WorldWideScience

Sample records for automatic incident detection

  1. Wireless Vehicular Communications for Automatic Incident Detection and Recovery

    OpenAIRE

    Joaquim José Castro Ferreira; José Alberto Gouveia Fonseca; Lopes, Jorge Alves

    2012-01-01

    Incident detection is the process by which an incident is brought to the attention of traffic operators in order to design and activate a response plan. To minimize the detection time is crucial to mitigate the incident severity for victims as well to reduce the risk of secondary crashes. Automated incident information dissemination and traffic conditions is useful to alert in-route drivers to decide alternative routes on unexpected traffic congestion and may be also used for the incident rec...

  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. Automatic Incident Classification for Big Traffic Data by Adaptive Boosting SVM

    OpenAIRE

    Wang, Li-li; Ngan, Henry Y. T.; Yung, Nelson H. C.

    2015-01-01

    Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however cr...

  6. Automatic detection of apnoea of prematurity

    International Nuclear Information System (INIS)

    The detection of the incidents of apnoea of prematurity (AP) in preterm infants is important in the intensive care unit, but this detection is often based on simple threshold techniques, which suffer from poor specificity. Three methods for the automatic detection of AP were designed, tested and evaluated using approximately 2426 h of continuous recording from 54 neonates (μ = 44 h and σ = 7 h). The first method was based on the cumulative sum of the time series of heart rate (HR), respiratory rate (RR) and oxygen saturation (SpO2) along with the sum of their Shannon entropy. The performance of this method gave 94.53% sensitivity, 74.72% specificity and 77.84% accuracy. The second method was based on the correlation between the time series of HR, RR and SpO2, which were used as inputs to an artificial neural network. This gave 81.85% sensitivity, 75.83% specificity and 76.78% accuracy. The third method utilized the derivative of the three time series and yielded a performance of 100% sensitivity, 96.19% specificity and 96.79% accuracy. Although not optimized to work in real time, the latter method has the potential for forming the basis of a real time system for the detection of incidents of AP

  7. 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.

  8. Detecting Terrorism Incidence Type from News Summary

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah

    2012-01-01

    The paper presents the experiments to detect terrorism incidence type from news summary data. We have applied classification techniques on news summary data to analyze the incidence and detect the type of incidence. A number of experiments are conducted using various classification algorithms and...

  9. 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. 

  10. 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...

  11. 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.

  12. 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.

  13. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg; Remvig, Line Sofie; Thomsen, Carsten Eckhart; Sørensen, Helge Bjarup Dissing

    2012-01-01

    Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...... of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure...... recorded directly on the epileptic focus. Conclusions: Based on our dataset, automatic seizure detection can be done using only three EEG channels without loss of performance. These channels should be selected based on maximum variance and not, as often done, using the focal channels. Significance: With...

  14. 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...

  15. Automatic detection of abnormalities in mammograms

    OpenAIRE

    Suhail, Zobia; Sarwar, Mansoor; Murtaza, Kashif

    2015-01-01

    Background In recent years, an increased interest has been seen in the area of medical image processing and, as a consequence, Computer Aided Diagnostic (CAD) systems. The basic purpose of CAD systems is to assist doctors in the process of diagnosis. CAD systems, however, are quite expensive, especially, in most of the developing countries. Our focus is on developing a low-cost CAD system. Today, most of the CAD systems regarding mammogram classification target automatic detection of calcific...

  16. Dynamic detection of nuclear reactor core incident

    International Nuclear Information System (INIS)

    Surveillance, safety and security of evolving systems area challenge to prevent accident. The dynamic detection of a hypothetical and theoretical blockage incident in the Phenix nuclear reactor is investigated. Such an incident is characterized by abnormal temperature rises in the neighbourhood of the concerned reactor core assembly. The data set is the output temperature map of the reactor, it is provided by the Atomic Energy and Alternative Energies Commission (CEA). A real time approach is proposed, based on a sliding temporal window, it is divided into two steps. The first one behaves like a sieve, its function is to detect simultaneous temperature evolutions in a close neighbourhood which may induce a potential incident. When such evolutions are detected, the second step computes the temperature contrast between each assembly having these evolutions and its neighbourhood. This method permits to monitor the system evolution in real time while only few observations are required. Results are validated on various noisy realistic simulated perturbations. (authors)

  17. Automatic Detection of Dominance and Expected Interest

    Directory of Open Access Journals (Sweden)

    M. Teresa Anguera

    2010-01-01

    Full Text Available Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.

  18. 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.

  19. 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...

  20. Towards fully automatic object detection and segmentation

    Science.gov (United States)

    Schramm, Hauke; Ecabert, Olivier; Peters, Jochen; Philomin, Vasanth; Weese, Juergen

    2006-03-01

    An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary for achieving a high level of automation in many medical applications. Since today's segmentation techniques typically rely on user input for initialization, they do not allow for a fully automatic workflow. In this work, the generalized Hough transform is used for detecting anatomical objects with well defined shape in 3-D medical images. This well-known technique has frequently been used for object detection in 2-D images and is known to be robust and reliable. However, its computational and memory requirements are generally huge, especially in case of considering 3-D images and various free transformation parameters. Our approach limits the complexity of the generalized Hough transform to a reasonable amount by (1) using object prior knowledge during the preprocessing in order to suppress unlikely regions in the image, (2) restricting the flexibility of the applied transformation to only scaling and translation, and (3) using a simple shape model which does not cover any inter-individual shape variability. Despite these limitations, the approach is demonstrated to allow for a coarse 3-D delineation of the femur, vertebra and heart in a number of experiments. Additionally it is shown that the quality of the object localization is in nearly all cases sufficient to initialize a successful segmentation using shape constrained deformable models.

  1. Automatic Detection of Omissions in Translations

    CERN Document Server

    Melamed, I D

    1996-01-01

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

  2. 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.

  3. Improvement and development of automatic detection techniques

    International Nuclear Information System (INIS)

    For detection of radiation-induced mutation, establishment of a new sample preparation method and its procedures suitable for its automation is thought to be the key step to improve the detection efficacy and save labor. In this study, an investigation was made on the sensitivity to radiation exposure in respect of the occurrence of chromosomal breakage by high precision chromosome coloring method utilizing FISH. The number of chromosome breakage per cell was determined in chromosome 1, 4, 5, 9, 11 and 13 prepared from an identical sample exposed to three different grays. The breakage number was found to increase linearly as an increase in the amount of chromosomal DNA and hotspots of the radiation-induced breakages tended to concentrate in R band and the position of R band was almost coincident with the sites of chromosomal translocation breakages specific to leukemia, showing a correlation of radiation exposure to leukemia. Chromosome 13, 14 and 15, which were different in band pattern but similar in its length taken from cells exposed to X-ray at 5 Gy were investigated in detail and it was found that the sensitivity of chromosome to radiation was depending on the quantity and the quality of R band in each chromosome. The benefits of this chromosome coloring method for the analysis of chromosome breakage were as follows: when compared with the conventional dicentric method, the kinds of chromosomal abnormalities to be detectable were much more and its detection rate as well as accuracy was higher. In addition, the time required for determination was loess than one tenth of the conventional one. A breakage site was detectable with differences in color tone and thus, any special technique was not necessary. Therefore, the chromosome coloring method by FISH was demonstrated to be much suitable for automatic image analysis by computer. (M.N.)

  4. 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

  5. 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.

  6. 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.

  7. Automatic video surveillance of outdoor scenes using track before detect

    DEFF Research Database (Denmark)

    Hansen, Morten; Sørensen, Helge Bjarup Dissing; Birkemark, Christian M.; Stage, Bjarne

    This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due to...

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

    OpenAIRE

    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 certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). T...

  9. Real-time Detection of Road Traffic Incidents

    OpenAIRE

    Škorput, Pero; Mandžuka, Sadko; Jelušić, Niko

    2010-01-01

    The paper analyses the real-time detection of incidents in road traffic. A general model is presented of an integral road traffic incident management system. The paper presents the major incident detection methods. The detection procedure on open highway sections has been dealt with in particular. Adequate mathematical model has been defined, as the base for the realisation of the estimators of the traffic flow condition variables. The proposed method is the Extended Kalman Filter. The final ...

  10. 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

  11. Feature detection with automatic scale selection

    OpenAIRE

    Lindeberg, Tony

    1998-01-01

    The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. Whereas scale-space representation provides a well-founded framework for dealing with this issue by representing image structures at different scales, traditional scale-space theory does not address the problem of how t...

  12. Automatic Detection of Wild-type Mouse Cranial Sutures

    DEFF Research Database (Denmark)

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

    , automatic detection of the cranial sutures becomes important. We have previously built a craniofacial, wild-type mouse atlas from a set of 10 Micro CT scans using a B-spline-based nonrigid registration method by Rueckert et al. Subsequently, all volumes were registered nonrigidly to the atlas. Using these...... 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...

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

    OpenAIRE

    Jose María Armingol; Arturo de la Escalera

    2010-01-01

    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 ana...

  14. 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...

  15. Automatic Detection of Purkinje Images' Horizontal Coordinates

    Czech Academy of Sciences Publication Activity Database

    Přečková, Petra

    2003-01-01

    Roč. 34, č. 2 (2003), s. 47-59. ISSN 0301-5491 Institutional research plan: AV0Z1030915 Keywords : optometry * videometry * videooculography * purkinje image * position detection Subject RIV: BB - Applied Statistics, Operational Research

  16. 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...

  17. 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…

  18. 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)

  19. Development of optical automatic positioning and wafer defect detection system

    Science.gov (United States)

    Tien, Chuen-Lin; Lai, Qun-Huang; Lin, Chern-Sheng

    2016-02-01

    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.

  20. Automatic flaw detecting device for reactor pressure vessel

    International Nuclear Information System (INIS)

    The device of the present invention is used for detecting flaws in welded portions of a pressure vessel while running along a track formed at the circumference of the pressure vessel, by which cables for transmitting collected data to a detection chamber can be laid around easily. Namely, a power supply conductor for supplying electric power to the device and a leakage coaxial cable as a receiving antenna are disposed to the tracks along the longitudinal direction. A connection terminal is disposed to the automatic flaw detecting device to be in contact with the power supply conductor. In addition, an oscillator is disposed for transmitting signals collected by a supersonic probe to the leakage coaxial cable by way of a transmission antenna. Then, since cables drawn from the automatic flaw detecting device are used only for transporting air and water to the flaw detection portion, the cables can be laid easily to improve the operationability. (I.S.)

  1. 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

  2. Automatic refinement of parallel applications structure detection

    OpenAIRE

    González, Juan,; Huck, Kevin; Giménez Lucas, Judit; Labarta Mancho, Jesús José

    2012-01-01

    Analyzing parallel programs has become increasingly difficult due to the immense amount of information collected on large systems. In this scenario, cluster analysis has been proved to be a useful technique to reduce the amount of data to analyze. A good example is the use of the density-based cluster algorithm DBSCAN to identify similar single program multiple data (SPMD) computing phases in message-passing applications. This structure detection simplifies the analyst wo...

  3. 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.

  4. Automatic detection of microcalcifications in mammography using a neuromimetic system based on retina.

    Science.gov (United States)

    Vibert, Jean-François; Valleron, Alain-jacques

    2003-01-01

    The incidence of breast cancer in France is roughly 26,000 and the annual number of deaths is 11,000. The mammography is the choice examination for the early identification of the tumours in an asymptomatic population. This is a simple, reliable, inexpensive examination, allowing to identify a grave and frequent pathology, but that can be the object of an effective treatment if early detected. The recognition of the microcalcifications in the mammographies is the key for early detection of cancers. Automatic detection methods were already proposed, but they have a very weak specificity and a relatively low sensibility. Currently, the eye of the expert still remains the better judge. We propose a neuromimetic method to localize automatically the microcalcifications. In this method, we devise a network of formal neurones inspired from the mammal retina architecture. This model mimics one characteristic of the retina which is is a sensor that automatically adapts to the image characteristics to analyse and realize the outlines extraction and adaptative filtering of the pictures, based on its network properties. The results were tested using a public standardized data set (DDSM), which was designed to test the automatic detection methods. We show that our "retina" can extracts most of the microcalcifications that can be grouped together in clusters. While we achieve a 95% sensitivity, we must acknowledge a low specificity (22%). Current efforts will focus to enhance this latter parameter. PMID:14664051

  5. 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.

  6. 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.

  7. 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.

  8. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

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

  9. @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.

  10. Method for automatic detection of wheezing in lung sounds

    Directory of Open Access Journals (Sweden)

    R.J. Riella

    2009-07-01

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

  11. 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.

  12. Semi-automatic organelle detection on transmission electron microscopic images.

    Science.gov (United States)

    Higaki, Takumi; Kutsuna, Natsumaro; Akita, Kae; Sato, Mayuko; Sawaki, Fumie; Kobayashi, Megumi; Nagata, Noriko; Toyooka, Kiminori; Hasezawa, Seiichiro

    2015-01-01

    Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with procedures to highlight and enlarge regions of interest on the image, we have developed a successful method for the semi-automatic detection of plant organelles including mitochondria, amyloplasts, chloroplasts, etioplasts, and Golgi stacks in transmission electron microscope images. Our proposed semi-automatic detection system will be helpful for labelling organelles in the interpretation and/or quantitative analysis of large-scale electron microscope imaging data. PMID:25589024

  13. 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.

  14. Detecting Pandemic and Endemic Incidents through Network Telescopes: Security Analysis

    OpenAIRE

    Gagadis, Fotis

    2009-01-01

    Moore et al., from the Cooperative Association for Internet Data Analysis (CAIDA), proposed in recent years another measurement and monitoring method for networks and the Internet. Network Telescopes are used to detect malicious traffc events generated from Denial of Service attacks, worm infected hosts and misconfiguration. This report is focused on endemic and pandemic incidents (DoS, Worm) and how these incidents observed through different Darknet topologies and sta...

  15. 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.

  16. Intelligent automatic overtaking system using vision for vehicle detection

    OpenAIRE

    Milanés Montero, Vicente; Fernández Llorca, David; Villagra Serrano, Jorge; Pérez, Joshué; Fernández López, Carlos; Parra Alonso, Ignacio; González Fernández-Vallejo, Carlos; Sotelo, Miguel Ángel

    2012-01-01

    There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomo...

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

    OpenAIRE

    Frimmel, Hans; Egevad, Lars; Busch, Christer; Bengtsson, Ewert

    2001-01-01

    Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this ...

  18. 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.

  19. 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.

  20. 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...... which 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...... 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...

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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...

  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. 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.

  10. Highway Incident Detection Research Based on Lipschitz Exponent Algorithm

    Directory of Open Access Journals (Sweden)

    Dan Yu

    2011-06-01

    Full Text Available The characteristic of traffic flow on highway is analyzed in this paper firstly. According to Catastrophe Theory, the function of occupancy time and volume is established. Then a highway traffic incident detection method based on Lipschitz exponent is advanced by using Wavelet Theory, whose core is to judge the singular points of traffic flow accurately. When the value of Lipschitz exponent is near to “one”, the singularity character of traffic flow function is in the pink and the probability of traffic incident is more. Take the data of Changchang-highway as an example, the superiority of the new method advanced is testified. The result shows that the false alarm rate is only 2.1% and the delay time is 12mins, when the detection rate is 95%.

  11. 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.

  12. Automatic Detection of Magnetic delta in Sunspot Groups

    CERN Document Server

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

    2015-01-01

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

  13. BgCut: automatic ship detection from UAV images.

    Science.gov (United States)

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

    2014-01-01

    Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches. PMID:24977182

  14. 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.

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

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    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...... measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability...

  16. 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

  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. An automatic detection software for differential reflection spectroscopy

    Science.gov (United States)

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

    2012-06-01

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

  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. 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

  1. 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%.

  2. 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

  3. 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 ...

  4. Automatic age-related macular degeneration detection and staging

    Science.gov (United States)

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

    2013-03-01

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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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; Andersen, Gunnar; Sorensen, Helge B.D.

    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 ...

  15. 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.

  16. 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...

  17. CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets

    OpenAIRE

    Saundra Manning; Lior Shamir

    2014-01-01

    The recent advancements in automated microscopy and information systems allow the acquisition and storage of massive datasets of microscopy images. Here we describe CHLOE, a software tool for automatic extraction of novelty in microscopy image datasets. The tool is based on a comprehensive set of numerical image content descriptors reflecting image morphology, and can be used in combination with ROI detection and segmentation tools such as ITK. The rich feature set allows automatic detection ...

  18. 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.

  19. 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.

  20. Automatic intraocular lens segmentation and detection in optical coherence tomography images.

    Science.gov (United States)

    Gillner, Melanie; Eppig, Timo; Langenbucher, Achim

    2014-05-01

    We present a new algorithm for automatic segmentation and detection of an accommodative intraocular lens implanted in a biomechanical eye model. We extracted lens curvature and position. The algorithm contains denoising and fan correction by a multi-level calibration routine. The segmentation is realized by an adapted canny edge detection algorithm followed by a detection of lens surface with an automatic region of interest search to suppress non-optical surfaces like the lens haptic. The optical distortion of lens back surface is corrected by inverse raytracing. Lens geometry was extracted by a spherical fit. We implemented and demonstrated a powerful algorithm for automatic segmentation, detection and surface analysis of intraocular lenses in vitro. The achieved accuracy is within the expected range determined by previous studies. Future improvements will include the transfer to clinical anterior segment OCT devices. PMID:23928353

  1. 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

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

  2. 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.

  3. Automatic welding detection by an intelligent tool pipe inspection

    Science.gov (United States)

    Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

    2015-07-01

    This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

  4. 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.

  5. 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 ...

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

    International Nuclear Information System (INIS)

    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

  7. 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

  8. Intelligent Fatigue Detection and Automatic Vehicle Control System

    OpenAIRE

    Monali Gulhane; P.S.Mohod

    2014-01-01

    This paper describes method for detecting the early signs of fatigue in train drivers. As soon as the train driver is falling in symptoms of fatigue immediate message will be transfer to the control room indicating the status of the drivers. In addition of the advance technology of heart rate sensors is also added in the system for correct detection of status of driver if in either case driver is falling to fatigue due to any sever medical problems .The fatigue is detected in the system by th...

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

    Directory of Open Access Journals (Sweden)

    Tongran eLiu

    2016-03-01

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

  10. Automatic 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

  11. 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.

  12. A Generative Statistical Algorithm for Automatic Detection of Complex Postures.

    Science.gov (United States)

    Nagy, Stanislav; Goessling, Marc; Amit, Yali; Biron, David

    2015-10-01

    This paper presents a method for automated detection of complex (non-self-avoiding) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects. Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers, where data from traditional trackers is limited. We restrict the input available to the algorithm to a single digitized frame, such that manual initialization is not required and the detection problem becomes embarrassingly parallel. Consequently, the proposed algorithm does not propagate detection errors and naturally integrates in a "big data" workflow used for large-scale analyses. Using this framework, we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes. Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals (e.g., for mating assays) or different species. PMID:26439258

  13. Automatic failure detection of serial products using novelty filter

    OpenAIRE

    Márcia Helena Veleda Moita; Juliana Ferreguette Sena; Ely Sena de Almeida; Rafael Postal

    2013-01-01

    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 ...

  14. 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.

  15. Automatic detection of temporal gait parameters in poststroke individuals.

    Science.gov (United States)

    Lopez-Meyer, Paulo; Fulk, George D; Sazonov, Edward S

    2011-07-01

    Approximately one-third of people who recover from a stroke require some form of assistance to walk. Repetitive task-oriented rehabilitation interventions have been shown to improve motor control and function in people with stroke. Our long-term goal is to design and test an intensive task-oriented intervention that will utilize the two primary components of constrained-induced movement therapy: massed, task-oriented training and behavioral methods to increase use of the affected limb in the real world. The technological component of the intervention is based on a wearable footwear-based sensor system that monitors relative activity levels, functional utilization, and gait parameters of affected and unaffected lower extremities. The purpose of this study is to describe a methodology to automatically identify temporal gait parameters of poststroke individuals to be used in assessment of functional utilization of the affected lower extremity as a part of behavior enhancing feedback. An algorithm accounting for intersubject variability is capable of achieving estimation error in the range of 2.6-18.6% producing comparable results for healthy and poststroke subjects. The proposed methodology is based on inexpensive and user-friendly technology that will enable research and clinical applications for rehabilitation of people who have experienced a stroke. PMID:21317087

  16. 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.

  17. Plagiarism meets paraphrasing: insights for the new generation in automatic plagiarism detection

    OpenAIRE

    Barrón-Cedeño, Alberto; Vila Rigat, Marta; Martí Antonin, M. Antònia; Rosso, Paolo

    2013-01-01

    Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyse the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagia...

  18. Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection

    OpenAIRE

    Barrón-Cedeño, Alberto; Vila, Marta; Martí, Maria Antonia; Rosso, Paolo

    2013-01-01

    Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagia...

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

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  20. CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets

    Directory of Open Access Journals (Sweden)

    Saundra Manning

    2014-09-01

    Full Text Available The recent advancements in automated microscopy and information systems allow the acquisition and storage of massive datasets of microscopy images. Here we describe CHLOE, a software tool for automatic extraction of novelty in microscopy image datasets. The tool is based on a comprehensive set of numerical image content descriptors reflecting image morphology, and can be used in combination with ROI detection and segmentation tools such as ITK. The rich feature set allows automatic detection of repetitive outlier images that are visually different from the common images in the dataset. The code and software are publicly available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/chloe.

  1. INTELLIGENT FATIGUE DETECTION AND AUTOMATIC VEHICLE CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    Monali Gulhane

    2014-10-01

    Full Text Available This paper describes method for detecting the early signs of fatigue in train drivers. As soon as the train driver is falling in symptoms of fatigue immediate message will be transfer to the control room indicating the status of the drivers. In addition of the advance technology of heart rate sensors is also added in the system for correct detection of status of driver if in either case driver is falling to fatigue due to any sever medical problems .The fatigue is detected in the system by the image processing method of comparing the image(frames in the video and by using the human features we are able to estimate the indirect way of detecting fatigue. The technique also focuses on modes of person when driving the train i.e. awake, drowsy state or sleepy and sleep state. The system is very efficient to detect the fatigue and control the train also train can be controlled if it cross any such signal by which the train may collide on another train

  2. 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.

  3. 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.

  4. 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.

  5. 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)

  6. 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.

  7. 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.

  8. Improvement and development of techniques for automatic detection of mutation

    International Nuclear Information System (INIS)

    This study was made as a part of ''Developments of techniques for detection and analysis of radiation-induced mutation using new methods for DNA analysis'', 5-year research project started from the fiscal year 1994. The availability of a new detection method using FISH for radiation-induced chromosome aberrations was examined using lymphocytes. Chromosome painting was performed with DNA probes purified from human chromosomes. Thus, estimation of radiated dose was made based on the rate of cleavage in the target chromosome. This method was most effective for analysis of structural changes in chromosome caused by radiation and this is expected to be as a rapid and labor-saving procedure. The present results indicate that this method has a high detectability and a high accuracy. The duration required for microscopic observation was less than one tenth in this method compared with the conventional method. The cleavage frequency was proportional to the chromosome length. The distribution of the cleavages was well coincident with the positions of chromosome translocations specific to leukemia. These results suggest that this detection method for chromosomal cleavages is very useful and convenient for the estimation of radiation dose. (M.N.)

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

    Science.gov (United States)

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

    2015-05-01

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

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. Automatic Detection and Correction for Glossy Reflections in Digital Photograph

    Directory of Open Access Journals (Sweden)

    Rong-Chi Chang

    2011-04-01

    Full Text Available The popularization of digital technology has made shooting digital photos and using related applications a part of daily life. However, the use of flash, to compensate for low atmospheric lighting, often leads to overexposure or glossy reflections. This study proposes an auto-detection and inpainting technique to correct overexposed faces in digital photography. This algorithm segments the skin color in the photo as well as uses face detection and capturing to determine candidate bright spots on the face. Based on the statistical analysis of color brightness and filtering, the bright spots are identified. Finally, bright spots are corrected through inpainting technology. From the experimental results, this study demonstrates the high accuracy and efficiency of the method.

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2015-10-01

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

  18. 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

  19. 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 ...

  20. Automatic detection of brain tumors in MR images

    Czech Academy of Sciences Publication Activity Database

    Dvořák, P.; Kropatsch, W.G.; Bartušek, Karel

    Brno: University of technolgy, 2013, s. 577-580. ISBN 978-1-4799-0404-4. [International conference on telecommunications and signal processing /36./. Rome (IT), 02.07.2013-04.07.2013] R&D Projects: GA ČR GAP102/12/1104; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : brain symmetry * brain tumor * magnetic resonance * tumor detection Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  1. Semi-automatic organelle detection on transmission electron microscopic images

    OpenAIRE

    Takumi Higaki; Natsumaro Kutsuna; Kae Akita; Mayuko Sato; Fumie Sawaki; Megumi Kobayashi; Noriko Nagata; Kiminori Toyooka; Seiichiro Hasezawa

    2015-01-01

    Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with pr...

  2. 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...

  3. 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.

  4. 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...

  5. 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.

  6. Alcohol Detection and Automatic Drunken Drive Avoiding System

    Directory of Open Access Journals (Sweden)

    Prof. P. H. Kulkarni

    2014-04-01

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

  7. 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

  8. 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)

  9. 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.

  10. Automatic ultrasonic image analysis method for defect detection

    International Nuclear Information System (INIS)

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

  11. 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.

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

    Science.gov (United States)

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

    2013-01-01

    Recognizing the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognize the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesized candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that (a) these two easily recognizable actions are sufficient for recognizing the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and (b) the use of reaction time experiments using natural materials is feasible and provides ecologically valid results. PMID:24009594

  13. 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.

  14. 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

  15. 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

  16. Automatic tuning of MST segmentation of mammograms for registration and mass detection algorithms

    OpenAIRE

    Mariusz Bajger; Fei Ma; Bottema, Murk J.

    2009-01-01

    A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms.

  17. 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...

  18. Automatic detection and spatial clustering of interictal discharges in invasive recordings

    Czech Academy of Sciences Publication Activity Database

    Janča, R.; Ježdík, P.; Čmejla, R.; Kršek, P.; Jefferys, J. G. R.; Marusič, P.; Jiruška, Přemysl

    Ottawa: IEEE Instrumentation & Measurement Society IEEE Ottawa Section, 2013, s. 219-223. ISBN 978-1-4673-5195-9. [IEEE International Symposium on Medical Measurements and Applications /8./. Gatineau (CA), 04.05.2013-05.05.2013] Institutional support: RVO:67985823 Keywords : epilepsy * interictal discharges * intracranial electroencephalography * automatic detection * clustering Subject RIV: FH - Neurology

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Detection and incidence of Pernospora variabilis in quinoa seeds

    OpenAIRE

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

    2012-01-01

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

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. On the automatic detection of otolith features for fish species identification and their age estimation

    OpenAIRE

    Sória Pérez, José A. (José Antonio)

    2013-01-01

    This thesis deals with the automatic detection of features in signals, either extracted from photographs or captured by means of electronic sensors, and its possible application in the detection of morphological structures in fish otoliths so as to identify species and estimate their age at death. From a more biological perspective, otoliths, which are calcified structures located in the auditory system of all teleostean fish, constitute one of the main elements employed in the study and mana...

  10. Development of monitoring and automatic fault detection solutions for grid-connected photovoltaic systems

    OpenAIRE

    Capogna, Vicenzo

    2012-01-01

    In this Final Thesis work, the development of a new monitoring and automatic fault detection system for grid-connected photovoltaic systems is presented and described in its details. This product has been developed in JavaScript and HTLM protocols and it allow real time an online performance monitoring and comparison together with fault detection and causes diagnosis. The presented solution is focus on the DC side of the PV system and it includes: a simple and effective simulat...

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    N. Khalili Moghadam

    2015-12-01

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

  13. 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.

  14. 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.

  15. Automatic detection of breast lesions with MIBI-Tc99m scintimammography using a novelty filter

    International Nuclear Information System (INIS)

    An automatic method for detecting breast lesion in scintimammography is described. It is reported that the proposed method not only detects lesions but also classifies them as benign or malignant. The detection method makes use of Kohonen's novelty filter and the classification method is obtained by the analysis of an identified lesion mean profile. The method was able to detect all lesions presented in the scintimammogram and to correctly classify 16 out of 17 malignant lesions and 15 out of 17 benign lesions. The sensitivity of the method was 94,12% and specificity was 88,24%

  16. 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.

  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. Visual mismatch negativity reveals automatic detection of sequential regularity violation

    Directory of Open Access Journals (Sweden)

    Gábor Stefanics

    2011-05-01

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

  19. 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%.

  20. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg; Conradsen, Isa; Kjær, Troels Wesenberg; Thomsen, Carsten Eckhart; Sørensen, Helge Bjarup Dissing

    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 of...... ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

  1. 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.

  2. 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.

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

    Science.gov (United States)

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

    2012-03-01

    In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

  4. 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.

  5. 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.

  6. 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.

  7. The development of automatic detection monitoring system for thermal failure part by infrared thermal vision camera

    International Nuclear Information System (INIS)

    The most part of various electric has been affected by thermal failure due to electric overload. Contact-sensor has been used, for detection to this thermal failure, until now. But, it is impossible to detect the unsuitable element by using contact-temperature-sensor. This problem, with development of the infrared thermal vision camera, will be solved. Because it take some advantages which are composed of non-contact detect and non-destructive detect for temperature distribution, it is possible to detect on the temperature of revolution part, high temperature part. We developed the automatic detection monitoring system for thermal failure part on electric with overload by using the infrared thermal vision camera. The first stage, thermal signal was detected from the infrared thermal vision camera, and then the data that was wanted from user was shown. The second stage, if the temperature that was decided to failure coded to the program, automatically electric was shut off, This monitoring system is possible to apply on various conveniences in the whole industrial sites.

  8. 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.

  9. 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

  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. 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 ...

  12. Automatic Detection of Dining Plates for Image-Based Dietary Evaluation

    OpenAIRE

    Nie, Jie; Wei, Zhiqiang; Jia, Wenyan; Li, Lu; Fernstrom, John D.; Sclabassi, Robert J.; Sun, Mingui

    2010-01-01

    An automatic detector that finds circular dining plates in chronically recorded images or videos is reported for the study of food intake and obesity. We first detect edges from input images. After a number of processing steps that convert edges into curves, arc filtering and grouping algorithms are applied. Then, convex hulls are identified and the ones that fit the description of ellipses corresponding to dining plates are determined. Our experiments using real-world images indicate that th...

  13. 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...

  14. Techniques for large-scale automatic detection of web site defacements.

    OpenAIRE

    Medvet, Eric

    2008-01-01

    Web site defacement, the process of introducing unauthorized modifications to a web site, is a very common form of attack. This thesis describes the design and experimental evaluation of a framework that may constitute the basis for a defacement detection service capable of monitoring thousands of remote web sites sistematically and automatically. With this framework an organization may join the service by simply providing the URL of the resource to be monitored along with the contact poin...

  15. 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...

  16. 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...

  17. 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...

  18. Automatic Feature Extraction, Categorization and Detection of Malicious Code in Android Applications

    OpenAIRE

    Muhammad Zuhair Qadir; Atif Nisar Jilani; Hassam Ullah Sheikh

    2014-01-01

    Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big concern. As the number of new Android applications tends to be rapidly increased in the near future, there is a need for automatic malware detection quickly and efficiently. In this paper, we define a simple static analysis approach to first extract the features of the android application based on intents and categori...

  19. 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...

  20. 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.

  1. 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.

  2. 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

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

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  4. 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%.

  5. Influence on ultrasonic incident angle and defect detection sensitivity by cast stainless steel structure

    International Nuclear Information System (INIS)

    It is well known that ultrasonic waves are affected strongly by macro-structures in cast stainless steel, as in the primary pipe or other components in pressurized water reactors (PWRs). In this work, ultrasonic refractive angles and defect detection sensitivities are investigated at different incident angles to cast stainless steel. The aims of the investigation are to clarify the transmission of ultrasonic waves in cast stainless steel and to contribute to the transducer design. The results are that ultrasonic refractive angles in cast stainless steel shift towards the 45-degree direction with respect to the direction of dendritic structures by 11.8 degrees at the maximum and that the sensitivity of transducer for inner surface breaking cracks increases with decreasing incident angle. However, in an ultrasonic inspection of actual welds at smaller incident angles, a trade-off occurs between increased defect detection sensitivity and decreased defect discrimination capability due to intense false signals produced by non-defective features. (orig.)

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

    International Nuclear Information System (INIS)

    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.)

  7. 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.)

  8. 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.

  9. 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.

  10. 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)

  11. 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.

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

    International Nuclear Information System (INIS)

    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. (paper)

  13. 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.

  14. 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.

  15. Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

    OpenAIRE

    Pastell, Matti

    2007-01-01

    The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feedi...

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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)

  5. From Automatic Sign Detection To Space Usage Rules Mining For Autonomous Driving

    OpenAIRE

    SAMSONOV, Pavel; Hecht, Brent; SCHOENING, Johannes

    2015-01-01

    While there is a large body of related work on the automatic detecting of road signs for (semi-)autonomous vehicles, we believe that these vehicles should also be aware of so-called “space usage rules” (SURs) more generally. Vehicles that understand SURs – e.g. “no swimming”, “no drone flying”, - could provide a novel set of context-aware services for autonomous driving. For instance, an autonomous car navigation system could provide directions to the nearest beach, where swimming is al...

  6. Automatic detection of hidden dimensions to obtain appropriate reaction coordinates in the Outlier FLOODing (OFLOOD) method

    Science.gov (United States)

    Harada, Ryuhei; Nakamura, Tomotake; Shigeta, Yasuteru

    2015-10-01

    As a strategy for reproducing rare, biologically important events, we previously developed the Outlier FLOODing (OFLOOD) method [J. Comput. Chem. 36 (2015) 97-102]. This method utilizes conformational resampling from rarely occurring states, detected as outliers, to promote conformational transitions relevant to the rare events. However, to perform OFLOOD efficiently requires specifying a set of appropriate reaction coordinates (RCs) with non-trivial specifications. Therefore, in this paper, we propose a strategy to obtain a set of appropriate RCs using a method where the best set of RCs are automatically searched from the initially given RCs, via clustering the states of biomolecules.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    International Nuclear Information System (INIS)

    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. (paper)

  13. 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.

  14. 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.

  15. Providing Security for ATMs Using Digital Image Processing for Abnormal Incident Detection

    OpenAIRE

    G. Himaja, B. Rambabu, B.Malakonda Reddy

    2013-01-01

    This paper is designed by using PIR (Passive Infrared Radial) sensor to provide high security in ATMs. The sensor is basically a pyroelectric device. When the PIR is exposed to infrared radiation, it generates an electric charge. This electric charge triggers the camera. Camera starts capturing video, and then this video is processed by DSP processor for abnormal incident detection (misbehavior with ATM system). Once it occurred, corresponding signal is sent to microcontroller. The microcontr...

  16. 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.

  17. 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.

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

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  19. An automatic seismic signal detection based on linear predition filter theory

    International Nuclear Information System (INIS)

    An automatic seismic event detection and first arrival picking algorithm based upon linear prediction filter theory has been developed for earthquakes recorded at distances from a few kilometers up to some thousands of kilometers. In this algorithm, the linear prediction filter is not directly applied to the seismic signal but to a low-frequency function built from the signal. The efficiency of this algorithm for P-wave detection and onset picking with computer time performances consistent with on-line processing has been demonstrated by an application to a broad class of single trace signals recorded on two short-period networks (one with a sample rate of 90 samples/s and one with a sample rate of 180 samples/s)

  20. 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.

  1. 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%.

  2. 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...

  3. 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.

  4. Configurable Automatic Detection and Registration of Fiducial Frames for Device-to-Image Registration in MRI-guided Prostate Interventions

    OpenAIRE

    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 thresholdi...

  5. 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.

  6. Preliminary results of a lidar-dial integrated system for the automatic detection of atmospheric pollutants

    Science.gov (United States)

    Gaudio, P.; Gelfusa, M.; Richetta, M.

    2012-11-01

    In the last decades, atmospheric pollution in urban and industrial areas has become a major concern of both developed and developing countries. In this context, surveying relative large areas in an automatic way is an increasing common objective of public health organisations. The Lidar-Dial techniques are widely recognized as a cost-effective approach to monitor large portions of the atmosphere and, for example, they have been successful applied to the early detection of forest fire. The studies and preliminary results reported in this paper concern the development of an integrated Lidar-Dial system able to detect sudden releases in air of harmful and polluting substances. The propose approach consists of continuous monitoring of the area under surveillance with a Lidar type measurement (by means of a low cost system). Once a significant increase in the density of a pollutant is revealed, the Dial technique is used to identify the released chemicals. In this paper, the specifications of the proposed station are discussed. The most stringent requirement is the need for a very compact system with a range of at least 600-700 m. Of course, the optical wavelengths must be in an absolute eye-safe range for humans. A conceptual design of the entire system is described and the most important characteristic of the main elements are provided. In particular the capability of the envisaged laser sources, Nd:YAG and CO2 lasers, to provide the necessary quality of the measurements is carefully assessed. Since the detection of dangerous substances must be performed in an automatic way, the monitoring station will be equipped with an adequate set of control and communication devices for independent autonomous operation. The results of the first preliminary tests illustrate the potential of the chosen approach.

  7. Automatic detection of lameness in gestating group-housed sows using positioning and acceleration measurements.

    Science.gov (United States)

    Traulsen, I; Breitenberger, S; Auer, W; Stamer, E; Müller, K; Krieter, J

    2016-06-01

    Lameness is an important issue in group-housed sows. Automatic detection systems are a beneficial diagnostic tool to support management. The aim of the present study was to evaluate data of a positioning system including acceleration measurements to detect lameness in group-housed sows. Data were acquired at the Futterkamp research farm from May 2012 until April 2013. In the gestation unit, 212 group-housed sows were equipped with an ear sensor to sample position and acceleration per sow and second. Three activity indices were calculated per sow and day: path length walked by a sow during the day (Path), number of squares (25×25 cm) visited during the day (Square) and variance of the acceleration measurement during the day (Acc). In addition, data on lameness treatments of the sows and a weekly lameness score were used as reference systems. To determine the influence of a lameness event, all indices were analysed in a linear random regression model. Test day, parity class and day before treatment had a significant influence on all activity indices (P<0.05). In healthy sows, indices Path and Square increased with increasing parity, whereas variance slightly decreased. The indices Path and Square showed a decreasing trend in a 14-day period before a lameness treatment and to a smaller extent before a lameness score of 2 (severe lameness). For the index acceleration, there was no obvious difference between the lame and non-lame periods. In conclusion, positioning and acceleration measurements with ear sensors can be used to describe the activity pattern of sows. However, improvements in sampling rate and analysis techniques should be made for a practical application as an automatic lameness detection system. PMID:27074864

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

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

    Science.gov (United States)

    Su, Jie; Xu, Xuan; He, Yongjun; Song, Jinming

    2016-01-01

    We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review. PMID:27298758

  14. 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.

  15. A Hessian-based methodology for automatic surface crack detection and classification from pavement images

    Science.gov (United States)

    Ghanta, Sindhu; Shahini Shamsabadi, Salar; Dy, Jennifer; Wang, Ming; Birken, Ralf

    2015-04-01

    Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  20. 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

  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. 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...

  3. 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.

  4. 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

  5. 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.

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

    KAUST Repository

    Gereige, Issam

    2012-09-01

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

  7. Automatic detection of defects on radiant heaters based on infrared radiation

    Energy Technology Data Exchange (ETDEWEB)

    Madruga, F.J.; Gonzalez, D.A.; Mirapeix, J.; Lopez Higuera, J.M. [Cantabria Univ., Santander (Spain). Photonics Engineering Group; Jauregui, C. [Southampton Univ. (United Kingdom). Optoelectronics Research Centre

    2006-07-01

    A non-destructive testing method for automatic testing radiant heaters in an industrial quality control process based on statistical analysis of the distribution of the radiation intensity is proposed. A heater is measured by an infrared thermographic camera after a short pulse excitation. A method to locate and classify defects based on the statistical analysis of the sequence of images is developed. The distribution of the radiation intensity in each image is regulated by a fixed histogram pattern which is modified by the appearance of a defect. The histograms of the intensities are studied and the slopes, peak positions and values of the associated histogram-function define the position and kind of the defects. Fast mathematical algorithms to find peaks permit the automation of the defect-detection. The proposed technique was successfully validated with experiments in the laboratory. (orig.)

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

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

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

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

    Directory of Open Access Journals (Sweden)

    de Leon José

    2001-01-01

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

  10. Automatic detection 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...

  11. Automatic Feature Extraction, Categorization and Detection of Malicious Code in Android Applications

    Directory of Open Access Journals (Sweden)

    Muhammad Zuhair Qadir

    2014-02-01

    Full Text Available Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big concern. As the number of new Android applications tends to be rapidly increased in the near future, there is a need for automatic malware detection quickly and efficiently. In this paper, we define a simple static analysis approach to first extract the features of the android application based on intents and categories the application into a known major category and later on mapping it with the permissions requested by the application and also comparing it with the most obvious intents of category.  As a result, getting to know which apps are using features which they are not supposed to use or they do not need.

  12. 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

  13. 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

  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 Round-the-Clock Detection of Whales for Mitigation from Underwater Noise Impacts

    Science.gov (United States)

    Zitterbart, Daniel P.; Kindermann, Lars; Burkhardt, Elke; Boebel, Olaf

    2013-01-01

    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. PMID:23951113

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  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. PMID:10477365

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

    Directory of Open Access Journals (Sweden)

    R. Anand Raji

    2015-05-01

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

  2. 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.

  3. Machine intelligence-based decision-making (MIND) for automatic anomaly detection

    Science.gov (United States)

    Prasad, Nadipuram R.; King, Jason C.; Lu, Thomas

    2007-04-01

    Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

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

    Directory of Open Access Journals (Sweden)

    Fernando Mucio Bando

    2016-02-01

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

  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. 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.

  7. 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.

  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. MRI detection of unsuspected vertebral injury in acute spinal trauma: incidence and significance

    International Nuclear Information System (INIS)

    Objective. Multilevel spinal injury is well recognised. Previous studies reviewing the radiographs of spinal injury patients have shown an incidence of 15.2% of unsuspected spinal injury. It is recognised that magnetic resonance imaging (MRI) can identify injuries that are not demonstrated on radiographs. The objective of this study was to determine the incidence and significance of spinal injuries using MRI in comparison with radiographs.Design and patients. The radiographs and MR images of 110 acute spinal injury patients were reviewed independently of each other and the findings were then correlated to determine any unsuspected injury.Results. MRI detected vertebral body bone bruises (microtrabecular bone injury) in 41.8% of spinal injury patients which were not seen on radiographs. These bone bruises were best appreciated on sagittal short tau inversion recovery MR sequences and seen at contiguous and non-contiguous levels in relation to the primary injury.Conclusion. This level of incidence of bone bruises has not previously been appreciated. We recommend that patients undergoing MRI for an injured segment of the spine are better assessed by MRI of the entire spine at the same time to exclude further injury. (orig.)

  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. 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.

  12. 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

  13. 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

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Jiange Liu

    2016-06-01

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

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

    International Nuclear Information System (INIS)

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

  20. An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images.

    Science.gov (United States)

    Medhi, Jyoti Prakash; Dandapat, Samarendra

    2016-07-01

    Prolonged diabetes causes severe damage to the vision through leakage of blood and blood constituents over the retina. The effect of the leakage becomes more threatening when these abnormalities involve the macula. This condition is known as diabetic maculopathy and it leads to blindness, if not treated in time. Early detection and proper diagnosis can help in preventing this irreversible damage. To achieve this, the possible way is to perform retinal screening at regular intervals. But the ratio of ophthalmologists to patients is very small and the process of evaluation is time consuming. Here, the automatic methods for analyzing retinal/fundus images prove handy and help the ophthalmologists to screen at a faster rate. Motivated from this aspect, an automated method for detection and analysis of diabetic maculopathy is proposed in this work. The method is implemented in two stages. The first stage involves preprocessing required for preparing the image for further analysis. During this stage the input image is enhanced and the optic disc is masked to avoid false detection during bright lesion identification. The second stage is maculopathy detection and its analysis. Here, the retinal lesions including microaneurysms, hemorrhages and exudates are identified by processing the green and hue plane color images. The macula and the fovea locations are determined using intensity property of processed red plane image. Different circular regions are thereafter marked in the neighborhood of the macula. The presence of lesions in these regions is identified to confirm positive maculopathy. Later, the information is used for evaluating its severity. The principal advantage of the proposed algorithm is, utilization of the relation of blood vessels with optic disc and macula, which enhances the detection process. Proper usage of various color plane information sequentially enables the algorithm to perform better. The method is tested on various publicly available databases

  1. 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...

  2. Automatic detection of coronary artery disease in myocardial perfusion SPECT using image registration and voxel to voxel statistical comparisons.

    Science.gov (United States)

    Peace, R A; Staff, R T; Gemmell, H G; McKiddie, F I; Metcalfe, M J

    2002-08-01

    The purpose of this study was to compare the performance of automatic detection of coronary artery disease (CAD) with that of expert observers. A male and female normal image template was constructed from normal stress technetium-99m single photon emission computed tomography (SPECT) studies. Mean and standard deviation images for each sex were created by registering normal studies to a standard shape and position. The test group consisted of 104 patients who had been routinely referred for SPECT and angiography. The gold standard for CAD was defined by angiography. The test group studies were registered to the respective templates and the Z-score was calculated for each voxel. Voxels with a Z-score greater than 5 indicated the presence of CAD. The performance of this method and that of three observers were compared by continuous receiver operating characteristic (CROC) analysis. The overall sensitivity and specificity for automatic detection were 73% and 92%, respectively. The area (Az) under the CROC curve (+/-1 SE) for automatic detection of CAD was 0.88+/-0.06. There was no statistically significant difference between the performances of the three observers in terms of Az and that of automatic detection (P> or =0.25, univariate Z-score test). The use of this automated statistical mapping approach shows a performance comparable with experienced observers, but avoids inter-observer and intra-observer variability. PMID:12124485

  3. 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...

  4. An automatic integrated image segmentation, registration and change detection method for water-body extraction using HSR images and GIS data

    OpenAIRE

    H.G. Sui; Chen, G.; Hua, L.

    2013-01-01

    Automatic water-body extraction from remote sense images is a challenging problem. Using GIS data to update and extract waterbody is an old but active topic. However, automatic registration and change detection of the two data sets often presents difficulties. In this paper, a novel automatic water-body extraction method is proposed. The core idea is to integrate image segmentation, image registration and change detection with GIS data as a whole processing. A new iterative segmentat...

  5. Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system

    International Nuclear Information System (INIS)

    The aim of this study was to evaluate a computer-aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Eighty-eight consecutive spiral-CT examinations were reported by two radiologists in consensus. All examinations were reviewed using a CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm is designed to detect nodules with diameters of at least 5 mm. A total of 153 nodules were detected with at least one modality (radiologists in consensus, CAD, 85 nodules with diameter <5 mm, 68 with diameter ≥5 mm). The results of automatic nodule detection were compared to nodules detected with any modality as gold standard. Computer-aided diagnosis correctly identified 26 of 59 (38%) nodules with diameters ≥5 mm detected by visual assessment by the radiologists; of these, CAD detected 44% (24 of 54) nodules without pleural contact. In addition, 12 nodules ≥5 mm were detected which were not mentioned in the radiologist's report but represented real nodules. Sensitivity for detection of nodules ≥5 mm was 85% (58 of 68) for radiologists and 38% (26 of 68) for CAD. There were 5.8±3.6 false-positive results of CAD per CT study. Computer-aided diagnosis improves detection of pulmonary nodules at spiral CT and is a valuable second opinion in a clinical setting for lung cancer screening despite of its still limited sensitivity. (orig.)

  6. Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

    International Nuclear Information System (INIS)

    Nuclear emulsion, a tracking detector with sub-micron position resolution, has played a successful role in the field of particle physics and the analysis speed has been substantially improved by the development of automated scanning systems. This paper describes a newly developed automated scanning system and its application to the analysis of nuclear fragments emitted almost isotropically in nuclear evaporation. This system is able to recognize tracks of nuclear fragments up to |tan θ| < 3.0 (where θ is the track angle with respect to the perpendicular to the emulsion film), while existing systems have an angular acceptance limited to |tan θ| < 0.6. The automatic scanning for such a large angle track in nuclear emulsion is the first trial. Furthermore the track recognition algorithm is performed by a powerful Graphics Processing Unit (GPU) for the first time. This GPU has a sufficient computing power to process large area scanning data with a wide angular acceptance and enough flexibility to allow the tuning of the recognition algorithm. This new system will in particular be applied in the framework of the OPERA experiment: the background in the sample of τ decay candidates due to hadronic interactions will be reduced by a better detection of the emitted nuclear fragments.

  7. 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

  8. On the feasibility of automatic detection of range deviations from in-beam PET data

    Science.gov (United States)

    Helmbrecht, S.; Santiago, A.; Enghardt, W.; Kuess, P.; Fiedler, F.

    2012-03-01

    In-beam PET is a clinically proven method for monitoring ion beam cancer treatment. The objective is predominantly the verification of the range of the primary particles. Due to different processes leading to dose and activity, evaluation is done by comparing measured data to simulated. Up to now, the comparison is performed by well-trained observers (clinicians, physicists). This process is very time consuming and low in reproducibility. However, an automatic method is desirable. A one-dimensional algorithm for range comparison has been enhanced and extended to three dimensions. System-inherent uncertainties are handled by means of a statistical approach. To test the method, a set of data was prepared. Distributions of β+-activity calculated from treatment plans were compared to measurements performed in the framework of the German Heavy Ion Tumor Therapy Project at GSI Helmholtz Centre for Heavy Ion Research, Darmstadt, Germany. Artificial range deviations in the simulations served as test objects for the algorithm. Range modifications of different depth (4, 6 and 10 mm water equivalent path length) can be detected. Even though the sensitivity and specificity of a visual evaluation are higher, the method is feasible as the basis for the selection of patients from the data pool for retrospective evaluation of treatment and treatment plans and correlation with follow-up data. Furthermore, it can be used for the development of an assistance tool for a clinical application.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (EC...... 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....... 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 ( $Se=99.90$ %, $P^{+}=99.87$ ) and a private ePatch training database ( $Se=99.88$ %, $P^{+}=99.37$ %). The offline validation was conducted on the European ST-T database ( $Se=99...

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

    Science.gov (United States)

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

    2015-02-01

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

  11. Fractal analysis of elastographic images for automatic detection of diffuse diseases of salivary glands: preliminary results.

    Science.gov (United States)

    Badea, Alexandru Florin; Lupsor Platon, Monica; Crisan, Maria; Cattani, Carlo; Badea, Iulia; Pierro, Gaetano; Sannino, Gianpaolo; Baciut, Grigore

    2013-01-01

    The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD). It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of "real-time" elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology. PMID:23762183

  12. 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

  13. Hazard detection in noise-related incidents - the role of driving experience with battery electric vehicles.

    Science.gov (United States)

    Cocron, Peter; Bachl, Veronika; Früh, Laura; Koch, Iris; Krems, Josef F

    2014-12-01

    The low noise emission of battery electric vehicles (BEVs) has led to discussions about how to address potential safety issues for other road users. Legislative actions have already been undertaken to implement artificial sounds. In previous research, BEV drivers reported that due to low noise emission they paid particular attention to pedestrians and bicyclists. For the current research, we developed a hazard detection task to test whether drivers with BEV experience respond faster to incidents, which arise due to the low noise emission, than inexperienced drivers. The first study (N=65) revealed that BEV experience only played a minor role in drivers' response to hazards resulting from low BEV noise. The tendency to respond, reaction times and hazard evaluations were similar among experienced and inexperienced BEV drivers; only small trends in the assumed direction were observed. Still, both groups clearly differentiated between critical and non-critical scenarios and responded accordingly. In the second study (N=58), we investigated additionally if sensitization to low noise emission of BEVs had an effect on hazard perception in incidents where the noise difference is crucial. Again, participants in all groups differentiated between critical and non-critical scenarios. Even though trends in response rates and latencies occurred, experience and sensitization to low noise seemed to only play a minor role in detecting hazards due to low BEV noise. An additional global evaluation of BEV noise further suggests that even after a short test drive, the lack of noise is perceived more as a comfort feature than a safety threat. PMID:25302423

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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

  20. DRCPlus in a router: automatic elimination of lithography hotspots using 2D pattern detection and correction

    Science.gov (United States)

    Yang, Jie; Rodriguez, Norma; Omedes, Olivier; Gennari, Frank; Lai, Ya-Chieh; Mankad, Viral

    2010-03-01

    As technology processes continue to shrink, standard design rule checking (DRC) has become insufficient to guarantee design manufacturability. DRCPlus is a powerful technique for capturing yield detractors related to complex 2D situations1,2. DRCPlus is a pattern-based 2D design rule check beyond traditional width and space DRC that can identify problematic 2D configurations which are difficult to manufacture. This paper describes a new approach for applying DRCPlus in a router, enabling an automated approach to detecting and fixing known lithography hotspots using an integrated fast 2D pattern matching engine. A simple pass/no-pass criterion associated with each pattern offers designers guidance on how to fix these problematic patterns. Since it does not rely on compute intensive simulations, DRCPlus can be applied on fairly large design blocks and enforced in conjunction with standard DRC in the early stages of the design flow. By embedding this capability into the router, 2D yield detractors can be identified and fixed by designers in a push-button manner without losing design connectivity. More robust designs can be achieved and the impact on parasitics can be easily assessed. This paper will describe a flow using a fast 2D pattern matching engine integrated into the router in order to enforce DRCPlus rules. An integrated approach allows for rapid identification of hotspot patterns and, more importantly, allows for rapid fixing and verification of these hotspots by a tool that understands design intent and constraints. The overall flow is illustrated in Figure 1. An inexact search pattern is passed to the integrated pattern matcher. The match locations are filtered by the router through application of a DRC constraint (typically a recommended rule). Matches that fail this constraint are automatically fixed by the router, with the modified regions incrementally re-checked to ensure no additional DRCPlus violations are introduced.

  1. Evaluation of Methods for Robust, Automatic Detection of Net Tear with Remotely Operated Vehicle and Remote Sensing

    OpenAIRE

    Haugene, Tormod

    2014-01-01

    Accompanying the continuous growth of the aquaculture fish farming industry in the recent years, the usage of Remotely Operated Vehicles (ROV) for regular inspections of net integrity has become increasingly common. For a human ROV operator, routine inspections can be repetitious and time consuming, and improving the regularity and efficiency of these operations are of interest. The aim of this study was therefore be to develop a robust technique for automatic detection of net damage with an ...

  2. Automatic earthquake detection and classification with continuous hidden Markov models: a possible tool for monitoring Las Canadas caldera in Tenerife

    International Nuclear Information System (INIS)

    A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

  3. 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.

  4. Automatically Detecting Acute Myocardial Infarction Events from EHR Text: A Preliminary Study.

    Science.gov (United States)

    Zheng, Jiaping; Yarzebski, Jorge; Ramesh, Balaji Polepalli; Goldberg, Robert J; Yu, Hong

    2014-01-01

    The Worcester Heart Attack Study (WHAS) is a population-based surveillance project examining trends in the incidence, in-hospital, and long-term survival rates of acute myocardial infarction (AMI) among residents of central Massachusetts. It provides insights into various aspects of AMI. Much of the data has been assessed manually. We are developing supervised machine learning approaches to automate this process. Since the existing WHAS data cannot be used directly for an automated system, we first annotated the AMI information in electronic health records (EHR). With strict inter-annotator agreement over 0.74 and un-strict agreement over 0.9 of Cohen's κ, we annotated 105 EHR discharge summaries (135k tokens). Subsequently, we applied the state-of-the-art supervised machine-learning model, Conditional Random Fields (CRFs) for AMI detection. We explored different approaches to overcome the data sparseness challenge and our results showed that cluster-based word features achieved the highest performance. PMID:25954440

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

    Science.gov (United States)

    Liu, Delian; Zhang, Jianqi; Wang, Xiaorui

    2016-04-01

    Reference spectral signature selection is a fundamental work for automatic oil spill detection. To address this issue, a new approach is proposed here, which employs the density-based cluster to select a specific spectral signature from a hyperspectral image. This paper first introduces the framework of oil spill detection from hyperspectral images, indicating that detecting oil spill requires a reference spectral signature of oil spill, parameters of background, and a target detection algorithm. Based on the framework, we give the new reference spectral signature selection approach in details. Then, we demonstrate the estimation of background parameters according to the reflectance of seawater in the infrared bands. Next, the conventional adaptive cosine estimator (ACE) algorithm is employed to achieve oil spill detection. Finally, the proposed approach is tested via several practical hyperspectral images that are collected during the Horizon Deep water oil spill. The experimental results show that this new approach can automatically select the reference spectral signature of oil spills from hyperspectral images and has high detection performance. PMID:27137031

  6. Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

    Science.gov (United States)

    Mencarelli, A.; van Beek, S.; Zijp, L. J.; Rasch, C.; van Herk, M.; Sonke, J.-J.

    2014-04-01

    Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland-Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (-1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically implemented in

  7. Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

    International Nuclear Information System (INIS)

    Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland–Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (−1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically

  8. Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection

    OpenAIRE

    Chen, Xiang; Gilkeson, Robert C.; Fei, Baowei

    2007-01-01

    We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the “gold standard” to evaluate the capability of DEDR images for the detection and localization of calcium, we developed an automatic, intensity-based 3D-to-2D registration method...

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

    International Nuclear Information System (INIS)

    Purpose: In this study automatic detection of implanted gold markers in megavoltage portal images for on-line position verification was investigated. Methods and Materials: A detection method for fiducial gold markers, consisting of a marker extraction kernel (MEK), was developed. The detection success rate was determined for different markers using this MEK. The localization accuracy was investigated by measuring distances between markers, which were fixed on a perspex template. In order to generate images comparable to images of patients with implanted markers, this template was placed on the skin of patients before the start of the treatment. Portal images were taken of lateral prostate fields at 18 MV within 1-2 monitor units (MU). Results: The detection success rates for markers of 5 mm length and 1.2 and 1.4 mm diameter were 0.95 and 0.99 respectively when placed at the beam entry and 0.39 and 0.86 when placed at the beam exit. The localization accuracy appears to be better than 0.6 mm for all markers. Conclusion: Automatic marker detection with an acceptable accuracy at the start of a radiotherapy fraction is feasible. Further minimization of marker diameters may be achieved with the help of an a-Si flat panel imager and may increase the clinical acceptance of this technique

  10. 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.

  11. 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

  12. 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 ...

  13. Automatic region detection of maxillary sinus from human face radiographs in the assessment of recovery in chronic sinusitis

    International Nuclear Information System (INIS)

    Automatic region detection of the maxillary sinus from human face X-ray pictures is studied and presented. Firstly a new algorithm for transformation from X-ray images to bi-level images is introduced, which is based on the local properties of the images. Secondly, a region detection algorithm is used to determine global shape parameters of the maxillary sinus such as area, perimeter length, and complexity. It is shown that the regions of the maxillary sinus detected by the new algorithm coincide well with clinical diagnosis. It is also shown that the cumulative distribution function calculated from the density histogram of detected regions is a useful parameter for assessing the stage of recovery in chronic sinusitis. (author)

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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...

  6. A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans

    Science.gov (United States)

    van de Leemput, Sil; Dorssers, Frank; Ehteshami Bejnordi, Babak

    2015-03-01

    Early detection of pulmonary nodules is crucial for improving prognosis of patients with lung cancer. Computer-aided detection of lung nodules in thoracic computed tomography (CT) scans has a great potential to enhance the performance of the radiologist in detecting nodules. In this paper we present a computer-aided lung nodule detection system for computed tomography (CT) scans that works in three steps. The system first segments the lung using thresholding and hole filling. From this segmentation the system extracts candidate nodules using Laplacian of Gaussian. To reject false positives among the detected candidate nodules, multiple established features are calculated. We propose a novel feature based on a spherical shell filter, which is specifically designed to distinguish between vascular structures and nodular structures. The performance of the proposed CAD system was evaluated by partaking in the ANODE09 challenge, which presents a platform for comparing automatic nodule detection programs. The results from the challenge show that our CAD system ranks third among the submitted works, demonstrating the efficacy of our proposed CAD system. The results also show that our proposed spherical shell filter in combination with conventional features can significantly reduce the number of false positives from the detected candidate nodules.

  7. Age-related incidence of pulmonary embolism and additional pathologic findings detected by computed tomography pulmonary angiography

    Energy Technology Data Exchange (ETDEWEB)

    Groth, M., E-mail: groth.michael@googlemail.com [Center for Radiology and Endoscopy, Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany); Henes, F.O., E-mail: f.henes@uke.uni-hamburg.de [Center for Radiology and Endoscopy, Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany); Mayer, U., E-mail: mayer@uke.uni-hamburg.de [Emergency Department, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany); Regier, M., E-mail: m.regier@uke.uni-hamburg.de [Center for Radiology and Endoscopy, Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany); Adam, G., E-mail: g.adam@uke.uni-hamburg.de [Center for Radiology and Endoscopy, Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany); Begemann, P.G.C., E-mail: p.begemann@me.com [Center for Radiology and Endoscopy, Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg (Germany)

    2012-08-15

    Objective: To compare the incidence of pulmonary embolism (PE) and additional pathologic findings (APF) detected by computed tomography pulmonary angiography (CTPA) according to different age-groups. Materials and methods: 1353 consecutive CTPA cases for suspected PE were retrospectively reviewed. Patients were divided into seven age groups: {<=}29, 30-39, 40-49, 50-59, 60-69, 70-79 and {>=}80 years. Differences between the groups were tested using Fisher's exact or chi-square test. A p-value < 0.0024 indicated statistical significance when Bonferroni correction was used. Results: Incidence rates of PE ranged from 11.4% to 25.4% in different age groups. The three main APF were pleural effusion, pneumonia and pulmonary nodules. No significant difference was found between the incidences of PE in different age groups. Furthermore, APF in different age groups revealed no significant differences (all p-values > 0.0024). Conclusion: The incidences of PE and APF detected by CTPA reveal no significant differences between various age groups.

  8. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    International Nuclear Information System (INIS)

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

  9. Feasibility and initial experience of assessment of mechanical dyssynchrony using cardiovascular magnetic resonance and semi-automatic border detection

    Directory of Open Access Journals (Sweden)

    Günther Rolf W

    2008-11-01

    Full Text Available Abstract Background The systolic dyssynchrony index (SDI has been introduced as a measure of mechanical dyssynchrony using three-dimensional echocardiography to select patients who may benefit from cardiac resynchronization therapy (CRT. However, three-dimensional echocardiography may be inadequate in a number of patients with suboptimal acoustic window and no single echocardiographic measure of dyssynchrony has proven to be of value in selecting patients for CRT. Thus, the aim of this study was to determine the value of cardiovascular magnetic resonance (CMR for the assessment of the SDI in patients with reduced LV function as well as in healthy controls using semi-automatic border tracking. Methods We investigated a total of 45 patients including 35 patients (65 ± 8 years with reduced LV function (EF 30 ± 11% and a wide QRS complex as well as 10 control subjects (42 ± 21 years, EF 70 ± 11%. For cine imaging a standard SSFP imaging sequence was used with a temporal resolution of 40 frames per RR-interval. Quantitative analysis was performed off-line using a software prototype for semi-automatic border detection. Global volumes, ejection fraction and the SDI were calculated in each subject. SDI was compared with standard echocardiographic parameters of dyssynchrony. Results The mean SDI differed significantly between patients (14 ± 5% and controls (5 ± 2%, p Conclusion The results of this preliminary study suggest that CMR with semi-automatic border detection may be useful for the assessment of mechanical dyssynchrony in patients with reduced LV function. No trial registration due to recruitment period between October 2004 and November 2006

  10. The Liverpool Telescope Automatic Pipeline for Real-time GRB Afterglow Detection

    CERN Document Server

    Gomboc, A; Guidorzi, C; Mundell, C G; Mottram, C J; Fraser, S N; Smith, R J; Steele, I A; Carter, D; Bode, M F; Newsam, A M

    2005-01-01

    The 2-m robotic Liverpool Telescope (LT) is ideally suited to the rapid follow-up of unpredictable and transient events such as GRBs. Our GRB follow-up strategy is designed to identify optical/IR counterparts in real time; it involves the automatic triggering of initial observations, on receipt of an alert from Gamma Ray Observatories HETE-2, INTEGRAL and Swift, followed by automated data reduction, analysis, OT identification and subsequent observing mode choice. The lack of human intervention in this process requires robustness at all stages of the procedure. Here we describe the telescope, its instrumentation and GRB pipeline.

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

    OpenAIRE

    Otakar Pinkas

    2015-01-01

    The processing of the older MS Word format in the GSDL depends on the correct encoding of the temporary HTML file. The “windows-scripting” fails, but the wvware.exe program is successful. The actual .docx format needs user to change the setting in the Word configuration. A temporary HTML file should be encoded in UTF-8 instead of the Windows-1250 preset in the Czech environment. The automatic conversion from ISO-8859-2 to Windows-1250 for HTML pages is wrong, but the conversion ISO-8859-1 to ...

  12. 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.

  13. 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...

  14. 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.

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

    Science.gov (United States)

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

    2015-03-01

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

  16. 'Oh, no it isn't!' 'Oh, yes it is!'. The omission of criticality incident detection systems in the UK

    International Nuclear Information System (INIS)

    In the UK, the default position is that every process involving fissile material should have a Criticality Incident Detection and Alarm System (CIDAS), unless a robust argument is made that such a system can be omitted. Implementation has been inconsistent and inefficient. In practice decisions appear to be subjective and there are different views and opinions. This paper argues two opposing points of view and presents some simple ground rules. (author)

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

    OpenAIRE

    Mohammad K. Gandhi; Upendra Rameshbhai Chaudhari; Nilesh Thakor

    2016-01-01

    Background: Congenital malformation represents defects in morphogenesis during early fetal life. Congenital anomalies account for 8-15% of perinatal deaths and 13-16% of neonatal deaths in India. The objective was to study overall and individual incidence and distribution of clinically detectable congenital malformations in newborns delivered at a tertiary hospital. Methods: The present study is a prospective study of all the newborns delivered at Obstetrics and Gynecology Department, New ...

  19. Automatic detection of small bowel tumors in endoscopic capsule images by ROI selection based on discarded lightness information.

    Science.gov (United States)

    Vieira, Pedro M; Ramos, Jaime; Lima, Carlos S

    2015-08-01

    This paper addresses the problem of automatic detection of tumoral frames in endoscopic capsule videos by using features directly extracted from the color space. We show that tumor can be appropriately discriminated from normal tissue by using only color information histogram measures from the Lab color space and that light saturated regions are usually classified as tumoral regions when color based discriminative procedures are used. These regions are correctly classified if lightening is discarded becoming the tissue classifier based only on the color differences a and b of the Lab color space. While current state of the art systems for small bowel tumor detection usually rely on the processing of the whole frame regarding features extraction this paper proposes the use of fully automatic segmentation in order to select regions likely to contain tumoral tissue. Classification is performed by using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) by using features from color channels a and b of the Lab color space. The proposed algorithm outperforms in more than 5% a series of other algorithms based on features obtained from the higher frequency components selected from Wavelets and Curvelets transforms while saving important computational resources. In a matter of fact the proposed algorithm is more than 25 times faster than algorithms requiring wavelet/curvelet and co-occurrence computations. PMID:26736929

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

    International Nuclear Information System (INIS)

    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

  1. 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.

  2. Image processing applied to automatic detection of defects during ultrasonic examination

    International Nuclear Information System (INIS)

    This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

  3. Automatically Detecting Acute Myocardial Infarction Events from EHR Text: A Preliminary Study

    OpenAIRE

    Zheng, Jiaping; Yarzebski, Jorge; Ramesh, Balaji Polepalli; Robert J Goldberg; Yu, Hong

    2014-01-01

    The Worcester Heart Attack Study (WHAS) is a population-based surveillance project examining trends in the incidence, in-hospital, and long-term survival rates of acute myocardial infarction (AMI) among residents of central Massachusetts. It provides insights into various aspects of AMI. Much of the data has been assessed manually. We are developing supervised machine learning approaches to automate this process. Since the existing WHAS data cannot be used directly for an automated system, we...

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

    Directory of Open Access Journals (Sweden)

    Jie Dou

    2015-04-01

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

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. An Automatic Phase-Change Detection Technique for Colloidal Hard Sphere Suspensions

    Science.gov (United States)

    McDowell, Mark; Gray, Elizabeth; Rogers, Richard B.

    2005-01-01

    Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. However, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time consuming. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The algorithm utilizes intelligent image processing techniques that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application where regions of interest are distinguished from the background by differing patterns of motion over time.

  11. Automatic detection of spiculation of pulmonary nodules in computed tomography images

    DEFF Research Database (Denmark)

    Ciompi, F; Jacobs, C; Scholten, E.T.; Van Riel, S.J.; Winkler Wille, Mathilde Marie; Prokop, M.; Van Ginneken, B

    2015-01-01

    spectrum. A library of spectra is created by clustering data via unsupervised learning. The centroids of the clusters are used to label back each spectrum in the sampling pattern. A compact descriptor encoding the nodule morphology is obtained as the histogram of labels along all the spherical surfaces and...... the two categories, information on morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a...... used to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation...

  12. Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters

    OpenAIRE

    Faithpraise, Fina; Birch, Philip; Young, Rupert; Obu, J; Faithpraise, Bassey; Chatwin, Chris

    2013-01-01

    Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. This research demonstrates the combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection of the dataset is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). Th...

  13. Automatic firewall rules generator for anomaly detection systems with Apriori algorithm

    OpenAIRE

    Saboori, Ehsan; Parsazad, Shafigh; Sanatkhani, Yasaman

    2012-01-01

    Network intrusion detection systems have become a crucial issue for computer systems security infrastructures. Different methods and algorithms are developed and proposed in recent years to improve intrusion detection systems. The most important issue in current systems is that they are poor at detecting novel anomaly attacks. These kinds of attacks refer to any action that significantly deviates from the normal behaviour which is considered intrusion. This paper proposed a model to improve t...

  14. Development of automatic DC ground fault detection circuit for research reactors

    International Nuclear Information System (INIS)

    The paper describes Cirus 125 volts dc class-I power supply system ground fault (GF) monitoring circuit along with the associated problems with manual identification of faulty-circuit. It also presents the technique developed to automatically identify the faulty circuit and thereby getting over the problems faced with manual identification. The technique developed identifies the faulty-circuit online i.e. without de-energizing it and quickly enough so that even short duration fault can be identified which is otherwise difficult. The technique uses a timer based scanning system in which each circuit is switched over to a derived dc power supply without interrupting its supply and checked for fault. If fault is found the scanning process is terminated else the circuit is swapped. The process continues till the faulty-circuit is identified. The scanning speed can be adjusted to 5-10 circuits per second. The paper also briefly touches the aspects of the scope and limitation of the technique used in the automatic online faulty-circuit identification method, the limitation of the ground fault monitoring circuit, and the effects of ageing on the monitoring system. For better and easy understanding of various aspects of design and analysis a visual basic based program has been developed which can be used for demonstration and training purpose. The concept used for faulty circuit identification can be explored to significantly reduce the downtime of reactor, due to ground fault, particularly if the distribution system is quite complex and large; and the technique is implemented at the design stage of power supply system. (author)

  15. High incidence of thrombophilia detected in Chinese patients with venous thrombosis

    OpenAIRE

    Liu, HW; Kwong, YL; BOURKE, C; Lam, CK; Lie, AKW; Wei, D; Chan, LC

    1994-01-01

    Venous thromboembolism is rare in Chinese. To determine the incidence and disease profile of thrombophilia in Chinese patients with thrombosis, 52 unselected Chinese patients with documented venous thrombosis were studied for the presence of thrombophilia. Levels of antithrombin III (AT III), protein C (PC) and protein S (PS) as well as the presence of acquired lupus anticoagulant (LA) and anticardiolipin antibody (ACA) were investigated. Thirty patients were found to be abnormal. These consi...

  16. 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.

  17. 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…

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

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton John

    2011-01-01

    Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in Nort...

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

    CERN Document Server

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

    2012-01-01

    Studying CMEs in coronagraph data can be challenging due to their diffuse structure and transient nature, and user-specific biases may be introduced through visual inspection of the images. The large amount of data available from the SOHO, STEREO, and future coronagraph missions, also makes manual cataloguing of CMEs tedious, and so a robust method of detection and analysis is required. This has led to the development of automated CME detection and cata- loguing packages such as CACTus, SEEDS and ARTEMIS. Here we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of current catalogues. It works by first employing the dynamic CME separation technique outlined in a companion paper, and then characterising CME structure via a multiscale edge-detection algorithm. The detections are chained through time to determine the CME kinematics and morphological changes as it propagates across the plane-of-sky. The effectiveness of the...

  20. An automatic detection system for buried explosive hazards in FL-LWIR and FL-GPR data

    Science.gov (United States)

    Stone, K.; Keller, J. M.; Anderson, D. T.; Barclay, D. B.

    2012-06-01

    Improvements to an automatic detection system for locating buried explosive hazards in forward-looking longwave infrared (FL-LWIR) imagery, as well as the system's application to detection in confidence maps and forwardlooking ground penetrating radar (FL-GPR) data, are discussed. The detection system, described in previous work, utilizes an ensemble of trainable size-contrast filters and the mean-shift algorithm in Universal Transverse Mercator (UTM) coordinates. Improvements of the raw detection algorithm include weighted mean-shift within the individual size-contrast filters and a secondary classification step which exacts cell structured image space features, including local binary patterns (LBP), histogram of oriented gradients (HOG), edge histogram descriptor (EHD), and maximally stable extremal regions (MSER) segmentation based shape information, from one or more looks and classifies the resulting feature vector using a support vector machine (SVM). FL-LWIR specific improvements include elimination of the need for multiple models due to diurnal temperature variation. The improved algorithm is assessed on FL-LWIR and FL-GPR data from recent collections at a US Army test site.

  1. 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.

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

    OpenAIRE

    Jacobs, C.

    2015-01-01

    Lung cancer is the most deadly cancer in both men and women. This can be largely attributed to the fact that lung cancer is usually detected in a late stage. If the disease is detected in an early stage, the survival rate is much better. Therefore, early detection of lung cancer, in which it is still treatable, is of major importance to reduce lung cancer mortality. Early stage lung cancer manifests itself as pulmonary nodules, which are described as round opacities, well or poorly defined, m...

  3. 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...

  4. 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...

  5. 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.

  6. 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...

  7. 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...

  8. 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)

  9. Automatic detection of the optimal ejecting direction based on a discrete Gauss map

    Directory of Open Access Journals (Sweden)

    Masatomo Inui

    2014-01-01

    Full Text Available In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level “rough” Gauss map with rather sparse point distribution and another lower level “fine” Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

  10. 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%.

  11. 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.)

  12. AUTOMATIC DETECTION AND TRACKING OF CORONAL MASS EJECTIONS. II. MULTISCALE FILTERING OF CORONAGRAPH IMAGES

    Energy Technology Data Exchange (ETDEWEB)

    Byrne, Jason P.; Morgan, Huw; Habbal, Shadia R. [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Gallagher, Peter T., E-mail: jbyrne@ifa.hawaii.edu [Astrophysics Research Group, School of Physics, Trinity College Dublin, Dublin 2 (Ireland)

    2012-06-20

    Studying coronal mass ejections (CMEs) in coronagraph data can be challenging due to their diffuse structure and transient nature, and user-specific biases may be introduced through visual inspection of the images. The large amount of data available from the Solar and Heliospheric Observatory (SOHO), Solar TErrestrial RElations Observatory (STEREO), and future coronagraph missions also makes manual cataloging of CMEs tedious, and so a robust method of detection and analysis is required. This has led to the development of automated CME detection and cataloging packages such as CACTus, SEEDS, and ARTEMIS. Here, we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of current catalogs. It works by first employing the dynamic CME separation technique outlined in a companion paper, and then characterizing CME structure via a multiscale edge-detection algorithm. The detections are chained through time to determine the CME kinematics and morphological changes as it propagates across the plane of sky. The effectiveness of the method is demonstrated by its application to a selection of SOHO/LASCO and STEREO/SECCHI images, as well as to synthetic coronagraph images created from a model corona with a variety of CMEs. The algorithms described in this article are being applied to the whole LASCO and SECCHI data sets, and a catalog of results will soon be available to the public.

  13. 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.

  14. 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.)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-23

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

  16. An algorithm for automatic detection and orientation estimation of planar structures in LiDAR-scanned outcrops

    Science.gov (United States)

    Gomes, Robson K.; de Oliveira, Luiz P. L.; Gonzaga, Luiz; Tognoli, Francisco M. W.; Veronez, Mauricio R.; de Souza, Marcelo K.

    2016-05-01

    The spatial orientation of linear and planar structures in geological fieldwork is still obtained using simple hand-held instruments such as a compass and clinometer. Despite their ease of use, the amount of data obtained in this way is normally smaller than would be considered as representative of the area available for sampling. LiDAR-based remote sensors are capable of sampling large areas and providing huge sets of digitized spatial points. However, the visual identification of planes in sets of points on geological outcrops is a difficult and time-consuming task. An automatic method for detecting and estimating the orientation of planar structures has been developed to reduce analysis and processing times, and to fit the best plane for each surface represented by a set of points and thus to increase the sampled area. The algorithm detects clusters of points that are part of the same plane based on the principal component analysis (PCA) technique. When applied to real cases, it has shown high precision in both the detection and orientation of fractures planes.

  17. Development of automatic reactor internal vibration monitoring system using fuzzy peak detection and vibration mode decision method

    International Nuclear Information System (INIS)

    In this work a method to detect the vibrational peak and to decide the vibrational mode of detected peak for internal vibration monitoring system which is particularly concerned on the core support barrel (CSB) and fuel assemblies is developed. Flow induced vibration and aging process in the reactor internals cause unsoundness of the internal structure. In order to monitor the vibrational status of core internal, signals from the ex-core neutron detectors are transformed into frequency domain. By analyzing transformed frequency domain signal, an analyst can acquire the information on the vibrational characteristics of the structure, i.e., vibration frequencies of each component, vibrational level, modes of vibration, and the causes of the abnormal vibration, if any. This study is focused on the development of the automated monitoring system. Several methods are surveyed to define the peaks in power spectrum and fuzzy theory is used to automatic detection of the vibrational peaks. Fuzzy algorithm is adopted to define the modes of vibration using the peak values from fuzzy peak recognition, phase spectrum, and coherence spectrum. (author)

  18. 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

  19. 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

  20. Imaging techniques in dentistry: automatic detection and quantitation of dental caries

    International Nuclear Information System (INIS)

    Approximal carious lesions are depicted on radiographs as small (typically 1 mm2), often diffuse, radiolucencies within the radio-opacities corresponding to the outer enamel cap which covers the posterior teeth. These subtle features approach the limits of human visual perception. This contribution describes a method for more reliable detection of the presence of an approximal radiolucency and, when a lesion is found, for more accurately quantifying it. Radiographic images are converted into digital form. The data are then assessed and processed by a minicomputer, dedicated software routines carrying out the task of lesion detection and measurement. 10 refs

  1. An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy.

    Science.gov (United States)

    Iakovidis, Dimitris K; Maroulis, Dimitris E; Karkanis, Stavros A

    2006-10-01

    Today 95% of all gastrointestinal carcinomas are believed to arise from adenomas. The early detection of adenomas could prevent their evolution to cancer. A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy is presented. Unlike other systems, it accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications. It combines intelligent processing techniques of SVMs and color-texture analysis methodologies into a sound pattern recognition framework. Concerning the system's accuracy this was measured using ROC analysis and found to exceed 94%. PMID:16293240

  2. 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...

  3. 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.

  4. 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...... evaluated on a time series of psoriasis images and results are compared with dermatologists' scores....

  5. Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

    Czech Academy of Sciences Publication Activity Database

    Dvořák, Pavel; Kropatsch, W.G.; Bartušek, Karel

    2013-01-01

    Roč. 13, č. 5 (2013), s. 223-230. ISSN 1335-8871 R&D Projects: GA ČR GAP102/12/1104; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : Brain tumor * Brain tumor detection * Symmetry analysis Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.162, year: 2013

  6. Automatic co-registration of space-based sensors for precision change detection and analysis

    Science.gov (United States)

    Bryant, N.; Zobrist, A.; Logan, T.

    2003-01-01

    A variety of techniques were developed at JPL to assure sub-pixel co-registration of scenes and ortho-rectification of satellite imagery to other georeferenced information to permit precise change detection and analysis of low and moderate resolution space sensors.

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

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2016-01-01

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

  8. 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.

  9. 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.

  10. 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

  11. 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...... 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. © 2014 Elsevier Ltd. All rights reserved...

  12. 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.

  13. 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.

  14. 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.

  15. Automatic Grain Boundary Detection of Polycrystalline Materials from Multiple SLEEM Images

    Czech Academy of Sciences Publication Activity Database

    Knápek, Alexandr; Pokorná, Zuzana

    Thessaloniki: Laboratory of Analytical Chemistry, Department of Chemical Engineering, AUTh, 2013, s. 177. [IMA 2013. International Conference on Instrumental Methods of Analysis: Modern Trends and Applications /8./. Thessaloniki (GR), 15.09.2013-19.09.2013] R&D Projects: GA TA ČR TE01020118; GA ČR GAP108/11/2270 Institutional support: RVO:68081731 Keywords : detection of polycrystalline materials * SLEEM Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  16. System for Automatic Detection and Analysis of Targets in FMICW Radar Signal

    Czech Academy of Sciences Publication Activity Database

    Rejfek, Luboš; Mošna, Zbyšek; Urbář, Jaroslav; Koucká Knížová, Petra

    2016-01-01

    Roč. 67, č. 1 (2016), s. 36-41. ISSN 1335-3632 R&D Projects: GA ČR(CZ) GAP209/12/2440; GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : power spectral density (PSD) * FMICW radar * Doppler measurement * thresholding * false alert detection Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.378, year: 2014 http://iris.elf.stuba.sk/JEEEC/data/pdf/1_116-05.pdf

  17. Automatic detection of cardiac contours on MR images using fuzzy logic and dynamic programming.

    OpenAIRE

    Lalande, A; Legrand, L; Walker, P M; Jaulent, M. C.; Guy, F.; Cottin, Y; Brunotte, F

    1997-01-01

    This paper deals with the use of fuzzy logic and dynamic programming in the detection of cardiac contours in MR Images. The definition of two parameters for each pixel allows the construction of the fuzzy set of the cardiac contour points. The first parameter takes into account the grey level, and the second the presence of an edge. A corresponding fuzzy matrix is derived from the initial image. Finally, a dynamic programming with graph searching is performed on this fuzzy matrix. The method ...

  18. Automatic detection of whole night snoring events using non-contact microphone.

    Directory of Open Access Journals (Sweden)

    Eliran Dafna

    Full Text Available OBJECTIVE: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN: Sounds during polysomnography (PSG were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2, m/f 40/27 referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND RESULTS: To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental. A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore and specificity of 98.3% (noise as noise. CONCLUSIONS: Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  19. Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

    OpenAIRE

    Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

    2013-01-01

    Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acous...

  20. 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...

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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)

  9. 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.

  10. 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.

  11. 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.

  12. Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging.

    Science.gov (United States)

    Wuttisarnwattana, Patiwet; Gargesha, Madhusudhana; Van't Hof, Wouter; Cooke, Kenneth R; Wilson, David L

    2016-03-01

    With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large ( ∼ 200 GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→ 99.36%) without a significant drop in precision (99.99%→ 99.75%) . With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells. PMID:26552080

  13. 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.

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. Duplicate Detection with Efficient Language Models for Automatic Bibliographic Heterogeneous Data Integration

    OpenAIRE

    Turenne, Nicolas

    2015-01-01

    We present a new method to detect duplicates used to merge different bibliographic record corpora with the help of lexical and social information. As we show, a trivial key is not available to delete useless documents. Merging heteregeneous document databases to get a maximum of information can be of interest. In our case we try to build a document corpus about the TOR molecule so as to extract relationships with other gene components from PubMed and WebOfScience document databases. Our appro...

  19. 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.

  20. 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%).

  1. 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.

  2. Incidence of clinically silent malrotation detected on barium swallow examination in children

    International Nuclear Information System (INIS)

    Duodenojejunal flexure (DJF) orientation is often examined routinely during contrast swallow studies, including those performed for purely oesophageal queries. We examine the radiation burden and the incidence of malrotation in patients undergoing contrast swallow, without clinical suspicion for malrotation. Two hundred eighteen consecutive contrast swallow studies were reviewed. Patients whose history may potentially suggest malrotation were identified (n = 90), and remaining children were grouped based on whether DJF was examined (Group 1; n = 88) or not (Group 2; n = 40). Data extracted include demographics, radiographic parameters (dosage, screening time, number of images obtained) and examination findings. Outcome measures comprised: (i) prevalence of clinically incidental malrotation; and (ii) influence of additional evaluation of DJF on patient dosage (mean ± SEM). Malrotation was identified in 2 of 90 patients (2.2%) examined with clinical indications for possible malrotation, but none in Group 1 (13% already had normal DJF confirmed on previous examinations). Groups 1 and 2 were comparable with respect to age and gender (P = ns). Additional evaluation of DJF (Group 1) meant that 54% more images were acquired (48.5 ± 2.9 vs. 31.4 ± 3.4 images in group 2; P = 0.0002) and 24.9% increased screening time (130.8 ± 9.3 vs. 104.7 ± 13.0 seconds in group 2; P = 0.089), resulting in 32.6% increased patient dosage (1.36 ± 0.21 vs. 1.02 ± 0.16 microGym2/kg in group 2; P = 0.19). This study highlights the increased radiation exposures involved with routine screening for DJF position in those patients without clinical suspicion of malrotation, and raises questions about the validity of this practice; however, further research is needed.

  3. 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...

  4. 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

  5. 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.)

  6. 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. PMID:26736369

  7. 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.

  8. 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.

  9. 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...

  10. Functional requirements for preparedness and response to threats, thefts, detection, incidents and accidents

    International Nuclear Information System (INIS)

    A State should have a plan ready for response to the detection or suspicion of illicit trafficking in or loss of control of radioactive sources. The response to a radiological emergency may involve many organizations. Therefore, in order to be effective, the response to a radiological emergency must be well co-ordinated, and arrangements must be appropriately integrated with those for a conventional emergency. The regulatory body should require that the emergency arrangements be tested in an exercise before the commencement of operations. (author)

  11. 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. PMID:15651562

  12. Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection

    International Nuclear Information System (INIS)

    We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the ''gold standard'' to evaluate the capability of DEDR 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 DEDR images. To generate digitally reconstructed radiography (DRR) from the CT volumes, we developed several projection algorithms using the fast shear-warp method. In particular, we created a Gaussian-weighted projection for this application. We used normalized mutual information (NMI) as the similarity measurement. Simulated projection images from CT values were fused with the corresponding DEDR 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 a translation difference of less than 0.8 mm and a rotation difference of less than 0.2 deg. . For physical phantom images, the registration accuracy is 0.43±0.24 mm. Color overlay and 3D visualization of clinical images show that the two images registered well. The NMI values between the DRR and DEDR images improved from 0.21±0.03 before registration to 0.25±0.03 after registration. Registration errors measured from anatomic markers decreased from 27.6±13.6 mm before registration to 2.5±0.5 mm after registration. Our results show that the automatic 3D-to-2D registration is accurate and robust. This technique can provide a useful tool for correlating DEDR with CT images for screening coronary artery calcification

  13. Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data

    Directory of Open Access Journals (Sweden)

    Mohammad Awrangjeb

    2015-10-01

    Full Text Available Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

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

    Directory of Open Access Journals (Sweden)

    Marek Adamczyk

    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.

  20. 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

  1. Synchronous and metachronous transitional cell carcinoma of the urinary tract: Prevalence, incidence, and radiographic detection

    International Nuclear Information System (INIS)

    The authors retrospectively evaluated 645 patients with transitional cell carcinoma (TCC) of the urinary tract who were seen over a 10-yer period. Synchronous upper tract lesions were found in 14% of 68 patients with renal TCC, 18% of 38 patients with ureteral TCC, and 2.3% of 597 patients with bladder TCC. Metachronous upper tract TCC occurred in 10% of patients with renal TCC and in 13% of patients with ureteral TCC after average delays of 20 and 22 months, respectively. In 3.9% of patients with bladder TCC, metachronous upper tract lesions developed after an average delay of 40 months. Timely recognition of these lesions requires adequate, global distention of the pyelocalyceal systems and ureters and detection of subtle filling defects, minimal marginal irregularities, and covert calyceal amputation

  2. 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

  3. Automatic detection of patient position for incorporation in exact 3D reconstruction for emission tomography

    International Nuclear Information System (INIS)

    Full text: SPECT involves acquiring a set of projection images using one or more rotating gamma cameras. The projections are then reconstructed to create transverse slices. Patient motion during the scan can introduce inconsistencies into the data leading to artifacts. There remains a need for robust and effective motion correction. One approach uses the (corrupt) data itself to derive the patient position at each projection angle. Corrected data is periodically incorporated into a 3-D reconstruction. Fundamental aspects of the algorithm mechanics, particularly performance in the presence of Poisson noise, have been examined. Brain SPECT studies were simulated using a digital version of the Huffman brain phantom. Projection datasets with Poisson noise imposed, generated for different positions of the phantom, were combined and reconstructed to produce motion-corrupted reconstructions. To examine the behaviour of the cost function as the object position was changed, the corrupted re-construction was forward projected and the mean square difference (MSD) between the resulting re-projections and corresponding original projections was calculated. The ability to detect mis-positioned projections for different degrees of freedom, the importance of using dual-head camera geometry, and the effect of smoothing the original projections prior to the MSD calculation were assessed. Re-projection of the corrupt reconstruction was able to correctly identify mis-positioned projection data. The degree of movement as defined by MSD was more easily identified for translations than for rotations. Noise resulted in an increasing bias that made it difficult to distinguish the minimum MSD, particularly for z-axis rotations. This was improved by median filtering of projections. Right-angled dual-head geometry is necessary to provide stability to the algorithm and to better identify motion in all 6 degrees of freedom. These findings will assist the optimisation of a fully automated motion

  4. HPV and Chlamydia trachomatis co-detection in young asymptomatic women from high incidence area for cervical cancer.

    Science.gov (United States)

    Bellaminutti, Serena; Seraceni, Silva; De Seta, Francesco; Gheit, Tarik; Tommasino, Massimo; Comar, Manola

    2014-11-01

    Chlamydia trachomatis causing chronic inflammatory diseases has investigated as possible human papillomavirus (HPV) cofactor in cervical cancer. The aim of this study is to evaluate the prevalence of Chlamydia trachomatis and HPV co-infection in different cohorts of asymptomatic women from a Northern Italy area at high incidence for cervical cancer. Cervical samples from 441 females were collected from Cervical Cancer Screening Program, Sexually Transmitted Infectious and Assisted Reproductive Technology centres. HPV and Chlamydia trachomatis were detected simultaneously and genotyped using a highly sensitive bead based assay. The overall prevalence of Chlamydia trachomatis was estimated 9.7%, in contrast with the reported national data of 2.3%, and co-infection with HPV was diagnosed in the 17% of the samples. In females ≤ 25 years of age, the infection reached a peak of 22% and co-infection with HPV of 45.8% (P technique exhibited higher analytical sensitivity than the referred assays for the diagnosis of Chlamydia trachomatis and HPV co-infection in asymptomatic females, leading to reduction of the potential to identify incorrectly the infection status. An active screening for timely treatment of Chlamydia trachomatis infection is suggested in young females to evaluate a possible decrease in incidence of pre-cancer intraepithelial lesions. PMID:25132162

  5. 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

  6. 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. PMID:26776211

  7. Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals.

    Science.gov (United States)

    Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

    2014-01-01

    Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954

  8. 13C-detected NMR experiments for automatic resonance assignment of IDPs and multiple-fixing SMFT processing

    International Nuclear Information System (INIS)

    Intrinsically disordered proteins (IDPs) have recently attracted much interest, due to their role in many biological processes, including signaling and regulation mechanisms. High-dimensional 13C 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

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

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

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

  10. Performance portability study of an automatic target detection and classification algorithm for hyperspectral image analysis using OpenCL

    Science.gov (United States)

    Bernabe, Sergio; Igual, Francisco D.; Botella, Guillermo; Garcia, Carlos; Prieto-Matias, Manuel; Plaza, Antonio

    2015-10-01

    Recent advances in heterogeneous high performance computing (HPC) have opened new avenues for demanding remote sensing applications. Perhaps one of the most popular algorithm in target detection and identification is the automatic target detection and classification algorithm (ATDCA) widely used in the hyperspectral image analysis community. Previous research has already investigated the mapping of ATDCA on graphics processing units (GPUs) and field programmable gate arrays (FPGAs), showing impressive speedup factors that allow its exploitation in time-critical scenarios. Based on these studies, our work explores the performance portability of a tuned OpenCL implementation across a range of processing devices including multicore processors, GPUs and other accelerators. This approach differs from previous papers, which focused on achieving the optimal performance on each platform. Here, we are more interested in the following issues: (1) evaluating if a single code written in OpenCL allows us to achieve acceptable performance across all of them, and (2) assessing the gap between our portable OpenCL code and those hand-tuned versions previously investigated. Our study includes the analysis of different tuning techniques that expose data parallelism as well as enable an efficient exploitation of the complex memory hierarchies found in these new heterogeneous devices. Experiments have been conducted using hyperspectral data sets collected by NASA's Airborne Visible Infra- red Imaging Spectrometer (AVIRIS) and the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensors. To the best of our knowledge, this kind of analysis has not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

  11. {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

  12. 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.

  13. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.

    Science.gov (United States)

    Cascio, D; Magro, R; Fauci, F; Iacomi, M; Raso, G

    2012-11-01

    We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied

  14. A new low-cost procedure for detecting nucleic acids in low-incidence samples: a case study of detecting spores of Paenibacillus larvae from bee debris.

    Science.gov (United States)

    Ryba, Stepan; Kindlmann, Pavel; Titera, Dalibor; Haklova, Marcela; Stopka, Pavel

    2012-10-01

    American foulbrood, because of its virulence and worldwide spread, is currently one of the most dangerous diseases of honey bees. Quick diagnosis of this disease is therefore vitally important. For its successful eradication, however, all the hives in the region must be tested. This is time consuming and costly. Therefore, a fast and sensitive method of detecting American foulbrood is needed. Here we present a method that significantly reduces the number of tests needed by combining batches of samples from different hives. The results of this method were verified by testing each sample. A simulation study was used to compare the efficiency of the new method with testing all the samples and to develop a decision tool for determining when best to use the new method. The method is suitable for testing large numbers of samples (over 100) when the incidence of the disease is low (10% or less). PMID:23156141

  15. Detection of two novel mutations and relatively high incidence of H-RAS mutations in Vietnamese oral cancer.

    Science.gov (United States)

    Murugan, Avaniyapuram Kannan; Hong, Nguyen Thi; Cuc, Tran Thi Kim; Hung, Nguyen Chan; Munirajan, Arasambattu Kannan; Ikeda, Masa-Aki; Tsuchida, Nobuo

    2009-10-01

    Oral squamous cell carcinoma is the sixth most common cancer in the world and the seventh most common cancer in Vietnam. The RAS and PI3K-AKT signaling pathways play an important role in oral carcinogenesis. Our previous study on PI3K signaling pathway showed the absence of PIK3CA and PTEN gene mutations in Vietnamese oral cancer. We thus hypothesized that the RAS could be more likely activated as an upstream effector. However, the status of RAS mutations in Vietnamese oral cancer had not been studied. In the present study, Fifty six primary tumor DNA samples were screened for mutations of hot spots in exons 1 and 2 of H-RAS and a part of the samples for exon 7 of ERK2 gene in which we previously reported a mutation in an OSCC cell line. The H-RAS mutations were detected in 10 of 56 tumors (18%). Two novel mutations were found, one was an insertion of three nucleotides (GGC) between codons 10 and 11 resulting in in-frame insertion of glycine (10(Gly)11) and the other was a missense mutation in codon 62 (GAG>GGG). We also found T81C single nucleotide polymorphism in 12 of 56 tumors (22%) and there was no mutation in exon 7 of ERK2 gene. The H-RAS mutation incidence showed significant association with advanced stages of the tumor and also with well-differentiated tumor grade. Our study is the first to report H-RAS mutation from Vietnamese ethnicity, with two novel mutations and relatively high incidence of H-RAS mutations. The results suggest that RAS is an important member in the PI3K-AKT signaling and could play an important role in the tumorigenesis of oral carcinoma. PMID:19628422

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

    International Nuclear Information System (INIS)

    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

  17. 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

  18. 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.

  19. 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.


  20. 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

  1. 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...

  2. 'Statistical methods for automatic crack detection based on vibrothermography sequence-of-images data' by M. Li,S. D. Holland and W. Q. Meeker: Discussion 1

    Czech Academy of Sciences Publication Activity Database

    Volf, Petr

    2010-01-01

    Roč. 26, č. 5 (2010), s. 496-501. ISSN 1524-1904 Institutional research plan: CEZ:AV0Z10750506 Keywords : image analysis * statistical characteristics * material tests Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.829, year: 2010 http://library.utia.cas.cz/separaty/2011/SI/volf-statistical methods for automatic crack detection based on vibrothermography sequence-of-images data.pdf

  3. 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 ...

  4. 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)

  5. Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy

    Energy Technology Data Exchange (ETDEWEB)

    Azcona, Juan Diego [Department of Radiation Oncology, Stanford University, Stanford, California 94305 and Department of Oncology, Division of Radiation Physics, Clinica Universidad de Navarra, Pamplona, Navarra 31008 (Spain); Li Ruijiang; Mok, Edward; Hancock, Steven; Xing Lei [Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States)

    2013-03-15

    accuracy during treatment. Results: The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm. Conclusions: An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial detection and tumor tracking during VMAT treatments without the use of extra imaging dose.

  6. Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy

    International Nuclear Information System (INIS)

    accuracy during treatment. Results: The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm. Conclusions: An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial detection and tumor tracking during VMAT treatments without the use of extra imaging dose.

  7. 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.

  8. 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.

  9. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    International Nuclear Information System (INIS)

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  10. 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

  11. 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...

  12. Automatic Arabic Text Classification

    OpenAIRE

    Al-harbi, S; Almuhareb, A.; Al-Thubaity , A; Khorsheed, M. S.; Al-Rajeh, A.

    2008-01-01

    Automated document classification is an important text mining task especially with the rapid growth of the number of online documents present in Arabic language. Text classification aims to automatically assign the text to a predefined category based on linguistic features. Such a process has different useful applications including, but not restricted to, e-mail spam detection, web page content filtering, and automatic message routing. This paper presents the results of experiments on documen...

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

    OpenAIRE

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

    2015-01-01

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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Detection of Overhead Contact Lines with a 2D-Digital-Beamforming Radar System for Automatic Guidance of Trolley Trucks

    OpenAIRE

    Marlene Harter; Tom Schipper; Lukasz Zwirello; Andreas Ziroff; Thomas Zwick

    2013-01-01

    The benefit of trolley truck systems is the substitution of the diesel fuel by the cheaper and more ecological electrical energy. Trolley trucks are powered by electricity from two overhead contact lines, where one is the supply and the other the return conductor. Such trolley trucks are used for haulage at open pit mining sites but could also be used for freight traffic at roadways in the future. Automatic guidance prevents the trolley-powered trucks from leaving the track and thus allows hi...

  20. 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)