation coefficient R(2) = 0.99 between the automatically and the manually extracted variables). Moreover, the computation time was really short, about 5s. This new method applied to DVEM could, therefore, provide a useful tool to eliminate the intra- and inter-observer variabilities associated with DVEM and thereby to improve the diagnosis of cardiovascular disease. This automatic method could also allow the echocardiographer to realize these measurements within a much shorter period of time compared to standard manual tracing method. From a practical point of view, the model developed can be easily implanted in a standard echocardiographic system. PMID:19965162

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

    Science.gov (United States)

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

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. PMID:27478364

  10. Detection of pneumoconiosis opacities on CT images and its application to automatic diagnosis

    International Nuclear Information System (INIS)

    We propose an automatic diagnosis method of pneumoconiosis by extracting rounded opacities from chest CT images. It can meet the requirement better according to the ILO classification system whose categorization is based on the density of pneumoconiosis opacities. Profusion of small rounded and irregular opacities caused by pneumoconiosis is classified on 4 major categories based on the number of opacities per unit lung area. X-ray CT images give sectional images of human body. There are no overlapping between pneumoconiosis opacities, ribs and blood vessels. It is of great advantage from the viewpoint of image processing to realize automatic diagnosis system. This paper presents a fundamental approach of quantitative diagnosis of pneumoconiosis by detecting rounded opacities of pneumoconiosis on CT images. This method is based on mathematical morphology and consists of five processes. They are extraction of lung area, enhancement of rounded opacity candidates and vessels by morphological processing with multi-structuring elements, detection of rounded opacity, detection of vessels, and calculation of statistical values for quantitative diagnosis. Experiments showed that the correct classification rate on 12 categories was 83%. These results show the effectiveness of the proposed method. (author)

  11. 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

  12. 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

    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 PV modules....

  13. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg;

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... band widening of the feature extraction is performed. This means that algorithms for sEEG should not be discarded for use on iEEG - they should be properly adjusted as exemplified in this paper....

  14. 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

  15. EMS response to mass casualty incidents : the critical importance of automatic statewide mutual aid and MCI training

    OpenAIRE

    HILL, CHERYL

    2008-01-01

    CHDS State/Local Incidence of natural and man-made disasters are increasing and expanding in scope. While these events may cause mass injuries, the pre-hospital emergency medical services (EMS) community is left out of the preparedness equation by virtue of being underrepresented on planning committees, not privy to disaster training, nor on the receiving end of preparedness funding. Additionally, for many states, outside standard mutual aid agreements a disaster declaration is require...

  16. 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

  17. 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.

  18. An automatic seismic signal detection method based on fourth-order statistics and applications

    Institute of Scientific and Technical Information of China (English)

    Liu Xi-Qiang; Cai Yin; Zhao Rui; Zhao Yin-Gang; Qu Bao-An; Feng Zhi-Jun; Li Hong

    2014-01-01

    Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function (CTF) for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion (AIC) for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset,fi rst a specifi c segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specifi c segment of P-wave seismograms are analyzed by S-wave polarizationfi ltering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.

  19. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

    Directory of Open Access Journals (Sweden)

    Wenyu Zhang

    2014-10-01

    Full Text Available Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

  20. 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.

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

    Science.gov (United States)

    Oleksiak, Justyna; Cybulski, Gerard

    2014-11-01

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

  2. Automatic detection of human and Energy saving based on Zigbee Communication

    Directory of Open Access Journals (Sweden)

    Chinnam Sujana,

    2011-06-01

    Full Text Available This paper proposes automatic detection of human and Energy saving room architecture to reduce standby power consumption and to make the room easily controllable with an IR remote control of a home appliance. To realize the proposed room architecture, we proposed and designed the Zigbee communication. Zigbee is a low-cost, low-power, wireless mesh networking. The low cost allows the technology to be widely deployed in wireless control and monitoring applications, the low power-usage allows longer life with smaller batteries, and the mesh networking provides high reliability and larger range. The proposed auto detection of human done using the IR sensor to indicate the entering or exit of the persons. Microcontroller continuously monitors the infrared receiver. When any object pass through the IR receiver then the IR rays falling on the receiver are obstructed, this obstruction is sensed by the microcontroller (LPC2148-ARM7 also PIR sensor will check the presence of human beings with the help of radiations emitted by human beings. Then microcontroller will check the input coming from these two sensors and simultaneously if somebody is present then automatically checks for the light intensity and the temperature. And then if the room is found dark it switches ON the lights and if the temperature is more it switches ON the fans. And if nobody is present then all the lights will be switched offautomatically.

  3. An Automatic Statistical Method to detect the Breast Border in a Mammogram

    Directory of Open Access Journals (Sweden)

    Wai Tak (Arthur Hung

    2007-03-01

    Full Text Available Segmentation is an image processing technique to divide an image into several meaningful objects. Edge enhancement and border detection are important components of image segmentation. A mammogram is a soft x-ray of a woman's breast, which is read by radiologists to detect breast cancer. Recently, digital mammography is also available. In order to do computer aided detection on mammogram, the image has to be either in digital form or digitized. A preprocessing step to a digital/digitized mammogram is to detect the breast border so as to minimize the area to search for breast lesion. An enclosed curve is used to define the breast area. In this paper we propose a modified measure of class separability and used it to select the best segmentation result objectively, which leads to an improved border detection method. This new method is then used to analyze a test set of 35 mammograms. The breast border of these 35 mammograms was also traced manually twice to test for their repeatability using Hung's method1. The borders obtained from the proposed automatic border detection method are shown to be of better quality than the corresponding ones traced manually.

  4. An object-based classification method for automatic detection of lunar impact craters from topographic data

    Science.gov (United States)

    Vamshi, Gasiganti T.; Martha, Tapas R.; Vinod Kumar, K.

    2016-05-01

    Identification of impact craters is a primary requirement to study past geological processes such as impact history. They are also used as proxies for measuring relative ages of various planetary or satellite bodies and help to understand the evolution of planetary surfaces. In this paper, we present a new method using object-based image analysis (OBIA) technique to detect impact craters of wide range of sizes from topographic data. Multiresolution image segmentation of digital terrain models (DTMs) available from the NASA's LRO mission was carried out to create objects. Subsequently, objects were classified into impact craters using shape and morphometric criteria resulting in 95% detection accuracy. The methodology developed in a training area in parts of Mare Imbrium in the form of a knowledge-based ruleset when applied in another area, detected impact craters with 90% accuracy. The minimum and maximum sizes (diameters) of impact craters detected in parts of Mare Imbrium by our method are 29 m and 1.5 km, respectively. Diameters of automatically detected impact craters show good correlation (R2 > 0.85) with the diameters of manually detected impact craters.

  5. 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.

  6. Automatic nuclear bud detection using ellipse fitting, moving sticks or top-hat transformation.

    Science.gov (United States)

    Zhang, C; Sun, C; Vallotton, P; Fenech, M; Pham, T D

    2013-11-01

    Micronucleus assays are extensively used by biologists to assess genotoxicity and to monitor human exposure to genotoxic materials. As recent studies suggested that nuclear buds can be a new source of micronuclei formed in interphase, the quantification of nuclear buds, which are micronucleus like objects that are attached to the nuclei in interphase, in normal and control group is needed. Three automatic nuclear bud detection algorithms fit for different situations are proposed in this paper. One is based on ellipse fitting, one is based on a stick model and the other is based on the top-hat transform. Comparison of the three methods is also given in this paper. Experimental results showed that the proposed algorithms are all effective and efficient for nuclear bud detection. PMID:23961938

  7. Automatic Detection of Exudates in Retinal Fundus Images using Differential Morphological Profile

    Directory of Open Access Journals (Sweden)

    Shraddha Tripathi

    2013-06-01

    Full Text Available This paper presents an automatic method for exudate detection from colour fundus imagesbased on Differential Morphological Profile (DMP.The detection of exudates is important for the identification of eye diseases such as diabetic retinopathy. The method involves of three main phases. Inthe first phase, pre processing tasks like Gaussian smoothing and contrast enhancement is done. In the second phase, DMP is applied on the pre-processed image. The image obtained from DMP containshighlighted bright regions consisting of exudates and optic disc. In the next phase, feature extraction based on location of optic disc, shape index and area is done to obtain actual exudates. The performance of the proposed method is evaluated by applying it on the DIARETDB1 database. The specificity,sensitivity and PPV of the proposed method were compared with two other methods. The results showthat the proposed method gives better results than the other conventional methods.

  8. 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.

  9. 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%.

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

    OpenAIRE

    Andrius Gudiškis

    2015-01-01

    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 dis...

  11. 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.

  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. Age-related incidence of pineal calcification detected by computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Zimmerman, R.A.; Bilaniuk, L.T.

    1982-03-01

    The age-related incidence of detectable pineal calcification in 725 patients (age range, newborn-20 yrs) suggests that there is a relationship between calcification and the hormonal role played by the pineal gland in the regulation of sexual development. Pineal calcification (demonstrated by computed tomography (CT) on 8-mm-thick sections) in patients less than 6 years old should be looked upon with suspicion, and follow-up CT should be considered to exclude the possible development of a pineal neoplasm.

  14. Whistlers detected and analyzed by Automatic Whistler Detector (AWD) at low latitude Indian stations

    Science.gov (United States)

    Singh, Abhay K.; Singh, S. B.; Singh, Rajesh; Gokani, Sneha A.; Singh, Ashok K.; Siingh, Devendraa; Lichtenberger, János

    2014-12-01

    Recently, at three Indian low latitude stations: Varanasi (geomag. lat. 14°55‧N, geomag. long. 153°54‧E, L: 1.078), Allahabad (geomag. lat. 16.05°N; geomag. long. 155.34°E, L: 1.081) and Lucknow (geomag. lat. 17.6°N, geomag. long. 154.5°E, L: 1.104) an Automatic Whistler Detector (AWD) has been installed in December, 2010 for detection and analysis of whistlers. This instrument automatically detects and collects statistical whistlers data for the investigation of whistlers generation and propagation. Large numbers of whistlers have been recorded at Varanasi and Allahabad during the year 2011 which is analyzed in the present study. Different types of whistlers have been recorded at Varanasi and Allahabad. The correlation between recorded whistlers and causative lightning strikes were analyzed using data provided by World-Wide Lightning Location Network (WWLLN). We observed that for both the stations more than 50% of causative sferics of whistlers were observed to match closely with the times of WWLLN detected lightning strikes within the propagation times of causative tweeks. All of these lightning strikes originated from the region within 500-600 km radius circle from the conjugate point of Varanasi and Allahabad supports the ducted propagation at low latitude stations. The dispersion of the observed whistlers varies between 8 and 18 s1/2, which shows that the observed whistlers have propagated in ducted mode and whole propagation path of whistlers lies in the ionosphere. The ionospheric columnar electron contents of these observed whistlers vary between 13.21 TECU and 56.57 TECU. The ionospheric parameters derived from whistler data at Varanasi compare well with the other measurements made by other techniques.

  15. 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.

  16. Automatic Detection and Characterization of Subsurface Features from Mars Radar Sounder Data

    Science.gov (United States)

    Ferro, A.; Bruzzone, L.; Heggy, E.; Plaut, J. J.

    2010-12-01

    MARSIS and SHARAD are currently orbiting Mars in an attempt to explore structural and volatile elements in its subsurface. The data returned from these two experiments are complementary in their nature for providing different penetration capabilities and vertical resolutions that is crucial to constrain the ambiguities on the subsurface structural and geophysical properties. To this day, both radars have acquired a substantial large volume of data that are yet to be quantitatively analyzed with more accurate radar inversion algorithms. Manual investigation of the radargrams is a time consuming task that is often dependent on user visual ability to distinguish subsurface reflectors. Such process induces a substantial ambiguity in data analysis from user to user, limits the amount of data to be explored and reduces efficiency of fusion studies to compile MARSIS and SHARAD data in a metric process. To address this deficiency, we started the development of automated techniques for the extraction of subsurface information from the radar sounding data. Such methods will greatly improve the ability to perform scientific analysis on larger scale areas using the two data sets from MARSIS and SHARAD simultaneously [Ferro and Bruzzone, 2009]. Our automated data analysis chain has been preliminarily applied only to SHARAD data for the statistical characterization of the radargrams and the automatic detection of linear subsurface features [Ferro and Bruzzone, 2010]. Our current development has been extended for the integration of both SHARAD and MARSIS data. We identified two targets of interest to test and validate our automated tools to explore subsurface features: (1) The North Polar Layer Deposits, and (2) Elysium Planitia. On the NPLD, the technique was able to extract the position and the extension of the returns coming from basal unit from SHARAD radargrams, both in range and azimuth. Therefore, it was possible to map the depth and thickness of the icy polar cap. The

  17. Automatic Detection of ECG R-R Interval using Discrete Wavelet Transformation

    Directory of Open Access Journals (Sweden)

    Vanisree K,

    2011-04-01

    Full Text Available Detection of QRS-complexes takes an important role in the analysis of ECG signal, based on which the number of heart beats and an irregularity of a heart beat through R-R interval can be determined. Since an ECG may be of different lengths and as being a non-stationary signal, the irregularity may not be periodic instead it can be shown up at any interval of the signal, it is difficult forphysician to analyze ECG manually. In the present study an algorithm has been developed to preprocess and to automatically determine the R-R interval of ECG signal based on Discrete Wavelet Transformation (DWT. The developed algorithm initially performs preprocessing of a signal in order to remove Baseline Drift (De-trending and noise (De-noising from the signal and then it uses the preprocessed signal for finding R-R interval of the ECG signal automatically. By using developed algorithm, the accuracy of the analysis can be increased and the analysis time can be reduced.

  18. 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.

  19. 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.

  20. 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.

  1. Environmental monitoring based on automatic change detection from remotely sensed data: kernel-based approach

    Science.gov (United States)

    Shah-Hosseini, Reza; Homayouni, Saeid; Safari, Abdolreza

    2015-01-01

    In the event of a natural disaster, such as a flood or earthquake, using fast and efficient methods for estimating the extent of the damage is critical. Automatic change mapping and estimating are important in order to monitor environmental changes, e.g., deforestation. Traditional change detection (CD) approaches are time consuming, user dependent, and strongly influenced by noise and/or complex spectral classes in a region. Change maps obtained by these methods usually suffer from isolated changed pixels and have low accuracy. To deal with this, an automatic CD framework-which is based on the integration of change vector analysis (CVA) technique, kernel-based C-means clustering (KCMC), and kernel-based minimum distance (KBMD) classifier-is proposed. In parallel with the proposed algorithm, a support vector machine (SVM) CD method is presented and analyzed. In the first step, a differential image is generated via two approaches in high dimensional Hilbert space. Next, by using CVA and automatically determining a threshold, the pseudo-training samples of the change and no-change classes are extracted. These training samples are used for determining the initial value of KCMC parameters and training the SVM-based CD method. Then optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the KCMC parameters and to select the precise training samples. These training samples are used to train the KBMD classifier. Last, the class label of each unknown pixel is determined using the KBMD classifier and SVM-based CD method. In order to evaluate the efficiency of the proposed algorithm for various remote sensing images and applications, two different datasets acquired by Quickbird and Landsat TM/ETM+ are used. The results show a good flexibility and effectiveness of this automatic CD method for environmental change monitoring. In addition, the comparative analysis of results from the proposed method

  2. 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.

  3. 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.

  4. 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...

  5. Automatic detection method for mura defects on display film surface using modified Weber's law

    Science.gov (United States)

    Kim, Myung-Muk; Lee, Seung-Ho

    2014-07-01

    We propose a method that automatically detects mura defects on display film surfaces using a modified version of Weber's law. The proposed method detects mura defects regardless of their properties and shapes by identifying regions perceived by human vision as mura using the brightness of pixel and image distribution ratio of mura in an image histogram. The proposed detection method comprises five stages. In the first stage, the display film surface image is acquired and a gray-level shift performed. In the second and third stages, the image histogram is acquired and analyzed, respectively. In the fourth stage, the mura range is acquired. This is followed by postprocessing in the fifth stage. Evaluations of the proposed method conducted using 200 display film mura image samples indicate a maximum detection rate of ˜95.5%. Further, the results of application of the Semu index for luminance mura in flat panel display (FPD) image quality inspection indicate that the proposed method is more reliable than a popular conventional method.

  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 Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography.

    Science.gov (United States)

    Chen, Naichao; Chen, Yingchao; Ai, Jun; Ren, Jianxin; Zhu, Rui; Ma, Xingchi; Han, Jun; Ma, Qingqian

    2016-02-01

    To eliminate the effect of subjective factors during manually determining the pearlite spheroidization grade of steel by analysis of optical metallography images, a novel method combining image mining and artificial neural networks (ANN) is proposed. The four co-occurrence matrices of angular second moment, contrast, correlation, and entropy are adopted to objectively characterize the images. ANN is employed to establish a mathematical model between the four co-occurrence matrices and the corresponding spheroidization grade. Three materials used in coal-fired power plants (ASTM A315-B steel, ASTM A335-P12 steel, and ASTM A355-P11 steel) were selected as the samples to test the validity of our proposed method. The results indicate that the accuracies of the calculated spheroidization grades reach 99.05, 95.46, and 93.63%, respectively. Hence, our newly proposed method is adequate for automatically detecting the pearlite spheroidization grade of steel using optical metallography. PMID:26754768

  8. 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.

  9. Automatic detection and classification of artifacts in single-channel EEG

    DEFF Research Database (Denmark)

    Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W.;

    2014-01-01

    Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single......-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different...... artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated...

  10. 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.

  11. 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.

  12. 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

  13. 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...

  14. 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

  15. AUTOMATIC APPLICATION LEVEL SET APPROACH IN DETECTION CALCIFICATIONS IN MAMMOGRAPHIC IMAGE

    Directory of Open Access Journals (Sweden)

    Mohamed Abid

    2011-09-01

    Full Text Available Breast cancer is considered as one of a major health problem that constitutes the strongest cause behindmortality among women in the world. So, in this decade, breast cancer is the second most common type ofcancer, in term of appearance frequency, and the fifth most common cause of cancer related death. Inorder 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 onCADe 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 detectionof Region Of Interest (ROI based on Level Set approach depended on edge and region criteria. Thisapproach gives good visual information from the radiologist. After that, the features extraction usingtextures characteristics and the vector classification using Multilayer Perception (MLP and k-NearestNeighbours (KNN are adopted to distinguish different ACR (American College of Radiologyclassification. Moreover, we use the Digital Database for Screening Mammography (DDSM forexperiments and these results in term of accuracy varied between 60 % and 70% are acceptable and mustbe ameliorated to aid radiologist.

  16. 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.

  17. 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.

  18. 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.

  19. 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. PMID:10477365

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2014-03-01

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

  2. Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Javier Estornell

    2011-06-01

    Full Text Available In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

  3. 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)

  4. Automatic round-the-clock detection of whales for mitigation from underwater noise impacts.

    Directory of Open Access Journals (Sweden)

    Daniel P Zitterbart

    Full Text Available Loud hydroacoustic sources, such as naval mid-frequency sonars or airguns for marine geophysical prospecting, have been increasingly criticized for their possible negative effects on marine mammals and were implicated in several whale stranding events. Competent authorities now regularly request the implementation of mitigation measures, including the shut-down of acoustic sources when marine mammals are sighted within a predefined exclusion zone. Commonly, ship-based marine mammal observers (MMOs are employed to visually monitor this zone. This approach is personnel-intensive and not applicable during night time, even though most hydroacoustic activities run day and night. This study describes and evaluates an automatic, ship-based, thermographic whale detection system that continuously scans the ship's environs for whale blows. Its performance is independent of daylight and exhibits an almost uniform, omnidirectional detection probability within a radius of 5 km. It outperforms alerted observers in terms of number of detected blows and ship-whale encounters. Our results demonstrate that thermal imaging can be used for reliable and continuous marine mammal protection.

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

    Science.gov (United States)

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

    2013-11-15

    External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. PMID:24148491

  6. Detection of the specific binding on protein microarrays by oblique-incidence reflectivity difference method

    Science.gov (United States)

    Lu, Heng; Wen, Juan; Wang, Xu; Yuan, Kun; Li, Wei; Lu, Huibin; Zhou, Yueliang; Jin, Kuijuan; Ruan, Kangcheng; Yang, Guozhen

    2010-09-01

    The specific binding between Cy5-labeled goat anti-mouse Immunoglobulin G (IgG) and mouse IgG with a concentration range from 625 to 104 µg ml - 1 has been detected successfully by the oblique-incidence reflectivity difference (OI-RD) method in each procedure of microarray fabrication. The experimental data prove that the OI-RD method can be employed not only to distinguish the different concentrations in label-free fashion but also to detect the antibody-antigen capture. In addition, the differential treatment of the OI-RD signals can decrease the negative influences of glass slide as the microarray upholder. Therefore the OI-RD technique has promising applications for the label-free and high-throughput detection of protein microarrays.

  7. Detection of the specific binding on protein microarrays by oblique-incidence reflectivity difference method

    International Nuclear Information System (INIS)

    The specific binding between Cy5-labeled goat anti-mouse Immunoglobulin G (IgG) and mouse IgG with a concentration range from 625 to 104 µg ml−1 has been detected successfully by the oblique-incidence reflectivity difference (OI-RD) method in each procedure of microarray fabrication. The experimental data prove that the OI-RD method can be employed not only to distinguish the different concentrations in label-free fashion but also to detect the antibody–antigen capture. In addition, the differential treatment of the OI-RD signals can decrease the negative influences of glass slide as the microarray upholder. Therefore the OI-RD technique has promising applications for the label-free and high-throughput detection of protein microarrays

  8. A Fast Algorithm for Automatic Detection of Ionospheric Disturbances Using GPS Slant Total Electron Content Data

    Science.gov (United States)

    Efendi, Emre; Arikan, Feza; Yarici, Aysenur

    2016-07-01

    Solar, geomagnetic, gravitational and seismic activities cause disturbances in the ionospheric region of upper atmosphere for space based communication, navigation and positioning systems. These disturbances can be categorized with respect to their amplitude, duration and frequency. Typically in the literature, ionospheric disturbances are investigated with gradient based methods on Total Electron Content (TEC) data estimated from ground based dual frequency Global Positioning System (GPS) receivers. In this study, a detection algorithm is developed to determine the variability in Slant TEC (STEC) data. The developed method, namely Differential Rate of TEC (DRoT), is based on Rate of Tec (RoT) method that is widely used in the literature. RoT is usually applied to Vertical TEC (VTEC) and it can be defined as normalized derivative of VTEC. Unfortunately, the resultant data obtained from the application of RoT on VTEC suffer from inaccuracies due to mapping function and the resultant values are very noisy which make it difficult to automatically detect the disturbance due to variability in the ionosphere. The developed DRoT method can be defined as the normalized metric norm (L2) between the RoT and its baseband trend structure. In this study, the error performance of DRoT is determined using synthetic data with variable bounds on the parameter set of amplitude, frequency and period of disturbance. It is observed that DRoT method can detect disturbances in three categories. For DRoT values less than 50%, there is no significant disturbance in STEC data. For DRoT values between 50 to 70 %, a medium scale disturbance can be observed. For DROT values over 70 %, severe disturbances such Large Scale Travelling Ionospheric Disturbances (TID) or plasma bubbles can be observed. When DRoT is applied to the GPS-STECdata for stations in high latitude, equatorial and mid-latitude regions, it is observed that disturbances with amplitudes larger than 10% of the difference between

  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. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  11. 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. PMID:27170891

  12. 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.

  13. 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.

  14. Semi-automatic detection and correction of body organ motion, particularly cardiac motion in SPECT studies

    International Nuclear Information System (INIS)

    patient and artificially imposed). The method is fast (<20s) and robust as compared with manual or other semi-automatic detection of body organ motions in nuclear medicine studies. Conclusion: A fast and robust semi-automatic patient motion detection and correction for SPECT studies has been developed

  15. 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.

  16. Using locality-constrained linear coding in automatic target detection of HRS images

    Science.gov (United States)

    Rezaee, M.; Mirikharaji, Z.; Zhang, Y.

    2016-04-01

    Automatic target detection with complicated shapes in high spatial resolution images is an ongoing challenge in remote sensing image processing. This is because most methods use spectral or texture information, which are not sufficient for detecting complex shapes. In this paper, a new detection framework, based on Spatial Pyramid Matching (SPM) and Locality- constraint Linear Coding (LLC), is proposed to solve this problem, and exemplified using airplane shapes. The process starts with partitioning the image into sub-regions and generating a unique histogram for local features of each sub-region. Then, linear Support Vector Machines (SVMs) are used to detect objects based on a pyramid-matching kernel, which analyses the descriptors inside patches in different resolution. In order to generate the histogram, first a point feature detector (e.g. SIFT) is applied on the patches, and then a quantization process is used to select local features. In this step, the k-mean method is used in conjunction with the locality-constrained linear coding method. The LLC forces the coefficient matrix in the quantization process to be local and sparse as well. As a result, the speed of the method improves around 24 times in comparison to using sparse coding for quantization. Quantitative analysis also shows improvement in comparison to just using k-mean, but the accuracy in comparison to using sparse coding is similar. Rotation and shift of the desired object has no effect on the obtained results. The speed and accuracy of this algorithm for high spatial resolution images make it capable for use in real-world applications.

  17. 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.

  18. Automatic detection and tracking of filaments for a solar feature database

    Directory of Open Access Journals (Sweden)

    J. Aboudarham

    2008-02-01

    Full Text Available A new method for the automatic detection and tracking of solar filaments is presented. The method addresses the problems facing existing catalogs, such as the one developed recently in the frame of the European Grid of Solar Observations (EGSO project. In particular, it takes into account the structural and temporal evolution of filaments, differences in intensity as seen from one observation to the next, and the possibility of sudden disappearance followed by reappearance. In this study, the problem of tracking is solved by plotting all detected filaments during each solar rotation on a Carrington map and then by applying region growing techniques on those plots. Using this approach, the "fixed" positions of the envelopes in the Carrington system can be deduced. This is followed by a backward tracking of each filament by considering one full solar rotation. The resulting shifted Carrington map then enables one to follow any filament from one rotation to the next. Such maps should prove valuable for studies of the role of filaments in solar activity, notably coronal mass ejections (CMEs.

  19. 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.

  20. 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

  1. 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...

  2. 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...

  3. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    OpenAIRE

    Borja Rodríguez-Cuenca; Silverio García-Cortés; Celestino Ordóñez; Maria C. Alonso

    2015-01-01

    Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical o...

  4. Detection of hybridization of protein microarrays using an oblique-incidence reflectivity difference method

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Mouse-Immunoglobulin G(mouse-IgG) with different concentrations in a range from 1000 to 0.0128 μg/mL and a specific hybridization with goat anti-mouse IgG were detected successfully by using an oblique-incidence reflectivity difference(OI-RD) method.Two detection signals,consisting of an imaginary part(Im{Δp-Δs}) and a real part(Re{Δp-Δs}) of OI-RD,were obtained simultaneously.The detection results of hybridization by OI-RD were in accord with that of traditional fluorescent scans.In particular,we label-freely detected the washed mouse-IgG microarray with a series of concentrations and acquired a linear correlation between OI-RD intensities and the protein concentrations in logarithmic coordinates.The detection sensitivity of OI-RD can reach 14 fg.These experimental results suggest that the OI-RD method has potential applications in proteomics and clinical diagnosis.

  5. Detection of hybridization of protein microarrays using an oblique-incidence reflectivity difference method

    Science.gov (United States)

    Lu, Heng; Wen, Juan; Wang, Xu; Yuan, Kun; Lu, Huibin; Zhou, Yueliang; Jin, Kui-Juan; Yang, Guozhen; Li, Wei; Ruan, Kangcheng

    2010-07-01

    Mouse-Immunoglobulin G (mouse-IgG) with different concentrations in a range from 1000 to 0.0128 μg/mL and a specific hybridization with goat anti-mouse IgG were detected successfully by using an oblique-incidence reflectivity difference (OI-RD) method. Two detection signals, consisting of an imaginary part (Im{Δp-Δs}) and a real part (Re{Δp-Δs}) of OI-RD, were obtained simultaneously. The detection results of hybridization by OI-RD were in accord with that of traditional fluorescent scans. In particular, we label-freely detected the washed mouse-IgG microarray with a series of concentrations and acquired a linear correlation between OI-RD intensities and the protein concentrations in logarithmic coordinates. The detection sensitivity of OI-RD can reach 14 fg. These experimental results suggest that the OI-RD method has potential applications in proteomics and clinical diagnosis.

  6. 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.

  7. Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection.

    Science.gov (United States)

    Huang, Lianfen; Weng, Minghui; Shuai, Haitao; Huang, Yue; Sun, Jianjun; Gao, Fenglian

    2016-01-01

    Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm's robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail. PMID:27631012

  8. Automatic, Real-Time Algorithms for Anomaly Detection in High Resolution Satellite Imagery

    Science.gov (United States)

    Srivastava, A. N.; Nemani, R. R.; Votava, P.

    2008-12-01

    Earth observing satellites are generating data at an unprecedented rate, surpassing almost all other data intensive applications. However, most of the data that arrives from the satellites is not analyzed directly. Rather, multiple scientific teams analyze only a small fraction of the total data available in the data stream. Although there are many reasons for this situation one paramount concern is developing algorithms and methods that can analyze the vast, high dimensional, streaming satellite images. This paper describes a new set of methods that are among the fastest available algorithms for real-time anomaly detection. These algorithms were built to maximize accuracy and speed for a variety of applications in fields outside of the earth sciences. However, our studies indicate that with appropriate modifications, these algorithms can be extremely valuable for identifying anomalies rapidly using only modest computational power. We review two algorithms which are used as benchmarks in the field: Orca, One-Class Support Vector Machines and discuss the anomalies that are discovered in MODIS data taken over the Central California region. We are especially interested in automatic identification of disturbances within the ecosystems (e,g, wildfires, droughts, floods, insect/pest damage, wind damage, logging). We show the scalability of the algorithms and demonstrate that with appropriately adapted technology, the dream of real-time analysis can be made a reality.

  9. 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.

  10. 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.

  11. 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.

  12. 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. PMID:26640970

  13. 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.

  14. 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.

  15. A new automatic Planetary Boundary Layers height detection and diurnal evolution with compact EZ Lidar

    Science.gov (United States)

    Loaec, S.; Boquet, M.,; Sauvage, L.; Lolli, S.; Rouget, V.

    2009-04-01

    Bigger strongly urbanized cities in the world are often exposed to atmospheric pollution events. To understand the chemical and physical processes that are taking place in these areas it is necessary to describe correctly the Planetary Boundary Layer (PBL) dynamics and the PBL height evolution. For these proposals, a compact and rugged eye safe UV Lidar, the EZLIDAR™, was developed together by CEA/LMD and LEOSPHERE (France) to study and investigate structural and optical properties of clouds and aerosols and PBL time evolution. EZLIDAR™ has been validated by different remote and in-situ instruments as MPL Type-4 Lidar manufactured by NASA at ARM/SGP site or the LNA (Lidar Nuage Aerosol) at the Laboratoire de Metereologie Dynamique LMD (France) and during several intercomparison campaigns. EZLIDAR™ algorithm retrieves automatically the PBL height in real-time. The method is based on the detection of the slope of the signal linked to a sharp change in concentration of the aerosols. Once detected, the different layers are filtered on a 15mn sample and classified between nocturnal, convective or residual layer, depending on the time and date. This method has been validated against those retrieved by the algorithm STRAT from data acquired at IPSL, France, showing 95% of correlation. In this paper are presented the results of the intercomparison campaign that took place in Orleans, France in the framework of ICOS (Integrated Carbon Observation System) project, where the EZ Lidar™ worked under all weather conditions, clear sky, fog, low clouds, during the whole month of October 2008. Moreover, thanks to its 3D scanning capability, the EZLIDAR was able to provide the variability of the PBL height around the site, enabling the scientists to estimate the flux intensities that play a key role in the radiative transfer budget and in the atmospheric pollutants dispersion.

  16. 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.

  17. Incidence and detection of beak and feather disease virus in psittacine birds in the UAE.

    Science.gov (United States)

    Hakimuddin, F; Abidi, F; Jafer, O; Li, C; Wernery, U; Hebel, Ch; Khazanehdari, K

    2016-01-01

    Beak and feather disease is caused by Circovirus, which affects actively growing beak and feather cells of avian species. The disease affects mainly young birds while older birds may overcome the disease with few lasting effects. Due to lack of treatment, the only way to control the disease is through hygiene and early diagnosis. As a diagnostic tool, we have established a Taqman probe based real-time PCR assay to detect the presence of the viral genome in psittacine birds in UAE and reported the incidence of circovirus in different species of psittacine birds. The sensitivity of our assay was found to be very high with detection limit of up to 3.5 fg of DNA in the sample. The mean prevalence of circovirus was found to be 58.33% in African Grey Parrots, 34.42% in Cockatoos, 31.8% in amazon parrots and 25.53% in Macaws. The Taqman assay is a quick, reliable and sensitive detection method that has been instrumental in identifying this disease that was not previously reported in the region. PMID:27077045

  18. 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

  19. 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

  20. Explodet Project:. Methods of Automatic Data Processing and Analysis for the Detection of Hidden Explosive

    Science.gov (United States)

    Lecca, Paola

    2003-12-01

    The research of the INFN Gruppo Collegato di Trento in the ambit of EXPLODET project for the humanitarian demining, is devoted to the development of a software procedure for the automatization of data analysis and decision taking about the presence of hidden explosive. Innovative algorithms of likely background calculation, a system based on neural networks for energy calibration and simple statistical methods for the qualitative consistency check of the signals are the main parts of the software performing the automatic data elaboration.

  1. Comparison of edge detection techniques for the automatic information extraction of Lidar data

    Science.gov (United States)

    Li, H.; di, L.; Huang, X.; Li, D.

    2008-05-01

    In recent years, there has been much interest in information extraction from Lidar point cloud data. Many automatic edge detection algorithms have been applied to extracting information from Lidar data. Generally they can be divided as three major categories: early vision gradient operators, optimal detectors and operators using parametric fitting models. Lidar point cloud includes the intensity information and the geographic information. Thus, traditional edge detectors used in remote sensed images can take advantage with the coordination information provided by point data. However, derivation of complex terrain features from Lidar data points depends on the intensity properties and topographic relief of each scene. Take road for example, in some urban area, road has the alike intensity as buildings, but the topographic relationship of road is distinct. The edge detector for road in urban area is different from the detector for buildings. Therefore, in Lidar extraction, each kind of scene has its own suitable edge detector. This paper compares application of the different edge detectors from the previous paragraph to various terrain areas, in order to figure out the proper algorithm for respective terrain type. The Canny, EDISON and SUSAN algorithms were applied to data points with the intensity character and topographic relationship of Lidar data. The Lidar data for test are over different terrain areas, such as an urban area with a mass of buildings, a rural area with vegetation, an area with slope, or an area with a bridge, etc. Results using these edge detectors are compared to determine which algorithm is suitable for a specific terrain area. Key words: Edge detector, Extraction, Lidar, Point data

  2. 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.

  3. A wavelet based method for automatic detection of slow eye movements: a pilot study.

    Science.gov (United States)

    Magosso, Elisa; Provini, Federica; Montagna, Pasquale; Ursino, Mauro

    2006-11-01

    Electro-oculographic (EOG) activity during the wake-sleep transition is characterized by the appearance of slow eye movements (SEM). The present work describes an algorithm for the automatic localisation of SEM events from EOG recordings. The algorithm is based on a wavelet multiresolution analysis of the difference between right and left EOG tracings, and includes three main steps: (i) wavelet decomposition down to 10 detail levels (i.e., 10 scales), using Daubechies order 4 wavelet; (ii) computation of energy in 0.5s time steps at any level of decomposition; (iii) construction of a non-linear discriminant function expressing the relative energy of high-scale details to both high- and low-scale details. The main assumption is that the value of the discriminant function increases above a given threshold during SEM episodes due to energy redistribution toward higher scales. Ten EOG recordings from ten male patients with obstructive sleep apnea syndrome were used. All tracings included a period from pre-sleep wakefulness to stage 2 sleep. Two experts inspected the tracings separately to score SEMs. A reference set of SEM (gold standard) were obtained by joint examination by both experts. Parameters of the discriminant function were assigned on three tracings (design set) to minimize the disagreement between the system classification and classification by the two experts; the algorithm was then tested on the remaining seven tracings (test set). Results show that the agreement between the algorithm and the gold standard was 80.44+/-4.09%, the sensitivity of the algorithm was 67.2+/-7.37% and the selectivity 83.93+/-8.65%. However, most errors were not caused by an inability of the system to detect intervals with SEM activity against NON-SEM intervals, but were due to a different localisation of the beginning and end of some SEM episodes. The proposed method may be a valuable tool for computerized EOG analysis. PMID:16497535

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

    Ambulatory long-term motility recording is used increasingly for evaluation of esophageal function. The enormous amount of motility data recorded by this method demands subsequent computer analysis. One of the most crucial steps of this analysis becomes the process of automatic selection of relev......Ambulatory long-term motility recording is used increasingly for evaluation of esophageal function. The enormous amount of motility data recorded by this method demands subsequent computer analysis. One of the most crucial steps of this analysis becomes the process of automatic selection...

  9. 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

  10. Making sense of sensor data : detecting clinical mastitis in automatic milking systems

    NARCIS (Netherlands)

    Kamphuis, C.

    2010-01-01

    Farmers milking dairy cows are obliged to exclude milk with abnormal homogeneity or color for human consumption (e.g., Regulation (EC) No 853/2004), where most abnormal milk is caused by clinical mastitis (CM). With automatic milking (AM), farmers are no longer physically present during the milking

  11. Ratios in Higher Order Statistics (RHOS) values of Seismograms for Improved Automatic P-Phase Arrival Detection

    CERN Document Server

    Dugda, Mulugeta

    2010-01-01

    In this paper we present two new procedures for automatic detection and picking of P-wave arrivals. The first involves the application of kurtosis and skewness on the vector magnitude of three component seismograms. Customarily, P-wave arrival detection techniques use vertical component seismogram which is appropriate only for teleseismic events. The inherent weakness of those methods stems from the fact that the energy from P-wave is distributed among horizontal and vertical recording channels. Our procedure, however, uses the vector magnitude which accommodates all components. The results show that this procedure would be useful for detecting/picking of P-arrivals from local and regional earthquakes and man-made explosions. The second procedure introduces a new method called "Ratios in Higher Order Statistics (RHOS)." Unlike commonly used techniques that involve derivatives, this technique employs ratios of adjacent kurtosis and skewness values to improve the accuracy of the detection of the P onset. RHOS c...

  12. 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...

  13. Label-Free and High-Throughput Detection of Protein Microarrays by Oblique-Incidence Reflectivity Difference Method

    Science.gov (United States)

    Wang, Xu; Lu, Heng; Wen, Juan; Yuan, Kun; LÜ, Hui-Bin; Jin, Kui-Juan; Zhou, Yue-Liang; Yang, Guo-Zhen

    2010-10-01

    We label-free detected the biological process of preparing a microarray that includes 400 spots of mouse immunoglobulin G (IgG) as well as the specific hybridization between mouse IgG and goat anti-mouse IgG by an oblique-incidence reflectivity difference (OI-RD) method. The detection results after each process including printing, washing, blocking, and hybridization, demonstrate that the OI-RD method can trace the preparation process of a microarray and detect the specific hybridization between antigens and antibodies. OI-RD is a promising method for label-free and high-throughput detection of biological microarrays.

  14. Label-Free and High-Throughput Detection of Protein Microarrays by Oblique-Incidence Reflectivity Difference Method

    International Nuclear Information System (INIS)

    We label-free detected the biological process of preparing a microarray that includes 400 spots of mouse immunoglobulin G (IgG) as well as the specific hybridization between mouse IgG and goat anti-mouse IgG by an oblique-incidence reflectivity difference (OI-RD) method. The detection results after each process including printing, washing, blocking, and hybridization, demonstrate that the OI-RD method can trace the preparation process of a microarray and detect the specific hybridization between antigens and antibodies. OI-RD is a promising method for label-free and high-throughput detection of biological microarrays

  15. 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.

  16. 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.

  17. Automatic detection of sleep macrostructure based on a sensorized T-shirt.

    Science.gov (United States)

    Bianchi, Anna M; Mendez, Martin O

    2010-01-01

    In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained classification was compared to the standard clinical practice based on polysomnography (PSG). In the present work we combined T-shirt recordings and automatic classification and could obtain reliable sleep profiles, i.e. the sleep classification in WAKE, REM (rapid eye movement) and NREM stages, based on heart rate variability (HRV), respiration and movement signals. PMID:21096842

  18. 3D facial expression recognition using SIFT descriptors of automatically detected keypoints

    OpenAIRE

    Berretti, Stefano; Ben Amor, Boulbaba; Daoudi, Mohamed; Del Bimbo, Alberto

    2011-01-01

    International audience; Methods to recognize humans' facial expressions have been proposed mainly focusing on 2D still images and videos. In this paper, the problem of person-independent facial expression recognition is addressed using the 3D geometry information extracted from the 3D shape of the face. To this end, a completely automatic approach is proposed that relies on identifying a set of facial keypoints, computing SIFT feature descriptors of depth images of the face around sample poin...

  19. 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.

  20. Influence of Incidence Angle on the Use of C-Band SAR Data for the Detection Flooded Forests

    Science.gov (United States)

    Lang, M. W.; Townsend, P.; Kasischke, E.

    2006-12-01

    Hydrology is the single most important factor in the formation and functioning of a wetland. Many limitations still exist to accurately characterize wetland hydrology over large spatial extents, especially in forested wetlands. Imaging radar has emerged as a viable tool for forested wetland flood mapping, although the limitations of radar data have not been fully explored. The influence of incidence angle on the ability to detect flooding in different forest types was examined along the Roanoke River in North Carolina using Radarsat (C- HH) data collected during the leaf-off and leaf-on seasons. Backscatter generally decreased with increasing incidence angle under all conditions, but the distinction between flooded and non-flooded areas did not decline sharply with incidence angle as predicted. The ability to detect flooding under leaf-on conditions varied much more according to incidence angle while forest type had a greater effect during the leaf-off season. Differentiation of flooded and non-flooded forests was similar during the leaf-off and leaf-on seasons. Use of a wider range of incidence angles during the entire year increases the temporal resolution of imagery which may, in turn, enhance mapping of inundation beneath forest canopies.

  1. 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.

  2. 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.

  3. Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography.

    Science.gov (United States)

    Krissian, Karl; Carreira, Jose M; Esclarin, Julio; Maynar, Manuel

    2014-01-01

    Aorta dissection is a serious vascular disease produced by a rupture of the tunica intima of the vessel wall that can be lethal to the patient. The related diagnosis is strongly based on images, where the multi-detector CT is the most generally used modality. We aim at developing a semi-automatic segmentation tool for aorta dissections, which will isolate the dissection (or flap) from the rest of the vascular structure. The proposed method is based on different stages, the first one being the semi-automatic extraction of the aorta centerline and its main branches, allowing an subsequent automatic segmentation of the outer wall of the aorta, based on a geodesic level set framework. This segmentation is then followed by an extraction the center of the dissected wall as a 3D mesh using an original algorithm based on the zero crossing of two vector fields. Our method has been applied to five datasets from three patients with chronic aortic dissection. The comparison with manually segmented dissections shows an average absolute distance value of about half a voxel. We believe that the proposed method, which tries to solve a problem that has attracted little attention to the medical image processing community, provides a new and interesting tool to isolate the intimal flap that can provide very useful information to the clinician. PMID:24161795

  4. 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.

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Navarro, Pedro J; Fernández-Isla, Carlos; Alcover, Pedro María; Suardíaz, Juan

    2016-01-01

    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. PMID:27472343

  8. Novel automatic detection of pleura and B-lines (comet-tail artifacts) on in vivo lung ultrasound scans

    Science.gov (United States)

    Moshavegh, Ramin; Hansen, Kristoffer Lindskov; Møller Sørensen, Hasse; Hemmsen, Martin Christian; Ewertsen, Caroline; Nielsen, Michael Bachmann; Jensen, Jørgen Arendt

    2016-04-01

    This paper presents a novel automatic method for detection of B-lines (comet-tail artifacts) in lung ultrasound scans. B-lines are the most commonly used artifacts for analyzing the pulmonary edema. They appear as laser-like vertical beams, which arise from the pleural line and spread down without fading to the edge of the screen. An increase in their number is associated with presence of edema. All the scans used in this study were acquired using a BK3000 ultrasound scanner (BK Ultrasound, Denmark) driving a 192-element 5:5 MHz wide linear transducer (10L2W, BK Ultrasound). The dynamic received focus technique was employed to generate the sequences. Six subjects, among those three patients after major surgery and three normal subjects, were scanned once and Six ultrasound sequences each containing 50 frames were acquired. The proposed algorithm was applied to all 300 in-vivo lung ultrasound images. The pleural line is first segmented on each image and then the B-line artifacts spreading down from the pleural line are detected and overlayed on the image. The resulting 300 images showed that the mean lateral distance between B-lines detected on images acquired from patients decreased by 20% in compare with that of normal subjects. Therefore, the method can be used as the basis of a method of automatically and qualitatively characterizing the distribution of B-lines.

  9. A perception-inspired building index for automatic built-up area detection in high-resolution satellite images

    OpenAIRE

    Liu, Gang; Xia, Gui-Song; Huang, Xin; Yang, Wen; Zhang, Liangpei

    2013-01-01

    This paper addresses the problem of automatic extraction of built-up areas from high-resolution remote sensing images. We propose a new building presence index from the point view of perception. We argue that built-up areas usually result in significant corners and junctions in high-resolution satellite images, due to the man-made structures and occlusion, and thus can be measured by the geometrical structures they contained. More precisely, we first detect corners and junctions by relying on...

  10. Automatic detection of slow-wave sleep and REM-sleep stages using polysomnographic ECG signals

    International Nuclear Information System (INIS)

    We describe in this paper a new approach of classifying the different sleep stages only by focusing on the polysomnographic ECG signals. We show the pre-processing technique of the ECG signals. At the same time the identifcation and elimination of the different types of artifacts which contain the signal and its reconstruction are shown. The automatic classification of the slow-deep sleep and the rapid eye movement sleep called in this work REM-sleep consists in extracting physiological indicators that characterize these two sleep stages through the polysomnographic ECG signal. In other words, this classification is based on the analysis of the cardiac rhythm during a night's sleep.

  11. An Automatic Optic Disk Detection and Segmentation System using Multi-level Thresholding

    OpenAIRE

    KARASULU, B.

    2014-01-01

    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. Th...

  12. 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.

  13. 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.

  14. Automatic change detection in time series of Synthetic Aperture Radar data using a scale-driven approach

    Science.gov (United States)

    Ajadi, O. A.; Meyer, F. J.

    2013-12-01

    Automatic change detection and change classification from Synthetic Aperture Radar (SAR) images is a difficult task mostly due to the high level of speckle noise inherent to SAR data and the highly non-Gaussian nature of the SAR amplitude information. Several approaches were developed in recent years to deal with SAR specific change detection problems from image pairs and time series of images. Despite these considerable efforts, no satisfying solution to this problem has been found so far. In this paper we present a promising new algorithm for change detection from SAR that is based on a multi-scale analysis of a times series of SAR images. Our approach is composed of three steps, including (1) data enhancement and filtering, (2) multi-scale change detection, and (3) time-series analysis of change detection maps. In the data enhancement and filtering step, we first form time-series of ratio images by dividing all SAR images by a reference acquisition to suppress stationary image information and enhance change signatures. Several methods for reference image selection will be discussed in the paper. The generated ratio images are further log-transformed to create near-Gaussian data and to convert the originally multiplicative noise into additive noise. A subsequent fast non-local mean filter is applied to reduce image noise whilst preserving most of the image details. The filtered log-ratio images are then inserted into a multi-scale change detection algorithm that is composed of: (1) a multi-scale decomposition of the input images using a two-dimensional discrete stationary wavelet transform (2D-SWT); (2) a multi-resolution classification into 'change' and 'no-change' areas; and (3) a scale-driven fusion of the classification results. In a final time-series analysis step the multi-temporal change detection maps are analyzed to identify seasonal, gradual, and abrupt changes. The performance of the developed approach will be demonstrated by application to the

  15. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

    Full Text Available Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS or terrestrial laser scanner (TLS point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

  16. Increased incidence of Mycoplasma pneumoniae infections detected by laboratory-based surveillance in Denmark in 2010

    DEFF Research Database (Denmark)

    Rasmussen, Jens; Voldstedlund, Marianne; R L, Andersen;

    2010-01-01

    In Denmark recurrent epidemics of Mycoplasma pneumoniae infections have been described since the 1950s at intervals of approximately four to six years. The latest epidemic occurred in 2004/05 followed by two years of high incidence and more than three years of low incidence. Due to a recent...... increase in diagnosed cases since late summer 2010, we conducted a survey of positive M. pneumoniae PCR tests performed by clinical microbiology departments in Denmark, which indicated that a new epidemic may be underway....

  17. Malaria incidence in children in South-West Burkina Faso: comparison of active and passive case detection methods.

    Directory of Open Access Journals (Sweden)

    Alfred B Tiono

    Full Text Available BACKGROUND: The aim of this study was to determine the incidence and seasonal pattern of malaria in children in South-West Burkina Faso, and to compare, in a randomized trial, characteristics of cases detected by active and passive surveillance. This study also enabled the planning of a malaria vaccine trial. METHODS: Households with young children, located within 5 kilometers of a health facility, were randomized to one of two malaria surveillance methods. In the first group, children were monitored actively. Each child was visited twice weekly; tympanic temperature was measured, and if the child had a fever or history of fever, a malaria rapid diagnostic test was performed and a blood smear collected. In the second group, children were monitored passively. The child's parent or caregiver was asked to bring the child to the nearest clinic if he was unwell. Follow up lasted 13 months from September 2009. RESULTS: Incidence of malaria (Fever with parasitaemia ≥5,000/µL was 1.18 episodes/child/year in the active cohort and 0.89 in the passive cohort (rate ratio 1.32, 95% CI 1.13-1.54. Malaria cases in the passive cohort were more likely to have high grade fever; but parasite densities were similar in the two groups. Incidence was highly seasonal; when a specific case definition was used, about 60% of cases occurred within the 4 months June-September. CONCLUSION: Passive case detection required at least a 30%-40% increase in the sample size for vaccine trials, compared to active detection, to achieve the same power. However we did not find any evidence that parasite densities were higher with passive than with active detection. The incidence of malaria is highly seasonal and meets the WHO criteria for Seasonal Malaria Chemoprevention (SMC. At least half of the malaria cases in these children could potentially be prevented if SMC was effectively deployed.

  18. 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.

  19. 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.

  20. An automatic detection method to the field wheat based on image processing

    Science.gov (United States)

    Wang, Yu; Cao, Zhiguo; Bai, Xiaodong; Yu, Zhenghong; Li, Yanan

    2013-10-01

    The automatic observation of the field crop attracts more and more attention recently. The use of image processing technology instead of the existing manual observation method can observe timely and manage consistently. It is the basis that extracting the wheat from the field wheat images. In order to improve accuracy of the wheat segmentation, a novel two-stage wheat image segmentation method is proposed. Training stage adjusts several key thresholds which will be used in segmentation stage to achieve the best segmentation results, and counts these thresholds. Segmentation stage compares the different values of color index to determine which class of each pixel is. To verify the superiority of the proposed algorithm, we compared our method with other crop segmentation methods. Experiment results shows that the proposed method has the best performance.

  1. 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.

  2. 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)

  3. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

    NARCIS (Netherlands)

    Murphy, K.; van Ginneken, B.; Schilham, A. M. R.; de Hoop, B. J.; Gietema, H. A.; Prokop, M.

    2009-01-01

    A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neig

  4. AUTOMATIC FAST VIDEO OBJECT DETECTION AND TRACKING ON VIDEO SURVEILLANCE SYSTEM

    Directory of Open Access Journals (Sweden)

    V. Arunachalam

    2012-08-01

    Full Text Available This paper describes the advance techniques for object detection and tracking in video. Most visual surveillance systems start with motion detection. Motion detection methods attempt to locate connected regions of pixels that represent the moving objects within the scene; different approaches include frame-to-frame difference, background subtraction and motion analysis. The motion detection can be achieved by Principle Component Analysis (PCA and then separate an objects from background using background subtraction. The detected object can be segmented. Segmentation consists of two schemes: one for spatial segmentation and the other for temporal segmentation. Tracking approach can be done in each frame of detected Object. Pixel label problem can be alleviated by the MAP (Maximum a Posteriori technique.

  5. Automatic tumor detection in the constrained region for ultrasound breast CAD

    Science.gov (United States)

    Seong, Yeong Kyeong; Park, Moon Ho; Ko, Eun Young; Woo, Kyoung-Gu

    2012-03-01

    In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.

  6. 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.

  7. 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…

  8. 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

  9. Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

    Directory of Open Access Journals (Sweden)

    Fernando eWario

    2015-09-01

    Full Text Available The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as durations or angles are extracted manually directly from the observation hive or from video recordings to quantify dance properties, particularly to determine where bees have foraged. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. As a further step, we have developed technologies to track all individuals of a honeybee colony and detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition and decoding of tags and dances, and we discuss potential research directions that may benefit from automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses for a continuous, experimental recording of nine weeks duration.

  10. 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.

  11. Automatic Vehicle Detection during Nighttime Using Bright Pixel Segment with Spatial Temporal Technique

    Directory of Open Access Journals (Sweden)

    S.Nandhini

    2012-06-01

    Full Text Available The paper proposes an effective Traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic. It identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis technique. By preprocessing noise is removed using median filter. Morphological operation is used to extract candidate headlight objects and then perform shape analysis. Template matching or pattern classification to find the paired headlight of moving vehicles. Salient points are used to represent local properties of image classification. It carries information about image content. Gabor derivation is used for edge detection and feature extraction.

  12. 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.

  13. 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.

  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 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.

  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. 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...

  18. Automatic learning of state machines for fault detection systems in discrete event based distributed systems

    OpenAIRE

    Neuner, Oliver

    2011-01-01

    The electronic components in modern automobiles build up a distributed system with so called electronic control units connected via bus systems. As more safety- and security-relevant functions are implemented in such systems, the more important fault detection becomes. A promising approach to fault detection is to build a system model from state machines and compare its predictions with properties observed in a real system. In the automobile, potential are communication characteristics betwee...

  19. Automatic UltraWideband Radar System for Life Detection of Hidden Humans

    OpenAIRE

    Chao, Chengchung

    2012-01-01

    The ultra-wideband (UWB) is a radio technology which can be used at very low energy levels for short-range high-bandwidth communications by using a large portion of the radio spectrum. In February 2002, the Federal Communications Commission (FCC) gave the permission for UWB to be used for imaging and radar production. The corresponding technology is continuing to be developed furthermore, especially the radar applications of life detection. In various situations, the life-detection sy...

  20. 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.

  1. Lamb wave based automatic damage detection using matching pursuit and machine learning

    International Nuclear Information System (INIS)

    In this study, matching pursuit (MP) has been tested with machine learning algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) to automate the process of damage detection in metallic plates. Here, damage detection is done using the Lamb wave response in a thin aluminium plate simulated using a finite element (FE) method. To reduce the complexity of the Lamb wave response, only the A0 mode is excited and sensed. The procedure adopted for damage detection consists of three major steps, involving signal processing and machine learning (ML). In the first step, MP is used for de-noising and enhancing the sparsity of the database. In the existing literature, MP is used to decompose any signal into a linear combination of waveforms that are selected from a redundant dictionary. In this work, MP is deployed in two stages to make the database sparse as well as to de-noise it. After using MP on the database, it is then passed as input data for ML classifiers. ANN and SVM are used to detect the location of the potential damage from the reduced data. The study demonstrates that the SVM is a robust classifier in the presence of noise and is more efficient than the ANN. Out-of-sample data are used for the validation of the trained and tested classifier. Trained classifiers are found to be successful in the detection of damage with a detection rate of more than 95%. (paper)

  2. 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.

  3. Automatic Eye Blink Generation and Detection System in Digital Image Processing

    Directory of Open Access Journals (Sweden)

    Abha Dubey

    2012-09-01

    Full Text Available The eyes are tracked and correlation scores between the actual eye and the corresponding “closed-eye” template are used to detect blinks. However, it requires offline training for different depths from the camera for the computation of the distance. In addition, the system requires initialization in which an Eye blinking is one of the prominent areas to solve many real world problems. The process of blink detection consists of two phases. These are eye tracking followed by detection of blink. The work that has been carried out for eye tracking only is not suitable for eye blink detection. Stored template for a particular depth is chosen. Once the template is chosen and the system is in operation, the subject will be restricted to be at the specified distance. Another disadvantage of the system is that changing camera Positions require the whole system to be retrained. Further more. The same system in lay not be as effective if it were used on people of different races with disparate eye sizes and distance between the eyes. Therefore some approaches had been proposed for eye tracking along with eyes blink detection. This paper implements one of the approaches given by Michael et al [1,2,3]. The result of template creation accuracy and total blink detection to count number of eye blinks in an image sequence. Online template is completely independent of any past templates that may have been created during the run of the system. At last after analyzing all these approaches some of the parameters we obtained on which better performance of eye blink detection algorithm depend.

  4. 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.)

  5. 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.

  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. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies

    International Nuclear Information System (INIS)

    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.)

  8. 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.

  9. Automatic detection of outlines. Application to the quantitative analysis of renal scintiscanning pictures

    International Nuclear Information System (INIS)

    The purpose of the work described is the finalizing of a method making it possible automatically to extract the significant outlines on a renal scintiscanning picture. The algorithms must be simple and of high performance, their routine execution on a mini-computer must be fast enough to compete effectively with human performances. However, the method that has been developed is general enough to be adapted, with slight modifications, to another type of picture. The first chapter is a brief introduction to the principle of scintiscanning, the equipment used and the type of picture obtained therefrom. In the second chapter the various approaches used for form recognition and scene analysis are very briefly described with the help of examples. The third chapter deals with pretreatment techniques (particularly the machine operators) used for segmenting the pictures. Chapter four presents techniques which segment the picture by parallel processing of all its points. In chapter five a description is given of the sequential research techniques of the outline elements, drawing inspiration from the methods used in artificial intelligence for resolving the optimization problem. The sixth chapter shows the difficulties encountered in extracting the renal outlines and the planning technique stages adopted to overcome these difficulties. Chapter seven describes in detail the two research methods employed for generating the plan. In chapter eight, the methods used for extending the areas obtained on the plan and for refining the outlines that bound them are dealt with. Chapter nine is a short presentation of the organization of the programmes and of their data structure. Finally, examples of results are given in chapter ten

  10. 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.

  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. Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions.

    Science.gov (United States)

    Solosenko, Andrius; Petrenas, Andrius; Marozas, Vaidotas

    2015-10-01

    This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear outputs was investigated as a feature classifier. PhysioNet databases, namely, the MIMIC II and the MIMIC, were used for training and testing, respectively. After annotating the PPGs with respect to synchronously recorded electrocardiogram, two main types of PVCs were distinguished: with and without the observable PPG pulse. The obtained sensitivity and specificity values for both considered PVC types were 92.4/99.9% and 93.2/99.9%, respectively. The achieved high classification results form a basis for a reliable PVC detection using a less obtrusive approach than the electrocardiography-based detection methods. PMID:26513800

  13. 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.

  14. 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.

  15. Automatic Detection of Fade-in and Fade-out in Video Sequences

    OpenAIRE

    Fernando, WAC; Canagarajah, CN; Bull, DR

    1999-01-01

    A common video indexing technique is to segment video shots by identifying scene changes and then to extract features. This paper discusses a novel algorithm for detecting fade-in and fade-out using statistical features of both luminance and chrominance signals. The ratio between incremental change in the mean of the luminance signal to the chrominance (average sum of Cr and Cb) is considered to identify fade-in and fade-out. Results show that the algorithm is capable of detecting all fade re...

  16. 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.

  17. Automatic detection of a hand-held needle in ultrasound via phased-based analysis of the tremor motion

    Science.gov (United States)

    Beigi, Parmida; Salcudean, Septimiu E.; Rohling, Robert; Ng, Gary C.

    2016-03-01

    This paper presents an automatic localization method for a standard hand-held needle in ultrasound based on temporal motion analysis of spatially decomposed data. Subtle displacement arising from tremor motion has a periodic pattern which is usually imperceptible in the intensity image but may convey information in the phase image. Our method aims to detect such periodic motion of a hand-held needle and distinguish it from intrinsic tissue motion, using a technique inspired by video magnification. Complex steerable pyramids allow specific design of the wavelets' orientations according to the insertion angle as well as the measurement of the local phase. We therefore use steerable pairs of even and odd Gabor wavelets to decompose the ultrasound B-mode sequence into various spatial frequency bands. Variations of the local phase measurements in the spatially decomposed input data is then temporally analyzed using a finite impulse response bandpass filter to detect regions with a tremor motion pattern. Results obtained from different pyramid levels are then combined and thresholded to generate the binary mask input for the Hough transform, which determines an estimate of the direction angle and discards some of the outliers. Polynomial fitting is used at the final stage to remove any remaining outliers and improve the trajectory detection. The detected needle is finally added back to the input sequence as an overlay of a cloud of points. We demonstrate the efficiency of our approach to detect the needle using subtle tremor motion in an agar phantom and in-vivo porcine cases where intrinsic motion is also present. The localization accuracy was calculated by comparing to expert manual segmentation, and presented in (mean, standard deviation and root-mean-square error) of (0.93°, 1.26° and 0.87°) and (1.53 mm, 1.02 mm and 1.82 mm) for the trajectory and the tip, respectively.

  18. Automatic change detection and quantification of dermatological diseases with an application to psoriasis images

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Butakoff, C.; Ersbøll, Bjarne Kjær;

    2007-01-01

    Change monitoring in skin lesion analysis has proven to be a useful adjunct in their assessment. This article presents a comparative study of the available change detection techniques applied to change visualization and quantification in bi-temporal psoriasis images. The chosen methods are evalua...

  19. AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    E. Khesali

    2015-12-01

    Full Text Available This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1 feature extraction; 2 feature selection; and 3 ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  20. Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning

    Directory of Open Access Journals (Sweden)

    Nadja Stumberg

    2014-10-01

    Full Text Available A large proportion of Norway’s land area is occupied by the forest-tundra ecotone. The vegetation of this temperature-sensitive ecosystem between mountain forest and the alpine zone is expected to be highly affected by climate change and effective monitoring techniques are required. For the detection of such small pioneer trees, airborne laser scanning (ALS has been proposed as a useful tool employing laser height data. The objective of this study was to assess the capability of an unsupervised classification for automated monitoring programs of small individual trees using high-density ALS data. Field and ALS data were collected along a 1500 km long transect stretching from northern to southern Norway. Different laser and tree height thresholds were tested in various combinations within an unsupervised classification of tree and nontree raster cells employing different cell sizes. Suitable initial cell sizes for the exclusion of large treeless areas as well as an optimal cell size for tree cell detection were determined. High rates of successful tree cell detection involved high levels of commission error at lower laser height thresholds, however, exceeding the 20 cm laser height threshold, the rates of commission error decreased substantially with a still satisfying rate of successful tree cell detection.

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

    International Nuclear Information System (INIS)

    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

  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. Morphological neural networks for automatic target detection by simulated annealing learning algorithm

    Institute of Scientific and Technical Information of China (English)

    余农; 吴昊; 吴常泳; 李范鸣; 吴立德

    2003-01-01

    A practical neural network model for morphological filtering and a simulated annealing optimal algorithm for the network parameters training are proposed in this paper. It is pointed out that the optimal designing process of the morphological filtering network in fact is the optimal learning process of adjusting network parameters (structuring element, or SE for short) to accommodate image environment. Then the network structure may possess the characteristics ofimage targets, and so give specific infor- mation to the SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to complex changing image. For application tomotional image target detection, dynamic training algorithm is applied to the designing process using asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has invariant propertywith respect to shift, scale and rotation of moving target in continuing detection of moving targets.

  4. 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.

  5. 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...... of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64...... source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given...

  6. 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.

  7. Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform

    OpenAIRE

    Marek eAdamczyk; Lisa eGenzel; Martin eDresler; Axel eSteiger; Elisabeth eFriess

    2015-01-01

    Mounting evidence for the role of sleep spindles in neuroplasticity has led to an increased interest in these non-rapid eye movement (NREM) sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here, we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform (CWT) and individual adjustment of slow and fast spindle frequency ranges. Eighteen nap recordings of ten subjects were used for algorit...

  8. Automatic reaction to a chemical event detected by a low-cost wireless chemical sensing network

    OpenAIRE

    Beirne, Stephen; Lau, King-Tong; Corcoran, Brian; Diamond, Dermot

    2009-01-01

    A test-scale wireless chemical sensor network (WCSN) has been deployed within a controlled Environmental Chamber (EC). The combined signals from the WCSN were used to initiate a controllable response to the detected chemical event. When a particular sensor response pattern was obtained, a purging cycle was initiated. Sensor data were continuously checked against user-defined action limits, to determine if a chemical event had occurred. An acidic contaminant was used to demonstrate the respons...

  9. 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.

  10. 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.

  11. Interoperable cross-domain semantic and geospatial framework for automatic change detection

    Science.gov (United States)

    Kuo, Chiao-Ling; Hong, Jung-Hong

    2016-01-01

    With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.

  12. 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.

  13. Automatic detection of diseased tomato plants using thermal and stereo visible light images.

    Science.gov (United States)

    Raza, Shan-e-Ahmed; Prince, Gillian; Clarkson, John P; Rajpoot, Nasir M

    2015-01-01

    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. PMID:25861025

  14. 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.

  15. Automatic detection of karstic sinkholes in seismic 3D images using circular Hough transform

    Science.gov (United States)

    Heydari Parchkoohi, Mostafa; Keshavarz Farajkhah, Nasser; Salimi Delshad, Meysam

    2015-10-01

    More than 30% of hydrocarbon reservoirs are reported in carbonates that mostly include evidence of fractures and karstification. Generally, the detection of karstic sinkholes prognosticate good quality hydrocarbon reservoirs where looser sediments fill the holes penetrating hard limestone and the overburden pressure on infill sediments is mostly tolerated by their sturdier surrounding structure. They are also useful for the detection of erosional surfaces in seismic stratigraphic studies and imply possible relative sea level fall at the time of establishment. Karstic sinkholes are identified straightforwardly by using seismic geometric attributes (e.g. coherency, curvature) in which lateral variations are much more emphasized with respect to the original 3D seismic image. Then, seismic interpreters rely on their visual skills and experience in detecting roughly round objects in seismic attribute maps. In this paper, we introduce an image processing workflow to enhance selective edges in seismic attribute volumes stemming from karstic sinkholes and finally locate them in a high quality 3D seismic image by using circular Hough transform. Afterwards, we present a case study from an on-shore oilfield in southwest Iran, in which the proposed algorithm is applied and karstic sinkholes are traced.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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%.

  1. Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

    Science.gov (United States)

    Helle, K. B.; Müller, T. O.; Astrup, P.; Dyve, J. E.

    2014-05-01

    Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often 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 of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64 source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given 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. The DOT runs on a server and can be accessed via common web browsers; it can also be installed locally.

  2. Automatic detection of microcalcifications using mathematical morphology and a support vector machine.

    Science.gov (United States)

    Zhang, Erhu; Wang, Fan; Li, Yongchao; Bai, Xiaonan

    2014-01-01

    In this paper, we propose a novel method for the detection of microcalcifications using mathematical morphology and a support vector machine (SVM). First, the contrast in the original mammogram was improved by gamma correction and two carefully designed structural elements were used to enhance any microcalcifications. Next, the potential regions were extracted using our proposed dual-threshold technique. Finally, a SVM classifier was used to reduce the number of false positives. The performance of the proposed method was evaluated using the MIAS database. The experimental results demonstrated the efficiency and effectiveness of our method. PMID:24211882

  3. Automatic phase detection in seismic data using the discrete wavelet transform

    OpenAIRE

    Oonincx, P.J.

    1998-01-01

    Seismic data consist of traces, which contain information about a seismic event, but in some period of time the traces may be just noise. A trace which c ontains seismic information, is called a seismic signal. Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarisation. Amongst others, a significant phase is the S-phase. We present a fast algorithm to detect the S-phase in a three-comp...

  4. Automatic detection of potentially illegal online sales of elephant ivory via data mining

    OpenAIRE

    Hernandez-Castro, Julio C.; Roberts, David L.

    2015-01-01

    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 descr...

  5. Dealing with Inaccurate Face Detection for Automatic Gender Recognition with Partially Occluded Faces

    Science.gov (United States)

    Andreu, Yasmina; García-Sevilla, Pedro; Mollineda, Ramón A.

    Gender recognition problem has not been extensively studied in situations where the face cannot be accurately detected and it also can be partially occluded. In this contribution, a comparison of several characterisation methods of the face is presented and they are evaluated in four different experiments that simulate the previous scenario. Two of the characterisation techniques are based on histograms, LBP and local contrast values, and the other one is a new kind of features, called Ranking Labels, that provide spatial information. Experiments have proved Ranking Labels description is the most reliable in inaccurate situations.

  6. Automatic Detection of ECG R-R Interval using Discrete Wavelet Transformation

    OpenAIRE

    Vanisree K,; Jyothi Singaraju

    2011-01-01

    Detection of QRS-complexes takes an important role in the analysis of ECG signal, based on which the number of heart beats and an irregularity of a heart beat through R-R interval can be determined. Since an ECG may be of different lengths and as being a non-stationary signal, the irregularity may not be periodic instead it can be shown up at any interval of the signal, it is difficult forphysician to analyze ECG manually. In the present study an algorithm has been developed to preprocess and...

  7. Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

    OpenAIRE

    Yochum, Maxime; Renaud, Charlotte; Jacquir, Sabir

    2016-01-01

    International audience In this paper, a new method based on the continuous wavelet transform is described in order to detect the QRS, P and T waves. QRS, P and T waves may be distinguished from noise, baseline drift or irregular heartbeats. The algorithm, described in this paper, has been evaluated using the Computers in Cardiology (CinC) Challenge 2011 database and also applied on the MIT-BIH Arrhythmia database (MITDB). The data from the CinC Challenge 2011 are standard 12 ECG leads reco...

  8. 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.

  9. Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

    Science.gov (United States)

    Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

    2012-12-01

    This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

  10. 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.

  11. 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.

  12. Early detection of the incidence of malignancy in mammograms using digital image correlation

    International Nuclear Information System (INIS)

    The digital image correlation has proved an effective way for Pattern Recognition, this research to identify the using Findings digitally extracted from a mammographic image, which is the means used by more specialists to determine if a person is a candidate or not, a Suffer Breast Cancer. This shown that early detection of symptom logy 'carcinogenic' is the key . (Author)

  13. 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.

  14. 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.

  15. 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,...

  16. Automatic detection and high resolution fine structure analysis of conic X-ray diffraction lines

    Energy Technology Data Exchange (ETDEWEB)

    Bauch, J.; Henschel, F. [TU Dresden, Institut fuer Werkstoffwissenschaft, 01069 Dresden (Germany); Schulze, M. [TU Dresden, Institut fuer Photogrammetrie und Fernerkundung, 01069 Dresden (Germany)

    2011-05-15

    The presented method demonstrates a first step in the development of a high resolution ''Residual stress microscope'' and facilitates through the implementation of largely automated procedures a fast detection of diffraction lines in the form of conic sections. It has been implemented for, but is not exclusively used for the Kossel technique and the ''X-ray Rotation-Tilt Method'' (XRT). The resulting multifaceted evaluable data base of many X-ray diffraction radiographies can be used not only for the systematic analysis of anomalies in diffraction lines (reflection fine structure), but also for direct calculation and output of precision residual stress tensors. (copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  17. 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...

  18. Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks.

    Science.gov (United States)

    Queiroz, Vinícius; Luz, Eduardo; Moreira, Gladston; Guarda, Álvaro; Menotti, David

    2015-01-01

    This paper intends to bring new insights in the methods for extracting features for cardiac arrhythmia detection and classification systems. We explore the possibility for utilizing vectorcardiograms (VCG) along with electrocardiograms (ECG) to get relevant informations from the heartbeats on the MIT-BIH database. For this purpose, we apply complex networks to extract features from the VCG. We follow the ANSI/AAMI EC57:1998 standard, for classifying the beats into 5 classes (N, V, S, F and Q), and de Chazal's scheme for dataset division into training and test set, with 22 folds validation setup for each set. We used the Support Vector Machinhe (SVM) classifier and the best result we chose had a global accuracy of 84.1%, while still obtaining relatively high Sensitivities and Positive Predictive Value and low False Positive Rates, when compared to other papers that follows the same evaluation methodology that we do. PMID:26737464

  19. Automatic polyp detection using global geometric constraints and local intensity variation patterns.

    Science.gov (United States)

    Tajbakhsh, Nima; Gurudu, Suryakanth R; Liang, Jianming

    2014-01-01

    This paper presents a new method for detecting polyps in colonoscopy. Its novelty lies in integrating the global geometric constraints of polyps with the local patterns of intensity variation across polyp boundaries: the former drives the detector towards the objects with curvy boundaries, while the latter minimizes the misleading effects of polyp-like structures. This paper makes three original contributions: (1) a fast and discriminative patch descriptor for precisely characterizing patterns of intensity variation across boundaries, (2) a new 2-stage classification scheme for accurately excluding non-polyp edges from an overcomplete edge map, and (3) a novel voting scheme for robustly localizing polyps from the retained edges. Evaluations on a public database and our own videos demonstrate that our method is promising and outperforms the state-of-the-art methods. PMID:25485377

  20. Cleaning the Usno-B Catalog Through Automatic Detection of Optical Artifacts

    Science.gov (United States)

    Barron, Jonathan T.; Stumm, Christopher; Hogg, David W.; Lang, Dustin; Roweis, Sam

    2008-01-01

    The USNO-B Catalog contains spurious entries that are caused by diffraction spikes and circular reflection halos around bright stars in the original imaging data. These spurious entries appear in the Catalog as if they were real stars; they are confusing for some scientific tasks. The spurious entries can be identified by simple computer vision techniques because they produce repeatable patterns on the sky. Some techniques employed here are variants of the Hough transform, one of which is sensitive to (two-dimensional) overdensities of faint stars in thin right-angle cross patterns centered on bright (halo criteria. The spurious entries are often detected in more than two bands and are not overwhelmingly outliers in any photometric properties; they therefore cannot be rejected easily on other grounds, i.e., without the use of computer vision techniques. We demonstrate our method, and return to the community in electronic form a table of spurious entries in the Catalog.

  1. 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.

  2. 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.

  3. 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.

  4. 基于速度分类算法的交通事件视频检测系统设计%Video Detection System Design for Traffic Incidents Based on Speed Classification Algorithm

    Institute of Scientific and Technical Information of China (English)

    熊昕; 徐建闽

    2013-01-01

    Real-time video traffic incident detection method was proposed based on speed classification algorithm. In addition , traffic detection method, vehicles cross-road processing, speed detection, traffic flow detection and the identification of traffic events were also discussed. Based on vehicle detection and tracking, events such as traffic stop, lane transform times, slow traffic congestion and others can be identified and detected automatically to derive traffic flow, occupation ratio, queue length, average speed and other transportation parameters. In comparison with the traditional traffic incident detection system, the system is intuitive convenient and low-cost,and has good market demand and practical value.%提出基于速度分类算法的交通事件实时视频检测方法,并对交通量检测方法、车辆跨道处理、速度检测、交通状况检测及交通事件识别等进行了研究.在车辆检测与跟踪的基础上,可实现车辆停止、慢行、车道变换次数和车流拥挤等交通事件识别功能,通过自动检测车辆避障、车道变换、超速、慢速、停止和交通阻塞等事件,获得交通流量、占有率、排队长度、车型和平均车速等交通参数.与传统交通事件检测系统相比,具有直观方便、费用低等优点.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Automatic detection of respiration rate from ambulatory single-lead ECG.

    Science.gov (United States)

    Boyle, Justin; Bidargaddi, Niranjan; Sarela, Antti; Karunanithi, Mohan

    2009-11-01

    Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2-0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were +/-4 breaths per minute (bpm) (all activities), +/-2 bpm (lying and sitting), and +/-1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods. PMID:19775978

  10. 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.

  11. Automatic Detection of Attention Shifts in Infancy: Eye Tracking in the Fixation Shift Paradigm.

    Directory of Open Access Journals (Sweden)

    Louisa Kulke

    Full Text Available This study measured changes in switches of attention between 1 and 9 months of age in 67 typically developing infants. Remote eye-tracking (Tobii X120 was used to measure saccadic latencies, related to switches of fixation, as a measure of shifts of attention, from a central stimulus to a peripheral visual target, measured in the Fixation Shift Paradigm. Fixation shifts occur later if the central fixation stimulus stays visible when the peripheral target appears (competition condition, than if the central stimulus disappears as the peripheral target appears (non-competition condition. This difference decreases with age. Our results show significantly faster disengagement in infants over 4 months than in the younger group, and provide more precise measures of fixation shifts, than behavioural observation with the same paradigm. Reduced saccadic latencies in the course of a test session indicate a novel learning effect. The Fixation Shift Paradigm combined with remote eye-tracking measures showed improved temporal and spatial accuracy compared to direct observation by a trained observer, and allowed an increased number of trials in a short testing time. This makes it an infant-friendly non-invasive procedure, involving minimal observational training, suitable for use in future studies of clinical populations to detect early attentional abnormalities in the first few months of life.

  12. Cleaning the USNO-B catalog through automatic detection of optical artifacts

    International Nuclear Information System (INIS)

    The USNO-B Catalog contains spurious entries that are caused by diffraction spikes and circular reflection halos around bright stars in the original imaging data. These spurious entries appear in the Catalog as if they were real stars; they are confusing for some scientific tasks. The spurious entries can be identified by simple computer vision techniques because they produce repeatable patterns on the sky. Some techniques employed here are variants of the Hough transform, one of which is sensitive to (two-dimensional) overdensities of faint stars in thin right-angle cross patterns centered on bright (<13 mag) stars, and one of which is sensitive to thin annular overdensities centered on very bright (<7 mag) stars. After enforcing conservative statistical requirements on spurious-entry identifications, we find that of the 1,042,618,261 entries in the USNO-B Catalog, 24,148,382 (2.3 percent) are identified as spurious by diffraction-spike criteria and 196,133 (0.02 percent) are identified as spurious by reflection-halo criteria. The spurious entries are often detected in more than two bands and are not overwhelmingly outliers in any photometric properties; they therefore cannot be rejected easily on other grounds, i.e., without the use of computer vision techniques. We demonstrate our method, and return to the community in electronic form a table of spurious entries in the Catalog.

  13. Automatic sleep spindle detection and genetic influence estimation using continuous wavelet transform

    Directory of Open Access Journals (Sweden)

    Marek eAdamczyk

    2015-11-01

    Full Text Available Mounting evidence for the role of sleep spindles for neuroplasticity led to an increased interest in these NREM sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform and individual adjustment of slow and fast spindle frequency ranges. 18 nap recordings of 10 subjects were used for algorithm validation. Our method was compared with human scorer and commercially available SIESTA spindle detector. For the validation set, mean agreement between our detector and human scorer measured during sleep stage 2 using kappa coefficient was 0.45, whereas mean agreement between our detector and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. Then, we applied our method to a study in monozygotic (MZ and dizygotic (DZ twins examining the heritability of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence of all slow spindle parameters, weaker genetic effect on fast spindles and no effects on fast spindle density and number during stage 2 sleep.

  14. Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step.

    Science.gov (United States)

    Garcia-Ceja, Enrique; Osmani, Venet; Mayora, Oscar

    2016-07-01

    Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer. PMID:26087509

  15. 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.

  16. Automatic Detection of Previously-Unseen Application States for Deployment Environment Testing and Analysis

    Science.gov (United States)

    Murphy, Christian; Vaughan, Moses; Ilahi, Waseem; Kaiser, Gail

    2010-01-01

    For large, complex software systems, it is typically impossible in terms of time and cost to reliably test the application in all possible execution states and configurations before releasing it into production. One proposed way of addressing this problem has been to continue testing and analysis of the application in the field, after it has been deployed. A practical limitation of many such automated approaches is the potentially high performance overhead incurred by the necessary instrumentation. However, it may be possible to reduce this overhead by selecting test cases and performing analysis only in previously-unseen application states, thus reducing the number of redundant tests and analyses that are run. Solutions for fault detection, model checking, security testing, and fault localization in deployed software may all benefit from a technique that ignores application states that have already been tested or explored. In this paper, we present a solution that ensures that deployment environment tests are only executed in states that the application has not previously encountered. In addition to discussing our implementation, we present the results of an empirical study that demonstrates its effectiveness, and explain how the new approach can be generalized to assist other automated testing and analysis techniques intended for the deployment environment. PMID:21197140

  17. 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

  18. Incidence and detection of beak and feather disease virus in psittacine birds in the UAE

    OpenAIRE

    F. Hakimuddin; Abidi, F; Jafer, O; Li, C; U. Wernery; Ch. Hebel; K. Khazanehdari

    2016-01-01

    Beak and feather disease is caused by Circovirus, which affects actively growing beak and feather cells of avian species. The disease affects mainly young birds while older birds may overcome the disease with few lasting effects. Due to lack of treatment, the only way to control the disease is through hygiene and early diagnosis. As a diagnostic tool, we have established a Taqman probe based real-time PCR assay to detect the presence of the viral genome in psittacine birds in UAE and reported...

  19. {sup 13}C-detected NMR experiments for automatic resonance assignment of IDPs and multiple-fixing SMFT processing

    Energy Technology Data Exchange (ETDEWEB)

    Dziekański, Paweł; Grudziąż, Katarzyna [University of Warsaw, Faculty of Chemistry, Biological and Chemical Research Centre (Poland); Jarvoll, Patrik [Agilent Technologies (United Kingdom); Koźmiński, Wiktor; Zawadzka-Kazimierczuk, Anna, E-mail: anzaw@chem.uw.edu.pl [University of Warsaw, Faculty of Chemistry, Biological and Chemical Research Centre (Poland)

    2015-06-15

    Intrinsically disordered proteins (IDPs) have recently attracted much interest, due to their role in many biological processes, including signaling and regulation mechanisms. High-dimensional {sup 13}C direct-detected NMR experiments have proven exceptionally useful in case of IDPs, providing spectra with superior peak dispersion. Here, two such novel experiments recorded with non-uniform sampling are introduced, these are 5D HabCabCO(CA)NCO and 5D HNCO(CA)NCO. Together with the 4D (HACA)CON(CA)NCO, an extension of the previously published 3D experiments (Pantoja-Uceda and Santoro in J Biomol NMR 59:43–50, 2014. doi: 10.1007/s10858-014-9827-1 10.1007/s10858-014-9827-1 ), they form a set allowing for complete and reliable resonance assignment of difficult IDPs. The processing is performed with sparse multidimensional Fourier transform based on the concept of restricting (fixing) some of spectral dimensions to a priori known resonance frequencies. In our study, a multiple-fixing method was developed, that allows easy access to spectral data. The experiments were tested on a resolution-demanding alpha-synuclein sample. Due to superior peak dispersion in high-dimensional spectrum and availability of the sequential connectivities between four consecutive residues, the overwhelming majority of resonances could be assigned automatically using the TSAR program.

  20. 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.

  1. Detection of Breast Cancer with Mammography in the First Screening Round in Relation to Expected Incidence in Different Age Groups

    International Nuclear Information System (INIS)

    The ratio (R) of prevalence of screening-detected breast cancer in the first screening round (P) was compared with the expected incidence rate (I) for different age groups in several screening programs. Published data on the first screening round from three Swedish randomized trials and six counties with service screening were used. The women invited to take part in the screening were aged 40-74 years. Not only P and I but also R increased with increasing age. With the youngest age group as reference, the increase was statistically significant for both invasive cancer and invasive cancer and carcinoma in situ together. The studied ratio (R) can be thought of as a measure of efficiency in detecting breast cancer cases in mammography screening. The reasons for the increase are probably that the breast tissue of younger women is denser, which makes the cancer more difficult to detect by mammography, and that slow-growing cancers tend to appear more frequently in older women

  2. {sup 18F}-FDG PET imaging with dual head gamma camera and co-incidence detection

    Energy Technology Data Exchange (ETDEWEB)

    Quach, T.; Camden, B.M.; Chu, J.M.G. [Liverpool Health Services, Liverpool, NSW (Australia). Department of Nuclear Medicine and Clinical Ultrasound

    1998-06-01

    Full text: {sup 18F}-Fluorodeoxyglucose (FDG) positron tomography is based on the detection of two 511 keV photons which are produced 180 deg apart as a result of an annihilation of a positron and an electron. Apart from the dedicated PET scanner, dual head gamma camera designed for Co-incidence Detection (CD) can now perform `{sup 18}F-FDG PET studies. CD imaging involves using a dual head gamma camera to detect those photons which are 180 deg apart and fall within a timing window of 15 nsec. No collimators are required as a timing gate of 15 nsec is used. {sup 18}F-FDG studies are performed using an ADAC Solus Molecular Co-incidence Detection (MCD) dual head gamma camera. The patients are fasted from midnight but well hydrated before the scan. Prior to injection, the blood sugar levels (BSL) are measured. For optimal {sup 18}F-FDG uptake, the BSL should be less than 8.9 mmol/L. A dose of 200MBq of {sup 18}F-FDG is intravenously injected via a cannula. Scanning commences at 1 hour post injection. To perform a wholebody tomography of the torso, the patient must void before scanning to reduce bladder activity. Excessive bladder activity leads to significant image degradation, therefore the wholebody tomography is started from the pelvis. Depending on the patient torso length, either 2 or 3 tomographies are collected with a 50% overlap. Each tomography is collected for 40 seconds per step for 32 steps. To avoid attenuation from the upper limbs, the patient is positioned supine with the arms above the head. If a patient cannot tolerate this position, scanning with the arms by the side may be necessary since the scanning time may take up to 50 minutes. If the area of interest is the neck, scanning with the patient`s arms down by their sides is preferred, although attenuation will occur. To scan the brain, a circular tomography is performed using 32 steps at 80 seconds per step. For processing purposes, the Singles count rate for each detector must be between 800K and

  3. 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

  4. Statistical analysis of automatically detected ion density variations recorded by DEMETER and their relation to seismic activity

    Directory of Open Access Journals (Sweden)

    Michel Parrot

    2012-04-01

    Full Text Available

    Many examples of ionospheric perturbations observed during large seismic events were recorded by the low-altitude satellite DEMETER. However, there are also ionospheric variations without seismic activity. The present study is devoted to a statistical analysis of the night-time ion density variations. Software was implemented to detect variations in the data before earthquakes world-wide. Earthquakes with magnitudes >4.8 were selected and classified according to their magnitudes, depths and locations (land, close to the coast, or below the sea. For each earthquake, an automatic search for ion density variations was conducted from 15 days before the earthquake, when the track of the satellite orbit was at less than 1,500 km from the earthquake epicenter. The result of this first step provided the variations relative to the background in the vicinity of the epicenter for each 15 days before each earthquake. In the second step, comparisons were carried out between the largest variations over the 15 days and the earthquake magnitudes. The statistical analysis is based on calculation of the median values as a function of the various seismic parameters (magnitude, depth, location. A comparison was also carried out with two other databases, where on the one hand, the locations of the epicenters were randomly modified, and on the other hand, the longitudes of the epicenters were shifted. The results show that the intensities of the ionospheric perturbations are larger prior to the earthquakes than prior to random events, and that the perturbations increase with the earthquake magnitudes.


  5. 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.

  6. 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

  7. Real-time and label-free detection of biomolecular interactions by oblique-incidence reflectivity difference method

    Science.gov (United States)

    Wang, Xu; Lu, Heng; Dai, Jun; Wen, Juan; Yuan, Kun; Lü, Hui-Bin; Jin, Kui-Juan; Zhou, Yue-Liang; Yang, Guo-Zhen

    2011-01-01

    We successfully conduct the label-free and real-time detection of the interactions between epoxy groups and rabbit IgG and 5' CTT CAG GTC ATG AGC CTG AT 3' oligonucleotide, and between the hybridization of 5' CTT CAG GTC ATG AGC CTG AT 3' and its complementary 3' GAA GTC CAG TAC TCG GAC TA 5' oligonucleotide, by the oblique-incidence reflectivity difference (OI-RD) method. The dynamic curves of OI-RD signals, corresponding to the kinetic processes of biomolecular combination or hybridization, are acquired. In our case, the combination of epoxy groups with rabbit IgG and 5' CTT CAG GTC ATG AGC CTG AT 3' oligonucleotide need almost one and a half hours and about two hundred seconds, respectively; and the hybridization of the two oligonucleotides needs about five hundred seconds. The experimental results show that the OI-RD is a promising method for the real-time and label-free detection of biomolecular interactions.

  8. Suspension fluorescence in situ hybridization (S-FISH) combined with automatic detection and laser microdissection for STR profiling of male cells in male/female mixtures

    OpenAIRE

    Vandewoestyne, Mado; Van Hoofstat, David; Van Nieuwerburgh, Filip; Deforce, Dieter

    2009-01-01

    Laser microdissection is a valuable tool for isolating specific cells from mixtures, such as male cells in a mixture with female cells, e.g., in cases of sexual assault. These cells can be stained with Y-chromosome-specific probes. We developed an automatic screening method to detect male cells after fluorescence in situ hybridization in suspension (S-FISH). To simulate forensic casework, the method was tested on female saliva after cataglottis (a kiss involving tongue-to-tongue contact) and ...

  9. Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

    OpenAIRE

    Martinis, Sandro

    2010-01-01

    This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activi...

  10. On detection and automatic tracking of butt weld line in thin wall pipe welding by a mobile robot with visual sensor

    International Nuclear Information System (INIS)

    An automatic pipe welding mobile robot system with visual sensor was constructed. The robot can move along a pipe, and detect the weld line to be welded by visual sensor. Moreover, in order to make an automatic welding, the welding torch can track the butt weld line of the pipes at a constant speed by rotating the robot head. Main results obtained are summarized as follows: 1) Using a proper lighting fixed in front of the CCD camera, the butt weld line of thin wall pipes can be recongnized stably. In this case, the root gap should be approximately 0.5 mm. 2) In order to detect the weld line stably during moving along the pipe, a brightness distribution measured by the CCD camera should be subjected to smoothing and differentiating and then the weld line is judged by the maximum and minimum values of the differentials. 3) By means of the basic robot system with a visual sensor controlled by a personal computer, the detection and in-process automatic tracking of a weld line are possible. The average tracking error was approximately 0.2 mm and maximum error 0.5 mm and the welding speed was held at a constant value with error of about 0.1 cm/min. (author)

  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. AUTOMATIC DOMINANCE DETECTION IN DYADIC CONVERSATIONS (Detección automática de la dominancia en conversaciones diádicas

    Directory of Open Access Journals (Sweden)

    Sergio Escalera

    2010-04-01

    Full Text Available Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correla¬tion among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers’ opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.

  13. Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets.

    Science.gov (United States)

    Gao, Xinpei; Kitslaar, Pieter H; Budde, Ricardo P J; Tu, Shengxian; de Graaf, Michiel A; Xu, Liang; Xu, Bo; Scholte, Arthur J H A; Dijkstra, Jouke; Reiber, Johan H C

    2016-08-01

    Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32 mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40 mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the

  14. 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。

  15. 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

  16. Prediction of Cyberbullying Incidents on the Instagram Social Network

    OpenAIRE

    Hosseinmardi, Homa; Mattson, Sabrina Arredondo; Rafiq, Rahat Ibn; Han, Richard; Lv, Qin; Mishr, 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 and predict incidents of cyberbullying in Instagram, a media-based mobile social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower...

  17. Automatic detection of local arterial input functions through Independent Component Analysis on Dynamic Contrast enhanced Magnetic Resonance Imaging.

    Science.gov (United States)

    Narvaez, Mario; Ruiz-Espana, Silvia; Arana, Estanislao; Moratal, David

    2015-08-01

    Arterial Input Function (AIF) is obtained from perfusion studies as a basic parameter for the calculus of hemodynamic variables used as surrogate markers of the vascular status of tissues. However, at present, its identification is made manually leading to high subjectivity, low repeatability and considerable time consumption. We propose an alternative method to automatically identify local AIF in perfusion images using Independent Component Analysis. PMID:26737244

  18. Automatic Detection, Segmentation and Classification of Retinal Horizontal Neurons in Large-scale 3D Confocal Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Karakaya, Mahmut [ORNL; Kerekes, Ryan A [ORNL; Gleason, Shaun Scott [ORNL; Martins, Rodrigo [St. Jude Children' s Research Hospital; Dyer, Michael [St. Jude Children' s Research Hospital

    2011-01-01

    Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

  19. Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases.

    Science.gov (United States)

    Cheng, Chung-Chuan; Lu, Chun-Feng; Hsieh, Tsu-Yi; Lin, Yaw-Jen; Taur, Jin-Shiuh; Chen, Yung-Fu

    2015-10-01

    Indirect immunofluorescence technique applied on HEp-2 cell substrates provides the major screening method to detect ANA patterns in the diagnosis of autoimmune diseases. Currently, the ANA patterns are mostly inspected by experienced physicians to identify abnormal cell patterns. The objective of this study is to design a computer-assisted system to automatically detect cell patterns of IIF images for the diagnosis of autoimmune diseases in the clinical setting. The system simulates the functions of modern flow cytometer and provides the diagnostic reports generated by the system to the technicians and physicians through the radar graphs, box-plots, and tables. The experimental results show that, among the IIF images collected from 17 patients, 6 were classified as coarse-speckled, 3 as diffused, 2 as discrete-speckled, 1 as fine-speckled, 2 as nucleolar, and 3 as peripheral patterns, which were consistent with the patterns determined by the physicians. In addition to recognition of cell patterns, the system also provides the function to automatically generate the report for each patient. The time needed for the whole procedure is less than 30 min, which is more efficient than the manual operation of the physician after inspecting the ANA IIF images. Besides, the system can be easily deployed on many desktop and laptop computers. In conclusion, the designed system, containing functions for automatic detection of ANA cell pattern and generation of diagnostic report, is effective and efficient to assist physicians to diagnose patients with autoimmune diseases. The limitations of the current developed system include (1) only a unique cell pattern was considered for the IIF images collected from a patient, and (2) the cells during the process of mitosis were not adopted for cell classification. PMID:26289629

  20. 水情自动测报系统的防雷与接地系统%Design of the Device against Thunder of Automatic Flood Detecting System

    Institute of Scientific and Technical Information of China (English)

    陆能年

    2011-01-01

    介绍了水情自动测报系统基站、中继站防止直接雷击的方法,中心站防止感应雷和反击雷的方法,以及接地装置的型式和埋设。%Introduced in this article are the methods to protect base station and relay station of automatic flood detecting system from lightning strikes,and the methods to protect central station from electric surge,harmonic wave,and lightning strikes,as well as the patterns of grounding connection and how to bury the line.

  1. Automatic Detection and Treatment of Broken Graphite Electrode in Electric Arc Furnace%电弧炉断电极分析检测与自动处理

    Institute of Scientific and Technical Information of China (English)

    刘仕良; 朱兴发; 唐琳

    2015-01-01

    针对电弧炉炼钢过程中出现的石墨电极折断问题,从不同角度多方面对其原因进行了分析,并提出了一种电极折断自动检测与处理的方法。%In view of the problem that the graphite electrode is broken in steelmaking process of electric arc furnace,the reason is analyzed from different angles,and a method for automatic detection and treatment of broken electrode is proposed.

  2. Automatic detection apparatus of maturity degree of viscose%粘胶熟成度的自动检测装置

    Institute of Scientific and Technical Information of China (English)

    高艳; 艾学忠; 白霞; 张连嘉

    2011-01-01

    The object of this paper is to study the detecting method of maturity degree of viscose during production of manufactured fibers. We have designed an automatic detecting apparatus for the maturity degree of viscose using C8051F410 system on chip as the core, along with the working principle and software flow. The constant speed control of the stirring motor during detection, control and detection of titration of NH4C1 liquor, measurement of stirring resistance, as well as judge of ending titration were studied. Compared with traditional test methods, the results showed that this automatic detecting apparatus for the maturity degree of viscoseexhibits high accuracy and good consistency, indicating profound significance for enhancing the level of automatic detection of maturity degree of viscose during production of manufactured fibers.%以化纤生产中粘胶熟成度的检测方法作为研究对象,以C8051F410片上系统为核心,设计了粘胶熟成度自动检测装置,就检测过程的搅拌电动机恒速控制、NH4CI溶液滴定量的控制与测量、搅拌阻力测量、滴定终点的判断进行了研究,介绍了设计的粘胶熟成度自动检测装置的工作原理,给出了装置工作的软件流程,并使用该装置针对同一批粘胶与传统测试方法进行了对比分析.结果表明,本文提出的粘胶熟成度自动检测方法精度高,结果一致性好,对提高化纤生产中粘胶熟成度检测手段的自动化程度意义深远.

  3. Detection of pneumoconiosis opacities on X-ray images by contour line processing and its application to automatic diagnosis

    International Nuclear Information System (INIS)

    This paper presents a study on automatic diagnosis of pneumoconiosis by X-ray image processing. Contour line processing method for identifying small opacities of pneumoconiosis is proposed and a new feature vector for classifying the profusion of small opacities is also proposed. This method is superior to the methods which are based on texture analysis because it is robust against variations of film quality and individual differences of structural patterns such as ribs and blood vessels. ILO standard films and 140 CR (computed radiography) images were used to test the performance of the proposed method. Experimental results show the effectiveness of the proposed method. (author)

  4. 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.

  5. Automatic inline defect detection for a thin film transistor–liquid crystal display array process using locally linear embedding and support vector data description

    International Nuclear Information System (INIS)

    Defect detection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacturing. This paper proposes an inline defect-detection (IDD) system, by which the defects can be automatically detected in a TFT array process. The IDD system is composed of three stages: the image preprocessing, the appearance-based classification and the decision-making stages. In the first stage, the pixels can be segmented from an input image based on the designed pixel segmentation method. The pixels are then sent into the appearance-based classification stage for defect and non-defect classification. Two novel methods are embedded in this stage: the locally linear embedding (LLE) and the support vector data description (SVDD). LLE is able to substantially reduce the dimensions of the input pixels by manifold learning and SVDD is able to effectively discriminate the normal pixels from the defective ones with a hypersphere by one-class classification. After aggregating the classification results, the third stage outputs the final detection result. Experimental results, carried out on real images provided by a LCD manufacturer, show that the IDD system can not only achieve a high defect-detection rate of over 98%, but also accomplish the task of inline defect detection within 4 s for one input image

  6. 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.

  7. 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.

  8. 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.

  9. Effect of endocervical specimen quality on detection of Chlamydia trachomatis and on the incidence of false-positive results with the Chlamydiazyme method.

    OpenAIRE

    Kellogg, J A; Seiple, J W; Murray, C L; Levisky, J S

    1990-01-01

    Duplicate endocervical swabs were collected from 1,675 patients to assess the effects of variations in specimen quality on Chlamydiazyme (Abbott Laboratories) detection of Chlamydia trachomatis and the incidence of false-positive results. One swab (at random) from each patient was tested for C. trachomatis antigen by using the standard Chlamydiazyme procedure. A 200-microliter volume of 0.9% saline was added to the other swab from each patient. After vortexing, 20 microliters was smeared on a...

  10. 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.

  11. 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.

  12. Label-Free and Real-Time Detection of Antigen-Antibody Capture Processes Using the Oblique-Incidence Reflectivity Difference Technique

    International Nuclear Information System (INIS)

    We successfully label-free and real-time detect the capture processes of human immunoglobulin G (IgG)/goat anti-human IgG and mouse IgG/goat anti-mouse IgG antigen-antibody pairs with different concentrations using the oblique-incidence reflectivity difference (OIRD) method, and obtain the interaction kinetics curves and the interaction times. The experimental results prove that the OIRD method is a promising technique for label-free and real-time detection of the biomolecular interaction processes and achieving the quantitative information of interaction kinetics. (general)

  13. Parallel detection and quantitative analysis of specific binding of proteins by oblique-incidence reflectivity difference technique in label-free format

    Institute of Scientific and Technical Information of China (English)

    DAI Jun; LI Lin; HE LiPing; RUAN KangCheng; LU HuiBin; JIN KuiJuan; YANG GuoZhen

    2014-01-01

    In this work,we parallelly detected the specific binding between microarray targets including 12 different kinds of proteins and the probe solution containing five corresponding antibodies and quantitatively analyzed the interactions between CDH13 and solution phase anti-CDH13 at six different probe concentrations by oblique-incidence reflectivity difference (OIRD) method in label-free format.The detection sensitivity reached 10 ng/mL.The experimental results indicate that the OIRD method is a promising and competing technique not only in research work but also in clinic.

  14. Label-Free and Real-Time Detection of Antigen-Antibody Capture Processes Using the Oblique-Incidence Reflectivity Difference Technique

    Science.gov (United States)

    He, Li-Ping; Dai, Jun; Sun, Yue; Wang, Jing-Yi; Lü, Hui-Bin; Wang, Shu-Fang; Jin, Kui-Juan; Zhou, Yue-Liang; Yang, Guo-Zhen

    2012-07-01

    We successfully label-free and real-time detect the capture processes of human immunoglobulin G (IgG)/goat anti-human IgG and mouse IgG/goat anti-mouse IgG antigen-antibody pairs with different concentrations using the oblique-incidence reflectivity difference (OIRD) method, and obtain the interaction kinetics curves and the interaction times. The experimental results prove that the OIRD method is a promising technique for label-free and real-time detection of the biomolecular interaction processes and achieving the quantitative information of interaction kinetics.

  15. Automatic Intensity-based 3D-to-2D Registration of CT Volume and Dual-energy Digital Radiography for the Detection of Cardiac Calcification

    Science.gov (United States)

    Chen, Xiang; Gilkeson, Robert; Fei, Baowei

    2013-01-01

    We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. CT is an established tool for the diagnosis of coronary artery diseases (CADs). Dual-energy digital radiography could be a cost-effective alternative for screening coronary artery calcification. In order to utilize CT as the “gold standard” to evaluate the ability of DR images for the detection and localization of calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, i.e. Gaussian-weighted projection, threshold-based projection, and average-based projection. We tested normalized cross correlation (NCC) and normalized mutual information (NMI) as similarity measurement. We used the Downhill Simplex method as the search strategy. Simulated projection images from CT were fused with the corresponding DR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with mean errors of less 0.8 mm and 0.2 degree for both NCC and NMI. The registration accuracy of the physical phantoms is 0.34 ± 0.27 mm. Color overlay and 3D visualization of the clinical data show that the two images are registered well. This is consistent with the improvement of the NMI values from 0.20 ± 0.03 to 0.25 ± 0.03 after registration. The automatic 3D-to-2D registration method is accurate and robust and may provide a useful tool to evaluate the dual-energy DR images for the detection of coronary artery calcification. PMID:24386527

  16. Diagnostic performance of a commercially available computer-aided diagnosis system for automatic detection of pulmonary nodules: comparison with single and double reading

    International Nuclear Information System (INIS)

    Objective: To assess the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system for automatic detection of pulmonary nodules with multi-row detector CT scans compared to single and double reading by radiologists. Materials and Methods: A CAD system for automatic nodule detection (Siemens LungCare NEV VB10) was applied to four-detector row low-dose CT (LDCT) performed on nine patients with pulmonary metastases and compared to the findings of three radiologists. A standard-dose CT (SDCT) was acquired simultaneously and used for establishing the reference data base. The study design was approved by the Institutional Review Board and the appropriate German authorities. The reference data base consisted of 457 nodules (mean size 3.9±3.1 mm) and was established by fusion of the sets of nodules detected by three radiologists independently reading LDCT and SDCT and by CAD. An independent radiologist used thin slices to eliminate false positive findings from the reference base. Results: An average sensitivity of 54% (range 51% to 55%) was observed for single reading by one radiologist. CAD demonstrated a similar sensitivity of 55%. Double reading by two radiologists increased the sensitivity to an average of 67% (range 67% to 68%). The difference to single reading was significant (p<0.001). CAD as second opinion after single reading increased the sensitivity to 79% (range 77% to 81%), which proved to be significantly better than double reading (p<0.001). CAD produced more false positive results (7.2%) than human readers but it was acceptable in clinical routine. (orig.)

  17. 漏泄电缆自动检测系统研究%Research on automatic detection system of leaky cable

    Institute of Scientific and Technical Information of China (English)

    杨海马; 于小强; 杨晖; ; 宋磊磊; 黄影平; 陆崚; 陈震宇

    2014-01-01

    In order to solve the problem of limitation and low efficiency in traditional leaky cable detection system , an automatic leaky cable detection system based on PSoC was introduced .First, based on requirements of detection system, PSoC was used to achieve data collection and motion control.Then, around hardware design and software process commenced the implementation of automatic test scheme .Finally, client PC software is used to complete remote automatic test.The experimental data show that communication rate can reach 2 Mbit/s through 2.4 GHz communication link, and bit error rate less than 2%.The deviation of instance accuracy is below 0.5%.These vi-tal characters basically meet requirements of system, such as stable, efficient and precision.%为了解决传统漏泄电缆检测系统的应用局限性及低效性,设计了基于片上可编程系统的漏泄电缆自动检测系统。根据漏泄电缆检测的功能需求,该系统利用PSoC4实现运动控制与数据采集,通过双无线射频芯片完成磁区通信。然后,利用硬件对测距脉冲计数弥补软件的不足。最后,通过上位机软件实现漏泄电缆远程自动检测流程。实验数据表明,2.4 GHz通信频段下,通信速率达到2 Mbit/s,通信误码率小于2%,并且150 m内系统测距误差小于0.5%。满足漏泄电缆自动检测系统的低成本、高效及精度高等要求。

  18. Investigation of photon detection probability dependence of SPADnet-I digital photon counter as a function of angle of incidence, wavelength and polarization

    Energy Technology Data Exchange (ETDEWEB)

    Játékos, Balázs, E-mail: jatekosb@eik.bme.hu; Ujhelyi, Ferenc; Lőrincz, Emőke; Erdei, Gábor

    2015-01-01

    SPADnet-I is a prototype, fully digital, high spatial and temporal resolution silicon photon counter, based on standard CMOS imaging technology, developed by the SPADnet consortium. Being a novel device, the exact dependence of photon detection probability (PDP) of SPADnet-I was not known as a function of angle of incidence, wavelength and polarization of the incident light. Our targeted application area of this sensor is next generation PET detector modules, where they will be used along with LYSO:Ce scintillators. Hence, we performed an extended investigation of PDP in a wide range of angle of incidence (0° to 80°), concentrating onto a 60 nm broad wavelength interval around the characteristic emission peak (λ=420 nm) of the scintillator. In the case where the sensor was optically coupled to a scintillator, our experiments showed a notable dependence of PDP on angle, polarization and wavelength. The sensor has an average PDP of approximately 30% from 0° to 60° angle of incidence, where it starts to drop rapidly. The PDP turned out not to be polarization dependent below 30°. If the sensor is used without a scintillator (i.e. the light source is in air), the polarization dependence is much less expressed, it begins only from 50°.

  19. Study on automatic detection for magnetic particle indications using image processing; Gazo shori ni yoru jifun tansho no jidoka ni kansuru ichikokoromi

    Energy Technology Data Exchange (ETDEWEB)

    Sekine, K.; Suzuki, S.; Iwai, O. [Yokohama National Univ. (Japan). Faculty of Engineering

    1997-07-25

    Magnetic particle indications have a good detection ability to fine surface flaws in high magnetic materials mainly for iron and steel materials, and widely used in the quality inspection of semi-finished steel products and the maintenance inspection of welds due to their simplicity. In this study, concerning the experiment of the magnetic particle indications as an objective of cracks in steel welds including seam cracks of steel billets, the extraction of the flaw indications and the evaluation of its length were attempted by the application of a common personal computer and simple image processing technique. Moreover, the basic investigation on the evaluation of crack depth was conducted, the possibility of the automatic detection of cracks in welds during the maintenance inspection was examined. In the image processing system to detect crack indications, a reflecting light was produced by irradiating an ultra-violet light in the detecting part using two sets of black lights and it was taken by image processing equipment with a lens and camera system, and thus image data were obtained. 9 refs., 11 figs.

  20. 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.