WorldWideScience

Sample records for automatic defect detection

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

    International Nuclear Information System (INIS)

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

    2003-01-01

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

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

    KAUST Repository

    Pietroy, David; Gereige, Issam; Gourgon, Cé cile

    2013-01-01

    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

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

    KAUST Repository

    Pietroy, David

    2013-12-01

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

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

    NARCIS (Netherlands)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2013-09-01

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

  6. Visualization and automatic detection of defect distribution in GaN atomic structure from sampling Moiré phase.

    Science.gov (United States)

    Wang, Qinghua; Ri, Shien; Tsuda, Hiroshi; Kodera, Masako; Suguro, Kyoichi; Miyashita, Naoto

    2017-09-19

    Quantitative detection of defects in atomic structures is of great significance to evaluating product quality and exploring quality improvement process. In this study, a Fourier transform filtered sampling Moire technique was proposed to visualize and detect defects in atomic arrays in a large field of view. Defect distributions, defect numbers and defect densities could be visually and quantitatively determined from a single atomic structure image at low cost. The effectiveness of the proposed technique was verified from numerical simulations. As an application, the dislocation distributions in a GaN/AlGaN atomic structure in two directions were magnified and displayed in Moire phase maps, and defect locations and densities were detected automatically. The proposed technique is able to provide valuable references to material scientists and engineers by checking the effect of various treatments for defect reduction. © 2017 IOP Publishing Ltd.

  7. 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.

  8. Image processing for an automatic detection of defect signals from electromagnetic cartographies

    International Nuclear Information System (INIS)

    Benoist, B.; Marqueste, L.; Birac, C.

    1994-01-01

    As the population of nuclear power plants ages, new defects are appearing in steam generator tubes (stress corrosion, corrosion pitting and intergranular corrosion). For more sophisticated expert appraisal of these defects, tubes can be examined by multifrequency eddy-current testing with an absolute coil (diameter value of 1 mm). A device, consisting of a push-puller mechanism and a motor-driven probe carrying this absolute coil, gives a helical movement to scan the inner surface of the tube. The signals obtained can be represented in the form of cartographies (3D representation in which the coordinates are the circumference, the length and amplitude of the X or Y component at a given frequency). The detection of defect signals by visual examination of these eddy-current cartographies is not always reproducible. The article describes an image processing procedure for the detection of defect signals which leads to a better reproductibility for more safety

  9. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model.

    Science.gov (United States)

    Mei, Shuang; Wang, Yudan; Wen, Guojun

    2018-04-02

    Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

  10. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

    Directory of Open Access Journals (Sweden)

    Shuang Mei

    2018-04-01

    Full Text Available Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality. Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

  11. Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding

    Directory of Open Access Journals (Sweden)

    Saad N.H.

    2016-01-01

    Full Text Available Quality control is one of important process in semiconductor manufacturing. A lot of issues trying to be solved in semiconductor manufacturing industry regarding the rate of production with respect to time. In most semiconductor assemblies, a lot of wafers from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents automatic defect segmentation of semiconductor wafer image based on multilevel thresholding algorithm which can be further adopted in machine vision system. In this work, the defect image which is in RGB image at first is converted to the gray scale image. Median filtering then is implemented to enhance the gray scale image. Then the modified multilevel thresholding algorithm is performed to the enhanced image. The algorithm worked in three main stages which are determination of the peak location of the histogram, segmentation the histogram between the peak and determination of first global minimum of histogram that correspond to the threshold value of the image. The proposed approach is being evaluated using defected wafer images. The experimental results shown that it can be used to segment the defect correctly and outperformed other thresholding technique such as Otsu and iterative thresholding.

  12. Automatic classification of defects in weld pipe

    International Nuclear Information System (INIS)

    Anuar Mikdad Muad; Mohd Ashhar Hj Khalid; Abdul Aziz Mohamad; Abu Bakar Mhd Ghazali; Abdul Razak Hamzah

    2000-01-01

    With the advancement of computer imaging technology, the image on hard radiographic film can be digitized and stored in a computer and the manual process of defect recognition and classification may be replace by the computer. In this paper a computerized method for automatic detection and classification of common defects in film radiography of weld pipe is described. The detection and classification processes consist of automatic selection of interest area on the image and then classify common defects using image processing and special algorithms. Analysis of the attributes of each defect such as area, size, shape and orientation are carried out by the feature analysis process. These attributes reveal the type of each defect. These methods of defect classification result in high success rate. Our experience showed that sharp film images produced better results

  13. Automatic classification of defects in weld pipe

    International Nuclear Information System (INIS)

    Anuar Mikdad Muad; Mohd Ashhar Khalid; Abdul Aziz Mohamad; Abu Bakar Mhd Ghazali; Abdul Razak Hamzah

    2001-01-01

    With the advancement of computer imaging technology, the image on hard radiographic film can be digitized and stored in a computer and the manual process of defect recognition and classification may be replaced by the computer. In this paper, a computerized method for automatic detection and classification of common defects in film radiography of weld pipe is described. The detection and classification processes consist of automatic selection of interest area on the image and then classify common defects using image processing and special algorithms. Analysis of the attributes of each defect such area, size, shape and orientation are carried out by the feature analysis process. These attributes reveal the type of each defect. These methods of defect classification result in high success rate. Our experience showed that sharp film images produced better results. (Author)

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

    International Nuclear Information System (INIS)

    Moysan, J.

    1992-10-01

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

  15. Automatic classification of blank substrate defects

    Science.gov (United States)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  16. Defect detection based on extreme edge of defective region histogram

    Directory of Open Access Journals (Sweden)

    Zouhir Wakaf

    2018-01-01

    Full Text Available Automatic thresholding has been used by many applications in image processing and pattern recognition systems. Specific attention was given during inspection for quality control purposes in various industries like steel processing and textile manufacturing. Automatic thresholding problem has been addressed well by the commonly used Otsu method, which provides suitable results for thresholding images based on a histogram of bimodal distribution. However, the Otsu method fails when the histogram is unimodal or close to unimodal. Defects have different shapes and sizes, ranging from very small to large. The gray-level distributions of the image histogram can vary between unimodal and multimodal. Furthermore, Otsu-revised methods, like the valley-emphasis method and the background histogram mode extents, which overcome the drawbacks of the Otsu method, require preprocessing steps and fail to use the general threshold for multimodal defects. This study proposes a new automatic thresholding algorithm based on the acquisition of the defective region histogram and the selection of its extreme edge as the threshold value to segment all defective objects in the foreground from the image background. To evaluate the proposed defect-detection method, common standard images for experimentation were used. Experimental results of the proposed method show that the proposed method outperforms the current methods in terms of defect detection.

  17. Automatic EEG spike detection.

    Science.gov (United States)

    Harner, Richard

    2009-10-01

    Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

  18. Automatic inspection of surface defects in die castings after machining

    Directory of Open Access Journals (Sweden)

    S. J. Świłło

    2011-07-01

    Full Text Available A new camera based machine vision system for the automatic inspection of surface defects in aluminum die casting was developed by the authors. The problem of surface defects in aluminum die casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. The casting surfaces are the most highly loaded regions of materials and components. Mechanical and thermal loads as well as corrosion or irradiation attacks are directed primarily at the surface of the castings. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks or tears, inclusions due to chemical reactions or foreign material in the molten metal, and pores that greatly influence the material ability to withstand these loads. Surface defects may act as a stress concentrator initiating a fracture point. If a pressure is applied in this area, the casting can fracture. The human visual system is well adapted to perform in areas of variety and change; the visual inspection processes, on the other hand, require observing the same type of image repeatedly to detect anomalies. Slow, expensive, erratic inspection usually is the result. Computer based visual inspection provides a viable alternative to human inspectors. Developed by authors machine vision system uses an image processing algorithm based on modified Laplacian of Gaussian edge detection method to detect defects with different sizes and shapes. The defect inspection algorithm consists of three parameters. One is a parameter of defects sensitivity, the second parameter is a threshold level and the third parameter is to identify the detected defects size and shape. The machine vision system has been successfully tested for the different types of defects on the surface of castings.

  19. Advantages of Multiscale Detection of Defective Pills during Manufacturing

    KAUST Repository

    Douglas, Craig C.; Deng, Li; Efendiev, Yalchin; Haase, Gundolf; Kucher, Andreas; Lodder, Robert; Qin, Guan

    2010-01-01

    We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw

  20. Automatic on-line detection system design research on internal defects of metal materials based on optical fiber F-P sensing technology

    Science.gov (United States)

    Xia, Liu; Shan, Ning; Chao, Ban; Caoshan, Wang

    2016-10-01

    Metal materials have been used in aerospace and other industrial fields widely because of its excellent characteristics, so its internal defects detection is very important. Ultrasound technology is used widely in the fields of nondestructive detection because of its excellent characteristic. But the conventional detection instrument for ultrasound, which has shortcomings such as low intelligent level and long development cycles, limits its development. In this paper, the theory of ultrasound detection is analyzed. A computational method of the defects distributional position is given. The non-contact type optical fiber F-P interference cavity structure is designed and the length of origin cavity is given. The real-time on-line ultrasound detecting experiment devices for internal defects of metal materials is established based on the optical fiber F-P sensing system. The virtual instrument of automation ultrasound detection internal defects is developed based on LabVIEW software and the experimental study is carried out. The results show that this system can be used in internal defect real-time on-line locating of engineering structures effectively. This system has higher measurement precision. Relative error is 6.7%. It can be met the requirement of engineering practice. The system is characterized by simple operation, easy realization. The software has a friendly interface, good expansibility, and high intelligent level.

  1. Visual detection of defects in solder joints

    Science.gov (United States)

    Blaignan, V. B.; Bourbakis, Nikolaos G.; Moghaddamzadeh, Ali; Yfantis, Evangelos A.

    1995-03-01

    The automatic, real-time visual acquisition and inspection of VLSI boards requires the use of machine vision and artificial intelligence methodologies in a new `frame' for the achievement of better results regarding efficiency, products quality and automated service. In this paper the visual detection and classification of different types of defects on solder joints in PC boards is presented by combining several image processing methods, such as smoothing, segmentation, edge detection, contour extraction and shape analysis. The results of this paper are based on simulated solder defects and a real one.

  2. Automatic detection of laughter

    NARCIS (Netherlands)

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

    2005-01-01

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

  3. Defect detection using transient thermography

    International Nuclear Information System (INIS)

    Mohd Zaki Umar; Ibrahim Ahmad; Ab Razak Hamzah; Wan Saffiey Wan Abdullah

    2008-08-01

    An experimental research had been carried out to study the potential of transient thermography in detecting sub-surface defect of non-metal material. In this research, eight pieces of bakelite material were used as samples. Each samples had a sub-surface defect in the circular shape with different diameters and depths. Experiment was conducted using one-sided Pulsed Thermal technique. Heating of samples were done using 30 kWatt adjustable quartz lamp while infra red (IR) images of samples were recorded using THV 550 IR camera. These IR images were then analysed with ThermofitTMPro software to obtain the Maximum Absolute Differential Temperature Signal value, ΔΤ m ax and the time of its appearance, τ m ax (ΔΤ). Result showed that all defects were able to be detected even for the smallest and deepest defect (diameter = 5 mm and depth = 4 mm). However the highest value of Differential Temperature Signal (ΔΤ m ax), were obtained at defect with the largest diameter, 20 mm and at the shallowest depth, 1 mm. As a conclusion, the sensitivity of the pulsed thermography technique to detect sub-surface defects of bakelite material is proportionately related with the size of defect diameter if the defects are at the same depth. On the contrary, the sensitivity of the pulsed thermography technique inversely related with the depth of defect if the defects have similar diameter size. (Author)

  4. Defect detection module

    International Nuclear Information System (INIS)

    Ernwein, R.; Westermann, G.

    1986-01-01

    The ''defect detector'' module is aimed at exceptional event or state recording. Foreseen for voltage presence monitoring on high supply voltage module of drift chambers, its characteristics can also show up the vanishing of supply voltage and take in account transitory fast signals [fr

  5. Toward Intelligent Software Defect Detection

    Science.gov (United States)

    Benson, Markland J.

    2011-01-01

    Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

  6. Automatic appraisal of defects in irradiated pins by eddy current testing

    International Nuclear Information System (INIS)

    Marsol, R.; Cornu, B.

    1986-10-01

    Eddy current testing is very efficient to inspect the sheaths of spent fuel elements. Automation of the process is developed to replace visual examination of recorded eddy current signals. The method is applied to austenitic steel fuel cans for fast neutron reactors to detect cracks, voids, inclusions... The different types of defects and experimental processes are recalled then automatic detection and the method for defect qualification are presented [fr

  7. Automatic Conflict Detection on Contracts

    Science.gov (United States)

    Fenech, Stephen; Pace, Gordon J.; Schneider, Gerardo

    Many software applications are based on collaborating, yet competing, agents or virtual organisations exchanging services. Contracts, expressing obligations, permissions and prohibitions of the different actors, can be used to protect the interests of the organisations engaged in such service exchange. However, the potentially dynamic composition of services with different contracts, and the combination of service contracts with local contracts can give rise to unexpected conflicts, exposing the need for automatic techniques for contract analysis. In this paper we look at automatic analysis techniques for contracts written in the contract language mathcal{CL}. We present a trace semantics of mathcal{CL} suitable for conflict analysis, and a decision procedure for detecting conflicts (together with its proof of soundness, completeness and termination). We also discuss its implementation and look into the applications of the contract analysis approach we present. These techniques are applied to a small case study of an airline check-in desk.

  8. Detecting accuracy of flaws by manual and automatic ultrasonic inspections

    International Nuclear Information System (INIS)

    Iida, K.

    1988-01-01

    As the final stage work in the nine year project on proving tests of the ultrasonic inspection technique applied to the ISI of LWR plants, automatic ultrasonic inspection tests were carried out on EDM notches, surface fatigue cracks, weld defects and stress corrosion cracks, which were deliberately introduced in full size structural components simulating a 1,100 MWe BWR. Investigated items are the performance of a newly assembled automatic inspection apparatus, detection limit of flaws, detection resolution of adjacent collinear or parallel EDM notches, detection reproducibility and detection accuracy. The manual ultrasonic inspection of the same flaws as inspected by the automatic ultrasonic inspection was also carried out in order to have comparative data. This paper reports how it was confirmed that the automatic ultrasonic inspection is much superior to the manual inspection in the flaw detection rate and in the detection reproducibility

  9. Detection of paint polishing defects

    Science.gov (United States)

    Rebeggiani, S.; Wagner, M.; Mazal, J.; Rosén, B.-G.; Dahlén, M.

    2018-06-01

    Surface finish plays a major role on perceived product quality, and is the first thing a potential buyer sees. Today end-of-line repairs of the body of cars and trucks are inevitably to secure required surface quality. Defects that occur in the paint shop, like dust particles, are eliminated by manual sanding/polishing which lead to other types of defects when the last polishing step is not performed correctly or not fully completed. One of those defects is known as ‘polishing roses’ or holograms, which are incredibly hard to detect in artificial light but are clearly visible in sunlight. This paper will present the first tests with a measurement set-up newly developed to measure and analyse polishing roses. The results showed good correlations to human visual evaluations where repaired panels were estimated based on the defects’ intensity, severity and viewing angle.

  10. Automatic Detection of Terminology Evolution

    Science.gov (United States)

    Tahmasebi, Nina

    As archives contain documents that span over a long period of time, the language used to create these documents and the language used for querying the archive can differ. This difference is due to evolution in both terminology and semantics and will cause a significant number of relevant documents being omitted. A static solution is to use query expansion based on explicit knowledge banks such as thesauri or ontologies. However as we are able to archive resources with more varied terminology, it will be infeasible to use only explicit knowledge for this purpose. There exist only few or no thesauri covering very domain specific terminologies or slang as used in blogs etc. In this Ph.D. thesis we focus on automatically detecting terminology evolution in a completely unsupervised manner as described in this technical paper.

  11. Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection

    Directory of Open Access Journals (Sweden)

    Bo-Lin Jian

    2017-06-01

    Full Text Available Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator’s night vision imaging systems AN/AVS-6(V1 and AN/AVS-6(V2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.

  12. Ultrasonic defect detection method for socket welding joint

    International Nuclear Information System (INIS)

    Tominaga, Masaaki; Matsuo, Toshiyuki; Ueno, Akihiro; Watanabe, Kunimichi; Kawamata, Kunio.

    1995-01-01

    The present invention provides a method of detecting defects over a wide range of a socket weld portion of various kinds of pipelines used, for example, in a nuclear power plant. Namely, an inclined probe is disposed to a jig for detecting defects by ultrasonic waves. This is rotated at least by one turn along the peripheral surface of the material to be detected such as weld tube joints. Defects of weld portion of the material can be detected automatically by using ultrasonic waves during the rotation. The inclined probe for detecting defects by ultrasonic waves comprises a transmission portion having a planar transmittance oscillator disposed to a wedge on the transmission side and a receiving portion comprising a planar receiving oscillator disposed to a wedge on the receiving side. With such a constitution, ultrasonic waves are emitted from the transmission portion to the defect detection portion in the welded portion. If a defect is present, defective echo is reflected to the receiving portion disposed ahead of the probe. Since the defective echo changes depending on the height of the detective portion, the estimation of the height of the defect can be facilitated. (I.S.)

  13. Automatically high accurate and efficient photomask defects management solution for advanced lithography manufacture

    Science.gov (United States)

    Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary

    2014-04-01

    Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.

  14. Automatic computer-aided diagnosis of retinal nerve fiber layer defects using fundus photographs in optic neuropathy.

    Science.gov (United States)

    Oh, Ji Eun; Yang, Hee Kyung; Kim, Kwang Gi; Hwang, Jeong-Min

    2015-05-01

    To evaluate the validity of an automatic computer-aided diagnosis (CAD) system for detection of retinal nerve fiber layer (RNFL) defects on fundus photographs of glaucomatous and nonglaucomatous optic neuropathy. We have proposed an automatic detection method for RNFL defects on fundus photographs in various cases of glaucomatous and nonglaucomatous optic neuropathy. In order to detect the vertical dark bands as candidate RNFL defects, the nonuniform illumination of the fundus image was corrected, the blood vessels were removed, and the images were converted to polar coordinates with the center of the optic disc. False positives (FPs) were reduced by using knowledge-based rules. The sensitivity and FP rates for all images were calculated. We tested 98 fundus photographs with 140 RNFL defects and 100 fundus photographs of healthy normal subjects. The proposed method achieved a sensitivity of 90% and a 0.67 FP rate per image and worked well with RNFL defects with variable depths and widths, with uniformly high detection rates regardless of the angular widths of the RNFL defects. The average detection accuracy was approximately 0.94. The overall diagnostic accuracy of the proposed algorithm for detecting RNFL defects among 98 patients and 100 healthy individuals was 86% sensitivity and 75% specificity. The proposed CAD system successfully detected RNFL defects in optic neuropathies. Thus, the proposed algorithm is useful for the detection of RNFL defects.

  15. Automatic Detection of Fake News

    OpenAIRE

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

    2017-01-01

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

  16. Lumber defect detection by ultrasonics

    Science.gov (United States)

    K. A. McDonald

    1978-01-01

    Ultrasonics, the technology of high-frequency sound, has been developed as a viable means for locating most defects In lumber for use in digital form in decision-making computers. Ultrasonics has the potential for locating surface and internal defects in lumber of all species, green or dry, and rough sawn or surfaced.

  17. 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.

  18. Automatic Smoker Detection from Telephone Speech Signals

    DEFF Research Database (Denmark)

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

    2017-01-01

    This paper proposes an automatic smoking habit detection from spontaneous telephone speech signals. In this method, each utterance is modeled using i-vector and non-negative factor analysis (NFA) frameworks, which yield low-dimensional representation of utterances by applying factor analysis...... method is evaluated on telephone speech signals of speakers whose smoking habits are known drawn from the National Institute of Standards and Technology (NIST) 2008 and 2010 Speaker Recognition Evaluation databases. Experimental results over 1194 utterances show the effectiveness of the proposed approach...... for the automatic smoking habit detection task....

  19. Defect core detection in radiata pine logs

    International Nuclear Information System (INIS)

    Wallace, G.

    1993-01-01

    Internal defect cores in Pinus radiata logs arise primarily from the practice in New Zealand of pruning trees to increase the amount of clear wood. Realising the benefits of this practice when milling the logs is hampered by the lack of a practical method for detecting the defect cores. This report attempts to establish industry requirements for detections and examine techniques which may be suitable. Some trials of a novel technique are described. (author) 19 refs.; 11 figs

  20. Stress and accidental defect detection on rolling mill rolls

    International Nuclear Information System (INIS)

    Auzas, J.-D.

    1999-01-01

    During the rolling mill process, rolls are submitted to high pressures that can lead to local decohesion or metallurgical changes. Both these cracks or softened areas must be detected as soon as they appear because of the risk of spalling, marks on the product, and mill wreck. These defects can be detected using the eddy current method, and particularly sensors specially developed for micro-defects detection. These sensors must be adapted to the environment of a roll grinding machine on which they must be installed. Users' schedule of conditions also require them to be attached to a wide range of eddy current generator and automatic computerized interpretation. Mill requirements for new high tech roll grades and quality lead to continuous development and improvement of the tools that will provide immediate 'go - no go' information. This paper is an update of these developments. (author)

  1. Automatic target detection using binary template matching

    Science.gov (United States)

    Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

    2005-03-01

    This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

  2. Progress in analysis of computed tomography (CT) images of hardwood logs for defect detection

    Science.gov (United States)

    Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt

    2003-01-01

    This paper addresses the problem of automatically detecting internal defects in logs using computed tomography (CT) images. The overall purpose is to assist in breakdown optimization. Several studies have shown that the commercial value of resulting boards can be increased substantially if defect locations are known in advance, and if this information is used to make...

  3. Automatic blood detection in capsule endoscopy video

    Czech Academy of Sciences Publication Activity Database

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

    2016-01-01

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

  4. Automatic Detection of Electric Power Troubles (ADEPT)

    Science.gov (United States)

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

    1988-11-01

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

  5. Oil defect detection of electrowetting display

    Science.gov (United States)

    Chiang, Hou-Chi; Tsai, Yu-Hsiang; Yan, Yung-Jhe; Huang, Ting-Wei; Mang, Ou-Yang

    2015-08-01

    In recent years, transparent display is an emerging topic in display technologies. Apply in many fields just like mobile device, shopping or advertising window, and etc. Electrowetting Display (EWD) is one kind of potential transparent display technology advantages of high transmittance, fast response time, high contrast and rich color with pigment based oil system. In mass production process of Electrowetting Display, oil defects should be found by Automated Optical Inspection (AOI) detection system. It is useful in determination of panel defects for quality control. According to the research of our group, we proposed a mechanism of AOI detection system detecting the different kinds of oil defects. This mechanism can detect different kinds of oil defect caused by oil overflow or material deteriorated after oil coating or driving. We had experiment our mechanism with a 6-inch Electrowetting Display panel from ITRI, using an Epson V750 scanner with 1200 dpi resolution. Two AOI algorithms were developed, which were high speed method and high precision method. In high precision method, oil jumping or non-recovered can be detected successfully. This mechanism of AOI detection system can be used to evaluate the oil uniformity in EWD panel process. In the future, our AOI detection system can be used in quality control of panel manufacturing for mass production.

  6. Advantages of Multiscale Detection of Defective Pills during Manufacturing

    KAUST Repository

    Douglas, Craig C.

    2010-01-01

    We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms. © 2010 Springer-Verlag.

  7. Channel selection for automatic seizure detection

    DEFF Research Database (Denmark)

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

    2012-01-01

    Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...... of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure detection...... sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded...

  8. Sub-surface defects detection of by using active thermography and advanced image edge detection

    International Nuclear Information System (INIS)

    Tse, Peter W.; Wang, Gaochao

    2017-01-01

    Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided. (paper)

  9. Artificial defects detection and location during welding

    International Nuclear Information System (INIS)

    Asty, M.

    1978-01-01

    Welding control by acoustic emission allows defects detection as soon as they are created. Acoustic testing saves time and gives better quality assurance in the case of multiple pass welding of plates. A welded joint was performed on A533B steel plates 250 mm thick by submerged arc welding. Artificial defects were implanted to determine significative parameters of acoustic reception. In operating conditions a significant acoustic activity takes place only during welding as shown by preliminary tests. At the same time an important noise is created by the arc, scories cooling and metal solidification and cooling. These problems are solved by an original processing in time-space detecting and locating defects with a good approximation [fr

  10. Defect detection and sizing in ultrasonic imaging

    International Nuclear Information System (INIS)

    Moysan, J.; Benoist, P.; Chapuis, N.; Magnin, I.

    1991-01-01

    This paper introduces imaging processing developed with the SPARTACUS system in the field of ultrasonic testing. The aim of the imaging processing is to detect and to separate defects echoes from background noise. Image segmentation and particularities of ultrasonic images are the base of studied methods. 4 figs.; 6 refs [fr

  11. Detection and defect correction of operating process

    International Nuclear Information System (INIS)

    Vasendina, Elena; Plotnikova, Inna; Levitskaya, Anastasiya; Kvesko, Svetlana

    2016-01-01

    The article is devoted to the current problem of enterprise competitiveness rise in hard and competitive terms of business environment. The importance of modern equipment for detection of defects and their correction is explained. Production of chipboard is used as an object of research. Short description and main results of estimation efficiency of innovative solutions of enterprises are considered. (paper)

  12. Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network

    OpenAIRE

    Andrade, Roberto Márcio de; Eduardo, Alexandre Carlos

    2011-01-01

    In the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), det...

  13. Automatic detection, tracking and sensor integration

    Science.gov (United States)

    Trunk, G. V.

    1988-06-01

    This report surveys the state of the art of automatic detection, tracking, and sensor integration. In the area of detection, various noncoherent integrators such as the moving window integrator, feedback integrator, two-pole filter, binary integrator, and batch processor are discussed. Next, the three techniques for controlling false alarms, adapting thresholds, nonparametric detectors, and clutter maps are presented. In the area of tracking, a general outline is given of a track-while-scan system, and then a discussion is presented of the file system, contact-entry logic, coordinate systems, tracking filters, maneuver-following logic, tracking initiating, track-drop logic, and correlation procedures. Finally, in the area of multisensor integration the problems of colocated-radar integration, multisite-radar integration, radar-IFF integration, and radar-DF bearing strobe integration are treated.

  14. 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.

  15. Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

    Science.gov (United States)

    Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

  16. Improvement and development of automatic detection techniques

    International Nuclear Information System (INIS)

    Yamada, Kiyomi; Takai, Setsuo; Togashi, Chikako; Itami, Jun

    2000-01-01

    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.)

  17. Automatic Earthquake Detection by Active Learning

    Science.gov (United States)

    Bergen, K.; Beroza, G. C.

    2017-12-01

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

  18. 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

  19. Brain correlates of automatic visual change detection.

    Science.gov (United States)

    Cléry, H; Andersson, F; Fonlupt, P; Gomot, M

    2013-07-15

    A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Influence of defects on diamond detection properties

    International Nuclear Information System (INIS)

    Tromson, Dominique

    2000-01-01

    This work focuses on the study of the influence of defects on the detection properties of diamond. Devices are fabricated using natural as well as synthetic diamond samples grown using the plasma enhanced chemical vapour deposition (CVD). Optical studies with infrared and Raman spectrometry are used to characterise the material properties as well as thermoluminescence and thermally stimulated current measurements. These thermally stimulated analyses reveal the presence of several trapping levels with emission temperatures below or near room temperature as well as an important level near 550 K. The influence of these defects on the alpha and X-ray detector responses is studied as a function of the initial state of the detectors (thermal treatment, irradiation) and of the measurement conditions (time, temperature). The results show a significant correlation between the charged state of traps, namely filled or empty and the response of the detectors. It appears that filling and emptying the traps respectively enhances the sensitivity and stability of detection devices to be used at room temperature and decreases the detection properties at higher temperature. Localised measurements are also used to study the spatial inhomogeneity of natural and CVD diamond samples from the 2D mapping of the detector responses. Non uniformity are attributed to a non-isotropic distribution of defects in natural diamonds. By comparing the detector responses to the topographical map of CVD samples a correlation appears between grains and grain boundaries with the variation of the detector sensitivity. Devices fabricated for detection applications with CVD samples are presented and namely for the monitoring and profiling of synchrotron beams as well as dose rate measurements in harsh environments. (author) [fr

  1. Automatic Encoding and Language Detection in the GSDL

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2014-10-01

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

  2. Defect detection on videos using neural network

    Directory of Open Access Journals (Sweden)

    Sizyakin Roman

    2017-01-01

    Full Text Available In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.

  3. Automatic polyp detection in colonoscopy videos

    Science.gov (United States)

    Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

    2017-02-01

    Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

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

    Science.gov (United States)

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

    2017-10-15

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

  5. Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

    Science.gov (United States)

    Karimi, Mohammad H; Asemani, Davud

    2014-05-01

    Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

    Science.gov (United States)

    Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

    2018-05-01

    Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    DEFF Research Database (Denmark)

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

    2005-01-01

    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...

  9. Automatic invariant detection in dynamic web applications

    NARCIS (Netherlands)

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

    2010-01-01

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

  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.

  11. A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique

    Directory of Open Access Journals (Sweden)

    Bocz Péter

    2018-03-01

    Full Text Available This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.

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

    Science.gov (United States)

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

    2013-11-15

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

  13. Defect Detectability Improvement for Conventional Friction Stir Welds

    Science.gov (United States)

    Hill, Chris

    2013-01-01

    This research was conducted to evaluate the effects of defect detectability via phased array ultrasound technology in conventional friction stir welds by comparing conventionally prepped post weld surfaces to a machined surface finish. A machined surface is hypothesized to improve defect detectability and increase material strength.

  14. Improvement to defect detection by ultrasonic data processing: the DTVG method

    International Nuclear Information System (INIS)

    Francois, D.

    1995-10-01

    The cast elbows of the pipes of the principal primary circuit of French PWR, made of austenitic-ferritic stainless steel, pose problems to control. In order to improve the ultrasonic detection of defects in coarse-grained materials, we propose a method (called DTVG) based on the statistic study of the spatial stability of events contained in temporal signals. At the Beginning, the method was developed during a thesis (G. Corneloup, 1998) to improve the detection of cracks in thin thickness austenitic welds. Here, we propose to adapt the DTVG method and estimate its performances in detection of defects in thick materials representative of cast austenitic-ferritic elbows steels. The first objective of the study is adapting the original treatment applied to the thin thickness austenitic welds for the detection of defects in thick thickness austenitic-ferritic cast steels. The second objective consist of improving the algorithm to take in account the difference between thin and thick material and estimating the performances of the DTVG method in detection in specimen block with artificial defects. This work has led to adapt the original DTVG method to control thick cast austenitic-ferritic specimen (80 mm) under normal and oblique incidence. More, the study has allowed to make the the treatment automatic (automatic research of parameters). The results have shown that the DTVG method is fitted to detect artificial defects in thick cast austenitic-ferritic sample steel. All the defects in the specimen block have been detected without revealing false indication. (author). 4 refs., 4 figs

  15. Automatic change detection to facial expressions in adolescents

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Jiannong, Shi

    2016-01-01

    Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group) were...... 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....

  16. Automatic Detection of Wild-type Mouse Cranial Sutures

    DEFF Research Database (Denmark)

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

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

  17. Development of automatic flaw detection systems for magnetic particle examination

    International Nuclear Information System (INIS)

    Shirai, T.; Kimura, J.; Amako, T.

    1988-01-01

    Utilizing a video camera and an image processor, development was carried out on automatic flaw detection and discrimination techniques for the purpose of achieving automated magnetic particle examination. Following this, fluorescent wet magnetic particle examination systems for blade roots and rotor grooves of turbine rotors and the non-fluorescent dry magnetic particle examination system for butt welds, were developed. This paper describes these automatic magnetic particle examination (MT) systems and the functional test results

  18. Automatic patient respiration failure detection system with wireless transmission

    Science.gov (United States)

    Dimeff, J.; Pope, J. M.

    1968-01-01

    Automatic respiration failure detection system detects respiration failure in patients with a surgically implanted tracheostomy tube, and actuates an audible and/or visual alarm. The system incorporates a miniature radio transmitter so that the patient is unencumbered by wires yet can be monitored from a remote location.

  19. Pipeline Defects Detection Using MFL Signals and Self Quotient Image

    International Nuclear Information System (INIS)

    Kim, Min Ho; Choi, Doo Hyun; Rho, Yong Woo

    2010-01-01

    Defects positioning of underground gas pipelines using MFL(magnetic flux leakage) inspection which is one of non-destructive evaluation techniques is proposed in this paper. MFL signals acquired from MFL PIG(pipeline inspection gauge) have nonlinearity and distortion caused by various extemal disturbances. SQI(self quotient image), a compensation technique for nonlinearity and distortion of MFL signal, is used to correct positioning of pipeline defects. Through the experiments using artificial defects carved in the KOGAS pipeline simulation facility, it is found that the performance of proposed defect detection is greatly improved compared to that of the conventional DCT(discrete cosine transform) coefficients based detection

  20. Sub-surface defect detection using transient thermography

    International Nuclear Information System (INIS)

    Mohd Zaki Umar; Huda Abdullah; Abdul Razak Hamzah; Wan Saffiey Wan Abdullah; Ibrahim Ahmad; Vavilov, Vladimir

    2009-04-01

    An experimental research had been carried out to study the potential of transient thermography in detecting sub-surface defect of non-metal material. In this research, eight pieces of bakelite material were used as samples. Each samples had a sub-surface defect in the circular shape with different diameters and depths. Experiment was conducted using one-sided Pulsed Thermal technique. Heating of samples were done using 30 k Watt adjustable quartz lamp while infra red (IR) images of samples were recorded using THV 550 IR camera. These IR images were then analysed with thermo fit TM Pro software to obtain the Maximum Absolute Differential Temperature Signal value, ΔT max and the time of its appearance, τ max (ΔT). Result showed that all defects were able to be detected even for the smallest and deepest defect (diameter = 5 mm and depth = 4 mm). However the highest value of Differential Temperature Signal (ΔT max ), were obtained at defect with the largest diameter, 20 mm and at the shallowest depth, 1 mm. As a conclusion, the sensitivity of the pulsed thermography technique to detect sub-surface defects of bakelite material is proportionately related with the size of defect diameter if the defect area at the same depth. On the contrary, the sensitivity of the pulsed thermography technique inversely related with the depth of defect if the defects have similar diameter size. (author)

  1. Eddy Current Testing for Detecting Small Defects in Thin Films

    Science.gov (United States)

    Obeid, Simon; Tranjan, Farid M.; Dogaru, Teodor

    2007-03-01

    Presented here is a technique of using Eddy Current based Giant Magneto-Resistance sensor (GMR) to detect surface and sub-layered minute defects in thin films. For surface crack detection, a measurement was performed on a copper metallization of 5-10 microns thick. It was done by scanning the GMR sensor on the surface of the wafer that had two scratches of 0.2 mm, and 2.5 mm in length respectively. In another experiment, metal coatings were deposited over the layers containing five defects with known lengths such that the defects were invisible from the surface. The limit of detection (resolution), in terms of defect size, of the GMR high-resolution Eddy Current probe was studied using this sample. Applications of Eddy Current testing include detecting defects in thin film metallic layers, and quality control of metallization layers on silicon wafers for integrated circuits manufacturing.

  2. An intelligent system for real time automatic defect inspection on specular coated surfaces

    Science.gov (United States)

    Li, Jinhua; Parker, Johné M.; Hou, Zhen

    2005-07-01

    Product visual inspection is still performed manually or semi automatically in most industries from simple ceramic tile grading to complex automotive body panel paint defect and surface quality inspection. Moreover, specular surfaces present additional challenge to conventional vision systems due to specular reflections, which may mask the true location of objects and lead to incorrect measurements. There are some sophisticated visual inspection methods developed in recent years. Unfortunately, most of them are highly computational. Systems built on those methods are either inapplicable or very costly to achieve real time inspection. In this paper, we describe an integrated low-cost intelligent system developed to automatically capture, extract, and segment defects on specular surfaces with uniform color coatings. The system inspects and locates regular surface defects with lateral dimensions as small as a millimeter. The proposed system is implemented on a group of smart cameras using its on-board processing ability to achieve real time inspection. The experimental results on real test panels demonstrate the effectiveness and robustness of proposed system.

  3. Development of Geometry Normalized Electromagnetic System (GNES) instrument for metal defect detection

    Science.gov (United States)

    Zakaria, Zakaria; Surbakti, Muhammad Syukri; Syahreza, Saumi; Mat Jafri, Mohd. Zubir; Tan, Kok Chooi

    2017-10-01

    It has been already made, calibrated and tested a geometry normalized electromagnetic system (GNES) for metal defect examination. The GNES has an automatic data acquisition system which supporting the efficiency and accuracy of the measurement. The data will be displayed on the computer monitor as a graphic display then saved automatically in the Microsoft Excel format. The transmitter will transmit the frequency pair (FP) signals i.e. 112.5 Hz and 337.5 Hz; 112.5 Hz and 1012.5 Hz; 112.5 Hz and 3037.5 Hz; 337.5 Hz and 1012.5 Hz; 337.5 Hz and 3037.5 Hz. Simultaneous transmissions of two electromagnetic waves without distortions by the transmitter will induce an eddy current in the metal. This current, in turn, will produce secondary electromagnetic fields which are measured by the receiver together with the primary fields. Measurement of percent change of a vertical component of the fields will give the percent response caused by the metal or the defect. The response examinations were performed by the models with various type of defect for the master curves. The materials of samples as a plate were using Aluminum, Brass, and Copper. The more of the defects is the more reduction of the eddy current response. The defect contrasts were tended to decrease when the more depth of the defect position. The magnitude and phase of the eddy currents will affect the loading on the coil thus its impedance. The defect must interrupt the surface eddy current flow to be detected. Defect lying parallel to the current path will not cause any significant interruption and may not be detected. The main factors which affect the eddy current response are metal conductivity, permeability, frequency, and geometry.

  4. Defect detection of elevator wire rope by using wavelet analysis; Wavelet kaiseki ni yoru elevator rope no sonsho kenshutsu

    Energy Technology Data Exchange (ETDEWEB)

    Kaneda, M.; Kawata, A.; Hayashi, S. [Kansai University, Osaka (Japan). Faculty of Engineering; Tokui, K. [Mitsubishi Electric Building Techno-Service Co. Ltd., Tokyo (Japan)

    1998-10-31

    Detecting strand breakage and local wear of elevator wire rope uses currently a method using a rope tester. This method magnetizes a rope with electric magnet and detects defected part as leakage flux. Pulsed signals are issued from the defected part, variation in magnetic flux leakage due to rope swinging produces noise, and both get mixed together. Therefore, the detection is performed finally by visual check and palpation. This paper discusses a method that analyzes measurement data derived by the rope tester by using wavelet conversion, and detects the defected part automatically without being confused by noise. The pulsed signals generated from the defected part can be detected from noise by decomposing multiplex resolution using the Haar basis. As a result of the experiment, cases that may be overlooked in visual check because of S/N ratio being too small or the pulsed signals being too weak were all detected. 11 refs., 14 figs.

  5. Automatic detection of microaneurysms using microstructure and ...

    Indian Academy of Sciences (India)

    with DIARETDB0 and DIARETDB1 datasets using a classifier based on multi- ... developed, the accurate detection of microaneurysm in colour retinal images .... Two dictionary elements of vessel and non-vessel were used in the sparse rep- ..... Images are analyzed from the point of view of its specific microstructures: ridges,.

  6. Automatic Emergence Detection in Complex Systems

    Directory of Open Access Journals (Sweden)

    Eugene Santos

    2017-01-01

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

  7. 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…

  8. Semi-supervised rail defect detection from imbalanced image data

    NARCIS (Netherlands)

    Hajizadeh, S.; Nunez Vicencio, Alfredo; Tax, D.M.J.; Acarman, Tankut

    2016-01-01

    Rail defect detection by video cameras has recently gained much attention in both
    academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning

  9. Automatic hair detection in the wild

    DEFF Research Database (Denmark)

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

    2010-01-01

    This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour....... The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images....

  10. Cracklike defects detection and sizing from co-occurrence matrices

    International Nuclear Information System (INIS)

    Moysan, J.; Benoist, P.; Magnin, I.

    1991-01-01

    The inspection of austenitic welds used in nuclear field with ultrasounds poses problems in interpretation: strong grain noise makes difficult the detection of the crack top and the crack bottom. Since corresponding echoes enable the defect sizing, defect sizing also becomes difficult. The formation of 2D images (BSCAN), and their processing enable an increase in the effectiveness of testing. We present a segmentation method, based on co-occurrence matrix, which separates defects zones and noise zones. Examples of segmentation improvement applied to artificial defects are presented

  11. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

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

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

    Science.gov (United States)

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

  13. Data mining to detect clinical mastitis with automatic milking

    NARCIS (Netherlands)

    Kamphuis, C.; Mollenhorst, H.; Heesterbeek, J.A.P.; Hogeveen, H.

    2010-01-01

    Our objective was to use data mining to develop and validate a detection model for clinical mastitis (CM) using sensor data collected at nine Dutch dairy herds milking automatically. Sensor data was available for almost 3.5 million quarter milkings (QM) from 1,109 cows; 348 QM with CM were observed

  14. Comparing different approaches for automatic pronunciation error detection

    NARCIS (Netherlands)

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

    2009-01-01

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

  15. Automatic detection of omissions in medication lists.

    Science.gov (United States)

    Hasan, Sharique; Duncan, George T; Neill, Daniel B; Padman, Rema

    2011-01-01

    Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. Results show that collaborative filtering identifies the missing drug in the top-10 list about 40-50% of the time and the therapeutic class of the missing drug 50%-65% of the time at the three clinics in this study. Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).

  16. PAUT-based defect detection method for submarine pressure hulls

    Directory of Open Access Journals (Sweden)

    Min-jae Jung

    2018-03-01

    Full Text Available A submarine has a pressure hull that can withstand high hydraulic pressure and therefore, requires the use of highly advanced shipbuilding technology. When producing a pressure hull, periodic inspection, repair, and maintenance are conducted to maintain its soundness. Of the maintenance methods, Non-Destructive Testing (NDT is the most effective, because it does not damage the target but sustains its original form and function while inspecting internal and external defects. The NDT process to detect defects in the welded parts of the submarine is applied through Magnetic particle Testing (MT to detect surface defects and Ultrasonic Testing (UT and Radiography Testing (RT to detect internal defects. In comparison with RT, UT encounters difficulties in distinguishing the types of defects, can yield different results depending on the skills of the inspector, and stores no inspection record. At the same time, the use of RT gives rise to issues related to worker safety due to radiation exposure. RT is also difficult to apply from the perspectives of the manufacturing of the submarine and economic feasibility. Therefore, in this study, the Phased Array Ultrasonic Testing (PAUT method was applied to propose an inspection method that can address the above disadvantages by designing a probe to enhance the precision of detection of hull defects and the reliability of calculations of defect size. Keywords: Submarine pressure hull, Non-destructive testing, Phased array ultrasonic testing

  17. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

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

    2010-01-01

    . 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...... electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures....

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

    Science.gov (United States)

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

    2009-01-01

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

  19. System for automatic detection of lung nodules exhibiting growth

    Science.gov (United States)

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

    2004-05-01

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

  20. Automatic detection of AutoPEEP during controlled mechanical ventilation

    Directory of Open Access Journals (Sweden)

    Nguyen Quang-Thang

    2012-06-01

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

  1. Studies on defect detectability in banded stainless steel tubes

    International Nuclear Information System (INIS)

    Shyamsunder, M.T.; Rao, B.P.C.; Babu Rao, C.; Jayakumar, T.; Kalyanasundaram, P.; Baldev Raj

    1996-01-01

    During inspection of one batch of stainless steel cladding tubes, a few of the tubes gave rise to continuous large amplitude indications throughout the length of the tube. It was observed that the presence of any defects in such tubes would be impossible to detect, due to the poor signal-to-noise ratio. Detailed investigations regarding the surface profile of the tubes were carried out using a novel technique called the projected interferometry method revealed periodic diametral variations and the same were further confirmed by cross sectional profiling. The feasibility of detecting defects in such banded tubes, using eddy current testing were carried out on tubes with artificial defects. This paper discusses the use of three different eddy current methods and their relative performances for inspection. The specific advantages of the phased array eddy current testing method in unambiguous defect detection in situations similar to the one encountered during the present investigations are also discussed. (author)

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Nicole van Klink

    2017-01-01

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

  4. 46 CFR 161.002-10 - Automatic fire detecting system control unit.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 6 2010-10-01 2010-10-01 false Automatic fire detecting system control unit. 161.002-10...-10 Automatic fire detecting system control unit. (a) General. The fire detecting system control unit... and the battery to be charged. (h) Automatic fire detecting system, battery charging and control—(1...

  5. A laser optical method for detecting corn kernel defects

    Energy Technology Data Exchange (ETDEWEB)

    Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.

    1984-01-01

    An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-02-10

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

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

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

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

  8. A Brazing Defect Detection Using an Ultrasonic Infrared Imaging Inspection

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jai Wan; Choi, Young Soo; Jung, Seung Ho; Jung, Hyun Kyu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2007-10-15

    When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material in real time. In this paper a realtime detection of the brazing defect of thin Inconel plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (23 kHz) ultrasonic transducer was used to infuse the welded Inconel plates with a short pulse of sound for 280 ms. The ultrasonic source has a maximum power of 2 kW. The surface temperature of the area under inspection is imaged by an infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the bound between the two faces of the Inconel plates near the defective brazing point and heated up highly, are observed. And the weak thermal signal is observed at the defect position of brazed plate also. Using the image processing technology such as background subtraction average and image enhancement using histogram equalization, the position of defective brazing regions in the thin Inconel plates can be located certainly

  9. A Novel Cascade Classifier for Automatic Microcalcification Detection.

    Directory of Open Access Journals (Sweden)

    Seung Yeon Shin

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

  10. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-07-01

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

  12. Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jose María Armingol

    2010-03-01

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

  14. Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  15. Echo detected EPR as a tool for detecting radiation-induced defect signals in pottery

    International Nuclear Information System (INIS)

    Zoleo, Alfonso; Bortolussi, Claudia; Brustolon, Marina

    2011-01-01

    Archaeological fragments of pottery have been investigated by using CW-EPR and Echo Detected EPR (EDEPR). EDEPR allows to remove the CW-EPR dominant Fe(III) background spectrum, hiding much weaker signals potentially useful for dating purpose. EDEPR spectra attributed to a methyl radical and to feldspar defects have been recorded at room and low temperature for an Iron Age cooking ware (700 B.C.). A study on the dependence of EDEPR intensity over absorbed dose on a series of γ-irradiated brick samples (estimated age of 562 ± 140 B.C.) has confirmed the potential efficacy of the proposed method for spotting defect signals out of the strong iron background. - Highlights: → Fe(III) CW-EPR signals cover CW-EPR-detectable defects in ceramics. → Echo detected EPR gets rid of Fe(III) signals, disclosing defect signals. → Echo detected EPR detects defect signals even at relatively low doses.

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

    Directory of Open Access Journals (Sweden)

    P. Duncan

    2012-08-01

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

  17. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

  19. Detection of Defects of BGA by Tomography Imaging

    Directory of Open Access Journals (Sweden)

    Tetsuhiro SUMIMOTO

    2005-08-01

    Full Text Available To improve a cost performance and the reliability of PC boards, an inspection of BGA is required in the surface mount process. Types of defects at BGA solder joints are solder bridges, missing connections, solder voids, open connections and miss-registrations of parts. As we can find mostly solder bridges in these defects, we pick up this to detect solder bridge in a production line. The problems of image analysis for the detection of defects at BGA solder joints are the detection accuracy and image processing time according to a line speed of production. To get design data for the development of the inspection system, which can be used easily in the surface mount process, it is important to develop image analysis techniques based on the X-ray image data. We attempt to detect the characteristics of the defects of BGA based on an image analysis. Using the X-ray penetration equipment, we have captured images of an IC package to search an abnormal BGA. Besides, in order to get information in detail of an abnormal BGA, we tried to capture the tomographic images utilizing the latest imaging techniques.

  20. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

    2016-01-01

    In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

  1. Color image segmentation to detect defects on fresh ham

    Science.gov (United States)

    Marty-Mahe, Pascale; Loisel, Philippe; Brossard, Didier

    2003-04-01

    We present in this paper the color segmentation methods that were used to detect appearance defects on 3 dimensional shape of fresh ham. The use of color histograms turned out to be an efficient solution to characterize the healthy skin, but a special care must be taken to choose the color components because of the 3 dimensional shape of ham.

  2. Detection and evaluation of weld defects in stainless steel using alternating current field measurement

    Science.gov (United States)

    Wei-Li, Ma, Weiping; Pan-Qi, Wen-jiao, Dou; Yuan, Xin'an; Yin, Xiaokang

    2018-04-01

    Stainless steel is widely used in nuclear power plants, such as various high-radioactive pool, tools storage and fuel transportation channel, and serves as an important barrier to stop the leakage of high-radioactive material. NonDestructive Evaluation (NDE) methods, eddy current testing (ET), ultrasonic examination (UT), penetration testing (PT) and hybrid detection method, etc., have been introduced into the inspection of a nuclear plant. In this paper, the Alternating Current Field Measurement (ACFM) was fully applied to detect and evaluate the defects in the welds of the stainless steel. Simulations were carried out on different defect types, crack lengths, and orientation to reveal the relationship between the signals and dimensions to determine whether methods could be validated by the experiment. A 3-axis ACFM probe was developed and three plates including 16 defects, which served in nuclear plant before, were examined by automatic detection equipment. The result shows that the minimum detectable crack length on the surface is 2mm and ACFM shows excellent inspection results for a weld in stainless steel and gives an encouraging prospect of broader application.

  3. Fully automatic AI-based leak detection system

    Energy Technology Data Exchange (ETDEWEB)

    Tylman, Wojciech; Kolczynski, Jakub [Dept. of Microelectronics and Computer Science, Technical University of Lodz in Poland, ul. Wolczanska 221/223, Lodz (Poland); Anders, George J. [Kinectrics Inc., 800 Kipling Ave., Toronto, Ontario M8Z 6C4 (Canada)

    2010-09-15

    This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network - the decision-making component of the system - using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in and form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed. (author)

  4. Automatic Constraint Detection for 2D Layout Regularization.

    Science.gov (United States)

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

    2016-08-01

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

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

    Science.gov (United States)

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

    2013-11-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  9. Automatic Shadow Detection and Removal from a Single Image.

    Science.gov (United States)

    Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

    2016-03-01

    We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

  10. 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...

  11. Development of an automatic human duress detection system

    International Nuclear Information System (INIS)

    Greene, E.R.; Davis, J.G.; Tuttle, W.C.

    1979-01-01

    A method for automatically detecting duress in security personnel utilizes real-time assessment of physiological data (heart rate) to evaluate psychological stress. Using body-worn tape recorders, field data have been collected on 22 Albuquerque police officers (20 male, 2 female) to determine actual heart rate responses in both routine and life-threatening situations. Off-line computer analysis has been applied to the data to determine the speed and reliability with which an alarm could be triggered. Alarm algorithms relating field responses to laboratory collected baseline responses have been developed

  12. Sensors based on GMR'S for detection of subsurface defects

    International Nuclear Information System (INIS)

    Cordon, J.; Ribes, B.; Vazquez, J.

    2010-01-01

    The use of magneto resistive sensors, GMR, as receptors in eddy current probe has certain advantages over the use of conventional inductive sensors, which puts an alternative for the detection of subsurface defects in metal components with thick materials. It has carried out a study of the most important characteristics of these sensors, which has enabled the manufacture of several probes based on OMR. In this paper we analyze different configurations and present the results of the analysis on several blocks with different defects in materials.

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

  15. AUTOMATIC ROAD GAP DETECTION USING FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    S. Hashemi

    2012-09-01

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

  16. Automatic Road Gap Detection Using Fuzzy Inference System

    Science.gov (United States)

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

    2011-09-01

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

  17. Automatic correspondence detection in mammogram and breast tomosynthesis images

    Science.gov (United States)

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

    2012-02-01

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

  18. 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.

  19. Detecting Topological Defect Dark Matter Using Coherent Laser Ranging System

    Science.gov (United States)

    Yang, Wanpeng; Leng, Jianxiao; Zhang, Shuangyou; Zhao, Jianye

    2016-01-01

    In the last few decades, optical frequency combs with high intensity, broad optical bandwidth, and directly traceable discrete wavelengths have triggered rapid developments in distance metrology. However, optical frequency combs to date have been limited to determine the absolute distance to an object (such as satellite missions). We propose a scheme for the detection of topological defect dark matter using a coherent laser ranging system composed of dual-combs and an optical clock via nongravitational signatures. The dark matter field, which comprises a defect, may interact with standard model particles, including quarks and photons, resulting in the alteration of their masses. Thus, a topological defect may function as a dielectric material with a distinctive frequency-depend index of refraction, which would cause the time delay of a periodic extraterrestrial or terrestrial light. When a topological defect passes through the Earth, the optical path of long-distance vacuum path is altered, this change in optical path can be detected through the coherent laser ranging system. Compared to continuous wavelength(cw) laser interferometry methods, dual-comb interferometry in our scheme excludes systematic misjudgement by measuring the absolute optical path length. PMID:27389642

  20. Real-time defect detection on highly reflective curved surfaces

    Science.gov (United States)

    Rosati, G.; Boschetti, G.; Biondi, A.; Rossi, A.

    2009-03-01

    This paper presents an automated defect detection system for coated plastic components for the automotive industry. This research activity came up as an evolution of a previous study which employed a non-flat mirror to illuminate and inspect high reflective curved surfaces. According to this method, the rays emitted from a light source are conveyed on the surface under investigation by means of a suitably curved mirror. After the reflection on the surface, the light rays are collected by a CCD camera, in which the coating defects appear as shadows of various shapes and dimensions. In this paper we present an evolution of the above-mentioned method, introducing a simplified mirror set-up in order to reduce the costs and the complexity of the defect detection system. In fact, a set of plane mirrors is employed instead of the curved one. Moreover, the inspection of multiple bend radius parts is investigated. A prototype of the machine vision system has been developed in order to test this simplified method. This device is made up of a light projector, a set of plane mirrors for light rays reflection, a conveyor belt for handling components, a CCD camera and a desktop PC which performs image acquisition and processing. Like in the previous system, the defects are identified as shadows inside a high brightness image. At the end of the paper, first experimental results are presented.

  1. Automatic detection and classification of human epicardial atrial unipolar electrograms

    International Nuclear Information System (INIS)

    Dubé, B; Vinet, A; Xiong, F; Yin, Y; LeBlanc, A-R; Pagé, P

    2009-01-01

    This paper describes an unsupervised signal processing method applied to three-channel unipolar electrograms recorded from human atria. These were obtained by epicardial wires sutured on the right and left atria after coronary artery bypass surgery. Atrial (A) and ventricular (V) activations had to be detected and identified on each channel, and gathered across the channels when belonging to the same global event. The algorithm was developed and optimized on a training set of 19 recordings of 5 min. It was assessed on twenty-seven 2 h recordings taken just before the onset of a prolonged atrial fibrillation for a total of 1593697 activations that were validated and classified as normal atrial or ventricular activations (A, V) and premature atrial or ventricular activations (PAA, PVA). 99.93% of the activations were detected, and amongst these, 99.89% of the A and 99.75% of the V activations were correctly labelled. In the subset of the 39705 PAA, 99.83% were detected and 99.3% were correctly classified as A. The false positive rate was 0.37%. In conclusion, a reliable fully automatic detection and classification algorithm was developed that can detect and discriminate A and V activations from atrial recordings. It can provide the time series needed to develop a monitoring system aiming to identify dynamic predictors of forthcoming cardiac events such as postoperative atrial fibrillation

  2. Defect detection method in digital radiography for porosity in magnesium castings

    International Nuclear Information System (INIS)

    Rebuffel, V.; Sood, S.; Blakeley, B.

    2006-01-01

    European project MAGCAST has been devoted to X-rays inspection of magnesium components of complex shapes, as used in spatial, aeronautics, or automotive industries. Porosity affects seriously casting quality, and is critical for safety parts. A radiographic system has been designed, and optimised considering the specific requirements, defect size, and component characteristics. It is composed of a stable mini-focus generator, a direct-conversion detector, and software. In this paper we focus on the numerical method ensuring the automatic detection from the obtained radiographs. The principle consists in a subtraction of a reference image, then a defect extraction on the resulting flattened image. We propose an original algorithm to built off-line a reference image from a set of radiographs acquired using different components. The purpose is to get a completely defect-free reference image, associated to a confidence map. After subtraction of this reference image to the on-line acquired radiograph, an extraction step is performed, taken into account the residual errors due to non-perfect previous steps within the framework of a bayesian segmentation method. Characteristics on defect are also computed, to allow a later classification. Experimental validation of the method on industrial castings is discussed. (orig.)

  3. Automatic detection of ''bore slug'' in tubes; Detection automatique des manques de metal internes sur tubes

    Energy Technology Data Exchange (ETDEWEB)

    Bisiaux, B.; Deutsch, S.; Tailleux, O.; Mette, F. [CEV Vallourec, Aulnoye (France)

    2001-07-01

    During the tube manufacturing for the petroleum industry, the lacks of internal metal (called Bore Slug) can be created during the hot rolling. These large defects are not good detected by the classic UT and by the wall thickness measurement. That's why VALLOUREC developed an automatic UT device which works by transmission. Nevertheless, this system is too little selective and can cause no doubtful pipes (tubes good detected bad). We adapted a Bore Slug control system on the VMOG UK RP20 at the end of August. The results are rather good and showed a good detection of the Bore Slug and very little no doubtful pipes. (authors)

  4. 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.

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

    Science.gov (United States)

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

    2018-03-26

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

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

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

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

  7. Detection of defective fuel rods in water reactors - a review

    International Nuclear Information System (INIS)

    Hartog, J.M.

    1980-01-01

    Consideration of the fundamental processes of fission product release within fuel pellets and at the pellet surface, and its transport in the fuel/cladding interspace and from fuel rod to coolant, indicates what radio-nuclides will be detectable in the coolant from small and large cladding failures. A better understanding of the aggregate fission product transport is required to allow reactor operators to interpret signals from detection systems in terms of quantitative cladding deterioration. This needs experimental investigation in a specially instrumented loop, as well as development of a technique to cause a rod to defect deliberately during steady power operation. (author)

  8. 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.

  9. Automatic crack detection and classification method for subway tunnel safety monitoring.

    Science.gov (United States)

    Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

    2014-10-16

    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.

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

    KAUST Repository

    Gereige, Issam

    2012-09-01

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

  11. PLASTIC PIPE DEFECT DETECTION USING NONLINEAR ACOUSTIC MODULATION

    Directory of Open Access Journals (Sweden)

    Gigih Priyandoko

    2015-02-01

    Full Text Available This project discuss about the defect detection of plastic pipe by using nonlinear acoustic wave modulation method. Nonlinaer acoustic modulations are investigated for fatigue crack detection. It is a sensitive method for damage detection and it is based on the propagation of high frequency acoustic waves in plastic pipe with low frequency excitation. The plastic pipe is excited simultaneously with a slow amplitude modulated vibration pumping wave and a constant amplitude probing wave. The frequency of both the excitation signals coincides with the resonances of the plastic pipe. An actuator is used for frequencies generation while sensor is used for the frequencies detection. Besides that, a PVP pipe is used as the specimen as it is commonly used for the conveyance of liquid in many fields. The results obtained are being observed and the difference between uncrack specimen and cracked specimen can be distinguished.

  12. 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.

  13. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

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

  14. Automatic cross-sectioning and monitoring system locates defects in electronic devices

    Science.gov (United States)

    Jacobs, G.; Slaughter, B.

    1971-01-01

    System consists of motorized grinding and lapping apparatus, sample holder, and electronic control circuit. Low power microscope examines device to pinpoint location of circuit defect, and monitor displays output signal when defect is located exactly.

  15. Advanced defect detection algorithm using clustering in ultrasonic NDE

    Science.gov (United States)

    Gongzhang, Rui; Gachagan, Anthony

    2016-02-01

    A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.

  16. Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy.

    Science.gov (United States)

    Zhi, Ying Xuan; Lukasik, Michelle; Li, Michael H; Dolatabadi, Elham; Wang, Rosalie H; Taati, Babak

    2018-01-01

    Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

  17. 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.

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

    Science.gov (United States)

    Kato, Y.; Wedemeyer, S.

    2017-05-01

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

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  20. Automatic detection of blurred images in UAV image sets

    Science.gov (United States)

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

    2016-12-01

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

  1. Symmetric Pin Diversion Detection using a Partial Defect Detector (PDET)

    International Nuclear Information System (INIS)

    Sitaraman, S.; Ham, Y.S.

    2009-01-01

    Since the signature from the Partial Defect Detector (PDET) is principally dependent on the geometric layout of the guide tube locations, the capability of the technique in detecting symmetric diversion of pins needs to be determined. The Monte Carlo simulation study consisted of cases where pins were removed in a symmetric manner and the resulting signatures were examined. In addition to the normalized gamma-to-neutron ratios, the neutron and gamma signatures normalized to their maximum values, were also examined. Examination of the shape of the three curves as well as of the peak-to-valley differences in excess of the maximum expected in intact assemblies, indicated pin diversion. A set of simulations with various symmetric patterns of diversion were examined. The results from these studies indicated that symmetric diversions as low as twelve percent could be detected by this methodology

  2. Towards the automatic detection and analysis of sunspot rotation

    Science.gov (United States)

    Brown, Daniel S.; Walker, Andrew P.

    2016-10-01

    Torsional rotation of sunspots have been noted by many authors over the past century. Sunspots have been observed to rotate up to the order of 200 degrees over 8-10 days, and these have often been linked with eruptive behaviour such as solar flares and coronal mass ejections. However, most studies in the literature are case studies or small-number studies which suffer from selection bias. In order to better understand sunspot rotation and its impact on the corona, unbiased large-sample statistical studies are required (including both rotating and non-rotating sunspots). While this can be done manually, a better approach is to automate the detection and analysis of rotating sunspots using robust methods with well characterised uncertainties. The SDO/HMI instrument provide long-duration, high-resolution and high-cadence continuum observations suitable for extracting a large number of examples of rotating sunspots. This presentation will outline the analysis of SDI/HMI data to determine the rotation (and non-rotation) profiles of sunspots for the complete duration of their transit across the solar disk, along with how this can be extended to automatically identify sunspots and initiate their analysis.

  3. Automatic detection of atrial fibrillation in cardiac vibration signals.

    Science.gov (United States)

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

    2013-01-01

    We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

  4. Development of a system for automatic detection of pellet failures

    International Nuclear Information System (INIS)

    Lavagnino, C.E.

    1996-01-01

    Nowadays, the failure controls in UO 2 pellets for Atucha and Embalse reactors are performed visually. In this work it is presented the first stage of the development of a system that allows an automatic approach to the task. For this purpose, the problem has been subdivided in three jobs: choosing the illumination environment, finding the algorithm that detects failures with user-defined tolerance and engineering the mechanic system that supports the desired manipulations of the pellets. In this paper, the former two are developed. a) Finding the illumination conditions that allow subtracting the failure from the normal element surface, knowing, in first place, the cylindrical characteristics of it and, as a consequence, the differences in the light reflection direction and, in second place, the texture differences in relation to the rectification type of the pellet. b) Writing a fast and simple algorithm that allows the identification of the failure following the production specifications. Examples of the developed algorithm are shown. (author). 4 refs

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

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

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

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

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

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

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

    Science.gov (United States)

    Salvagnini, Elena; Bosmans, Hilde; Struelens, Lara; Marshall, Nicholas W

    2015-07-01

    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. 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 (Tt) in the European guidelines for the 0.1 mm diameter disc (i.e., Tt ≤ 1.10 μm gold). The default and modified AEC modes were compared in terms of contrast-detail performance (Tt), 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. 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. Threshold gold thickness (0.1 mm diameter disc) for the default

  8. 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

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  10. System for the automatic analysis of defects in X-ray imaging

    International Nuclear Information System (INIS)

    Favier, C.; Thomas, G.; Brebant, C.; Mogavero, R.

    1984-05-01

    A radiological device was developed to obtain direct digitized views. A set of algorithms has been developed and demonstrated for the automatic evaluation of weldings. Some results concerning electronuclear fuel pin weldings are presented [fr

  11. Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

    Science.gov (United States)

    Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

    2018-02-01

    Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

  12. Optical sensor for real-time weld defect detection

    Science.gov (United States)

    Ancona, Antonio; Maggipinto, Tommaso; Spagnolo, Vincenzo; Ferrara, Michele; Lugara, Pietro M.

    2002-04-01

    In this work we present an innovative optical sensor for on- line and non-intrusive welding process monitoring. It is based on the spectroscopic analysis of the optical VIS emission of the welding plasma plume generated in the laser- metal interaction zone. Plasma electron temperature has been measured for different chemical species composing the plume. Temperature signal evolution has been recorded and analyzed during several CO2-laser welding processes, under variable operating conditions. We have developed a suitable software able to real time detect a wide range of weld defects like crater formation, lack of fusion, excessive penetration, seam oxidation. The same spectroscopic approach has been applied for electric arc welding process monitoring. We assembled our optical sensor in a torch for manual Gas Tungsten Arc Welding procedures and tested the prototype in a manufacturing industry production line. Even in this case we found a clear correlation between the signal behavior and the welded joint quality.

  13. New design environment for defect detection in web inspection systems

    Science.gov (United States)

    Hajimowlana, S. Hossain; Muscedere, Roberto; Jullien, Graham A.; Roberts, James W.

    1997-09-01

    One of the aims of industrial machine vision is to develop computer and electronic systems destined to replace human vision in the process of quality control of industrial production. In this paper we discuss the development of a new design environment developed for real-time defect detection using reconfigurable FPGA and DSP processor mounted inside a DALSA programmable CCD camera. The FPGA is directly connected to the video data-stream and outputs data to a low bandwidth output bus. The system is targeted for web inspection but has the potential for broader application areas. We describe and show test results of the prototype system board, mounted inside a DALSA camera and discuss some of the algorithms currently simulated and implemented for web inspection applications.

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

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2016-10-01

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

  15. Permeated defect detecting test method and device in reactor

    International Nuclear Information System (INIS)

    Sakurai, Yoshishige.

    1996-01-01

    The present invention provides a method of and a device capable of performing a test for entire inner surfaces of the reactor upon periodical inspection of a BWR type reactor while sufficiently taking countermeasures for radiation rays into consideration. Namely, the present invention comprises following steps. (1) A provisional step for taking a shroud head of a reactor core shroud and incore structural components above and below the shroud out of the reactor, discharging reactor water and water tightly closing openings such as reactor wall perforation holes, (2) a pretreatment step for washing exposed inner surfaces of the reactor and peeling deteriorated materials, (3) a first drying step for drying portions washed and peeled in the step (2), (4) a permeation step for applying a permeation liquid of a defect detecting medium on the exposed inner surfaces of the reactor, (5) a permeation liquid removing step for removing the an excess permeation liquid in the step (4), (6) a second drying step for drying corresponding portions after performing the step (5), and (7) a flaw detecting step for optically observing the corresponding portions after performing the step (6) and detecting flaws. (I.S.)

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

    Directory of Open Access Journals (Sweden)

    Yazan M. Alomari

    2014-01-01

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

  17. Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma.

    Science.gov (United States)

    Panda, Rashmi; Puhan, N B; Rao, Aparna; Padhy, Debananda; Panda, Ganapati

    2018-06-01

    Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles. The true boundary pixels are classified using random forest trained by newly proposed cumulative zero count local binary pattern (CZC-LBP) and directional differential energy (DDE) along with Shannon, Tsallis entropy and intensity features. Finally, the RNFLD angular width is obtained by random sample consensus (RANSAC) line fitting on the detected set of boundary pixels. The proposed method is found to achieve high RNFLD detection performance on a newly created dataset with sensitivity (SN) of 0.7821 at 0.2727 false positives per image (FPI) and the area under curve (AUC) value is obtained as 0.8733. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. 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...

  19. Interdependence between measures of extent and severity of myocardial perfusion defects provided by automatic quantification programs

    DEFF Research Database (Denmark)

    El-Ali, Henrik Hussein; Palmer, John; Carlsson, Marcus

    2005-01-01

    To evaluate the accuracy of the values of lesion extent and severity provided by the two automatic quantification programs AutoQUANT and 4D-MSPECT using myocardial perfusion images generated by Monte Carlo simulation of a digital phantom. The combination between a realistic computer phantom and a...

  20. Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count

    NARCIS (Netherlands)

    Kamphuis, C.; Sherlock, R.; Jago, J.; Mein, G.; Hogeveen, H.

    2008-01-01

    This study explored the potential value of in-line composite somatic cell count (ISCC) sensing as a sole criterion or in combination with quarter-based electrical conductivity (EC) of milk, for automatic detection of clinical mastitis (CM) during automatic milking. Data generated from a New Zealand

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

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

    NARCIS (Netherlands)

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

    2015-01-01

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

  3. On advisability of developing automatic complexes of radiation flow detection

    International Nuclear Information System (INIS)

    Akopov, V.S.; Voronin, S.A.; Meshalkin, I.A.

    1976-01-01

    On the basis of mathematical treatment of statistical data obtained by inquest of specialists from a number of factories, problems associated with the determination of the most acceptable efficiency of radiation defectoscopy automatized complexes are considered. Production requirements for radiation control sensitivity are generalized. The use of providing the complexes with computer technique is substantiated

  4. Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

    Science.gov (United States)

    Osmanoglu, B.; Ozkan, C.; Sunar, F.

    2013-10-01

    After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

  5. Detection of defects of Kenaf/Epoxy by Thermography Analyses

    International Nuclear Information System (INIS)

    Suriani, M J; Ali, Aidi; Sapuan, S M; Khalina, A; Abdullah, S

    2012-01-01

    There are quite a few defects can occur due to manufacturing of the composites such as voids, resin-rich zones, pockets of undispersed cross-linker, misaligned fibres and regions where resin has poorly wetted the fibres. Such defect can reduce the mechanical properties as well mechanical performance of the structure and thus must be determine. In this study, the defect of Kenaf/epoxy reinforced composite materials has been determined by thermography analyses and mechanical properties testing of the composites have been done by tensile test. 95% of the thermography analyses have proved that the defects occur in the composite has reduced the mechanical properties of the specimens.

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

    Energy Technology Data Exchange (ETDEWEB)

    2018-03-25

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

  7. Automatic cough episode detection using a vibroacoustic sensor.

    Science.gov (United States)

    Mlynczak, Marcel; Pariaszewska, Katarzyna; Cybulski, Gerard

    2015-08-01

    Cough monitoring is an important element of the diagnostics of respiratory diseases. The European Respiratory Society recommends objective assessment of cough episodes and the search for methods of automatic analysis to make obtaining the quantitative parameters possible. The cough "events" could be classified by a microphone and a sensor that measures the vibrations of the chest. Analysis of the recorded signals consists of calculating the features vectors for selected episodes and of performing automatic classification using them. The aim of the study was to assess the accuracy of classification based on an artificial neural networks using vibroacoustic signals collected from chest. Six healthy, young men and eight healthy, young women carried out an imitated cough, hand clapping, speech and shouting. Three methods of parametrization were used to prepare the vectors of episode features - time domain, time-frequency domain and spectral modeling. We obtained the accuracy of 95% using artificial neural networks.

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

    Science.gov (United States)

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

    1999-06-01

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

  9. Artificial intelligence and ultrasonic tests in detection of defects

    International Nuclear Information System (INIS)

    Barrera Cardiel, G.; Fabian Alvarez, M. a.; Velez Martinez, M.; Villasenor, L.

    2001-01-01

    One of the most serious problems in the quality control of welded unions is the location, identification and classification of defects. As a solution to this problem, a technique for classification, applicable to welded unions done by electric arc welding as well as by friction, is proposed; it is based on ultrasonic signals. The neuronal networks proposed are Kohonen and Multilayer Percept ron, all in a virtual instrument environment. Currently the techniques most used in this field are: radiological analysis (X-rays) and ultrasonic analysis (ultrasonic waves). The X-ray technique in addition to being dangerous requires highly specialized personnel and equipment, therefore its use is restricted. The ultrasonic technique, in spite of being one of the most used for detection of discontinuities, requires personnel with wide experience in the interpretation of ultrasonic signals, this is a time-consuming process which necessarily increases its operation cost. The classification techniques that we propose turn out to be safe, reliable, inexpensive and easy to implement for the solution of this important problem. (Author) 8 refs

  10. Non destructive defect detection by spectral density analysis.

    Science.gov (United States)

    Krejcar, Ondrej; Frischer, Robert

    2011-01-01

    The potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or under unexpected situations. Another option is to create an artificial pulse, which can help us to determine the actual state of the diagnosed device. The main idea of this method is based on the analysis of the current power spectrum density of the received signal and its postprocessing in the Matlab environment with a following sample comparison in the Statistica software environment. The last step, which is comparison of samples, is the most important, because it is possible to determine the status of the examined object at a given time. Nowadays samples are compared only visually, but this method can't produce good results. Further the presented filter can choose relevant data from a huge group of data, which originate from applying FFT (Fast Fourier Transform). On the other hand, using this approach they can be subjected to analysis with the assistance of a neural network. If correct and high-quality starting data are provided to the initial network, we are able to analyze other samples and state in which condition a certain object is. The success rate of this approximation, based on our testing of the solution, is now 85.7%. With further improvement of the filter, it could be even greater. Finally it is possible to detect defective conditions or upcoming limiting states of examined objects/materials by using only one device which contains HW and SW parts. This kind of detection can provide significant financial savings in certain cases (such as continuous casting of iron where it could save hundreds of thousands of USD).

  11. Influence of the geometrical soft effect on the radiographic detection of artificial defects

    International Nuclear Information System (INIS)

    Bodson, F.; Launay, J.P.

    1980-11-01

    The influence of the geometrical soft effect on image quality and on the sensitivity to detection of artificial defects has been assessed for radiographies achieved with Ir 192 and Co 60 sources. The results show that the threshold of detectability of defects depends increasingly on the geometrical soft effects as the thickness radiographed becomes greater and that the defects in question are finite. The image quality remains accepable on the whole [fr

  12. Optimal Fluorescence Waveband Determination for Detecting Defective Cherry Tomatoes Using a Fluorescence Excitation-Emission Matrix

    Directory of Open Access Journals (Sweden)

    In-Suck Baek

    2014-11-01

    Full Text Available A multi-spectral fluorescence imaging technique was used to detect defective cherry tomatoes. The fluorescence excitation and emission matrix was used to measure for defects, sound surface and stem areas to determine the optimal fluorescence excitation and emission wavelengths for discrimination. Two-way ANOVA revealed the optimal excitation wavelength for detecting defect areas was 410 nm. Principal component analysis (PCA was applied to the fluorescence emission spectra of all regions at 410 nm excitation to determine the emission wavelengths for defect detection. The major emission wavelengths were 688 nm and 506 nm for the detection. Fluorescence images combined with the determined emission wavebands demonstrated the feasibility of detecting defective cherry tomatoes with >98% accuracy. Multi-spectral fluorescence imaging has potential utility in non-destructive quality sorting of cherry tomatoes.

  13. Automatic pitch detection for a computer game interface

    International Nuclear Information System (INIS)

    Fonseca Solis, Juan M.

    2015-01-01

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

  14. Automatic multimodal detection for long-term seizure documentation in epilepsy.

    Science.gov (United States)

    Fürbass, F; Kampusch, S; Kaniusas, E; Koren, J; Pirker, S; Hopfengärtner, R; Stefan, H; Kluge, T; Baumgartner, C

    2017-08-01

    This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  15. Defect detection in conducting materials using eddy current testing techniques

    Directory of Open Access Journals (Sweden)

    Brauer Hartmut

    2014-01-01

    Full Text Available Lorentz force eddy current testing (LET is a novel nondestructive testing technique which can be applied preferably to the identification of internal defects in nonmagnetic moving conductors. The LET is compared (similar testing conditions with the classical eddy current testing (ECT. Numerical FEM simulations have been performed to analyze the measurements as well as the identification of internal defects in nonmagnetic conductors. The results are compared with measurements to test the feasibility of defect identification. Finally, the use of LET measurements to estimate of the electrical conductors under test are described as well.

  16. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

  17. Digital detection system of surface defects for large aperture optical elements

    International Nuclear Information System (INIS)

    Fan Yong; Chen Niannian; Gao Lingling; Jia Yuan; Wang Junbo; Cheng Xiaofeng

    2009-01-01

    Based on the light defect images against the dark background in a scattering imaging system, a digital detection system of surface defects for large aperture optical elements has been presented. In the system, the image is segmented by a multi-area self-adaptive threshold segmentation method, then a pixel labeling method based on replacing arrays is adopted to extract defect features quickly, and at last the defects are classified through back-propagation neural networks. Experiment results show that the system can achieve real-time detection and classification. (authors)

  18. The Application of Helicopter Rotor Defect Detection Using Wavelet Analysis and Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Jin-Li Sun

    2014-06-01

    Full Text Available When detect the helicopter rotor beam with ultrasonic testing, it is difficult to realize the noise removing and quantitative testing. This paper used the wavelet analysis technique to remove the noise among the ultrasonic detection signal and highlight the signal feature of defect, then drew the curve of defect size and signal amplitude. Based on the relationship of defect size and signal amplitude, a BP neural network was built up and the corresponding estimated value of the simulate defect was obtained by repeating training. It was confirmed that the wavelet analysis and neural network technique met the requirements of practical testing.

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

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

    OpenAIRE

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

    2008-01-01

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

  1. 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.

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

    NARCIS (Netherlands)

    Bouma, H.; Baan, J.; Burghouts, G.J.; Eendebak, P.T.; Huis, J.R. van; Dijk, J.; Rest, J.H.C. van

    2014-01-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature

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

    Science.gov (United States)

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

    2014-05-01

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

  5. Differences between automatically detected and steady-state fractional flow reserve.

    Science.gov (United States)

    Härle, Tobias; Meyer, Sven; Vahldiek, Felix; Elsässer, Albrecht

    2016-02-01

    Measurement of fractional flow reserve (FFR) has become a standard diagnostic tool in the catheterization laboratory. FFR evaluation studies were based on pressure recordings during steady-state maximum hyperemia. Commercially available computer systems detect the lowest Pd/Pa ratio automatically, which might not always be measured during steady-state hyperemia. We sought to compare the automatically detected FFR and true steady-state FFR. Pressure measurement traces of 105 coronary lesions from 77 patients with intermediate coronary lesions or multivessel disease were reviewed. In all patients, hyperemia had been achieved by intravenous adenosine administration using a dosage of 140 µg/kg/min. In 42 lesions (40%) automatically detected FFR was lower than true steady-state FFR. Mean bias was 0.009 (standard deviation 0.015, limits of agreement -0.02, 0.037). In 4 lesions (3.8%) both methods lead to different treatment recommendations, in all 4 cases instantaneous wave-free ratio confirmed steady-state FFR. Automatically detected FFR was slightly lower than steady-state FFR in more than one-third of cases. Consequently, interpretation of automatically detected FFR values closely below the cutoff value requires special attention.

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

    NARCIS (Netherlands)

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

    2000-01-01

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

  7. Defect Detection in Superconducting Radiofrequency Cavity Surface Using C + + and OpenCV

    Science.gov (United States)

    Oswald, Samantha; Thomas Jefferson National Accelerator Facility Collaboration

    2014-03-01

    Thomas Jefferson National Accelerator Facility (TJNAF) uses superconducting radiofrequency (SRF) cavities to accelerate an electron beam. If theses cavities have a small particle or defect, it can degrade the performance of the cavity. The problem at hand is inspecting the cavity for defects, little bubbles of niobium on the surface of the cavity. Thousands of pictures have to be taken of a single cavity and then looked through to see how many defects were found. A C + + program with Open Source Computer Vision (OpenCV) was constructed to reduce the number of hours searching through the images and finds all the defects. Using this code, the SRF group is now able to use the code to identify defects in on-going tests of SRF cavities. Real time detection is the next step so that instead of taking pictures when looking at the cavity, the camera will detect all the defects.

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

    Science.gov (United States)

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

    2017-04-01

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

  9. Link Between RI-ISI and Inspection Qualification: Relationship between Defect Detection Rate and Margin of Detection

    International Nuclear Information System (INIS)

    Shepherd, Barrie; Goujon, Sophie; Whittle, John

    2007-01-01

    Quantitative risk-informed in-service inspection (RI-ISI) requires a quantitative measurement of inspection effectiveness if the risk change associated with an inspection is to be determined. Knowing the probability of detection (POD) as a function of defect depth (through wall dimension) would provide ideal information. However the main in-service inspection method for nuclear plant is ultrasonics, for which defect detection capability depends on a wide variety of parameters besides defect depth, such as defect orientation, roughness, location, shape etc. In recognition of this the European approach to inspection qualification is generally based on some combination of technical justification, and practical trials on a relatively limited number of defects. This inspection qualification process involves demonstrating that defects of concern will generate responses in excess of the specified recording level or noise, depending on the inspection. It is not currently designed to quantify the probability with which defects will be detected. The work described in this report has been performed in order to help address the problem of how the information generated during inspection qualification can be used as an input for RI-ISI. The approach adopted has been to recognise that as the defect response increases above the recording or noise level, the probability of detecting defects is likely to increase. The work therefore involved an investigation of the relationship between POD (strictly speaking defect detection rate) and margin of detection. It involved blind manual and automated ultrasonic trials on artificial defects in test plates designed to generate a range of signal responses. The detection rate for defects which provided signals at a particular level above noise or above a recording level was then measured. A relationship between defect detection rate and margin of detection has been established based on these trials. In addition to establishing a stronger link

  10. Automatic Detection of Cyberbullying in Social Media Text

    NARCIS (Netherlands)

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

    2018-01-01

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

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

    National Research Council Canada - National Science Library

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

    2006-01-01

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

  12. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

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

    2015-01-01

    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

  13. Woven fabric defects detection based on texture classification algorithm

    International Nuclear Information System (INIS)

    Ben Salem, Y.; Nasri, S.

    2011-01-01

    In this paper we have compared two famous methods in texture classification to solve the problem of recognition and classification of defects occurring in a textile manufacture. We have compared local binary patterns method with co-occurrence matrix. The classifier used is the support vector machines (SVM). The system has been tested using TILDA database. The results obtained are interesting and show that LBP is a good method for the problems of recognition and classifcation defects, it gives a good running time especially for the real time applications.

  14. Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image

    Science.gov (United States)

    Neogi, Nirbhar; Mohanta, Dusmanta K.; Dutta, Pranab K.

    2017-12-01

    Steel strips are used extensively for white goods, auto bodies and other purposes where surface defects are not acceptable. On-line surface inspection systems can effectively detect and classify defects and help in taking corrective actions. For detection of defects use of gradients is very popular in highlighting and subsequently segmenting areas of interest in a surface inspection system. Most of the time, segmentation by a fixed value threshold leads to unsatisfactory results. As defects can be both very small and large in size, segmentation of a gradient image based on percentile thresholding can lead to inadequate or excessive segmentation of defective regions. A global adaptive percentile thresholding of gradient image has been formulated for blister defect and water-deposit (a pseudo defect) in steel strips. The developed method adaptively changes the percentile value used for thresholding depending on the number of pixels above some specific values of gray level of the gradient image. The method is able to segment defective regions selectively preserving the characteristics of defects irrespective of the size of the defects. The developed method performs better than Otsu method of thresholding and an adaptive thresholding method based on local properties.

  15. 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.

  16. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

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

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

    Science.gov (United States)

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

    2016-08-01

    A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer's gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ=0.516), while the Automatic Analysis showed that the computer program's accuracy was 74.5% (κ=0.492), both of which were higher than chance of 50% (κ=0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. 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.

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

    Science.gov (United States)

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

    2013-11-01

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

  20. Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data.

    Science.gov (United States)

    Yu, Yong-Jie; Xia, Qiao-Ling; Wang, Sheng; Wang, Bing; Xie, Fu-Wei; Zhang, Xiao-Bing; Ma, Yun-Ming; Wu, Hai-Long

    2014-09-12

    Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Development of eddy current sensor for detecting defect on ferromagnetic material

    International Nuclear Information System (INIS)

    Choi, Duck Su; Lee, Hyang Beom

    2002-01-01

    In this paper, the eddy current sensor is developed for observing the ability of detecting defect on ferromagnetic material with variation of frequency and velocity. In order to research the characteristics on eddy current sensor. The circuit which is designed for processing detected voltage is developed and differential frequency is used for eddy current sensor to detect defect with variation of frequency. The ability of eddy current sensor to detect defects is studied with variation of velocity adjusted by rotating the circular plate. This study shows that the ability of eddy current sensor for detecting defect is increased and decreased by frequency. This fact means that the sensor has its best ability at a certain frequency. And the ability of eddy current sensor by velocity is decreased by increased velocity. Therefore, the eddy current sensor has to be developed with consideration of its operation velocity and frequency.

  2. Automatic detection of regions of interest in mammographic images

    Science.gov (United States)

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

    2011-03-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

  4. speed related defect detection in a seta 4-ball life testing machine

    African Journals Online (AJOL)

    Dr Obe

    1987-09-01

    Sep 1, 1987 ... detection of incipient defect in a Seta mechanism is investigated using a number of ... improved if the diagnostic inspection of the mechanism is carried out at ..... frequency change with defect ... 8. Noll, A.M. "Cepstrum Pitch ...

  5. Automatic detection and analysis of nuclear plant malfunctions

    International Nuclear Information System (INIS)

    Bruschi, R.; Di Porto, P.; Pallottelli, R.

    1985-01-01

    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 TREE-CROWN DETECTION IN CHALLENGING SCENARIOS

    Directory of Open Access Journals (Sweden)

    D. Bulatov

    2016-06-01

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

  7. Multislice CT coronary angiography: evaluation of an automatic vessel detection tool

    International Nuclear Information System (INIS)

    Dewey, M.; Schnapauff, D.; Lembcke, A.; Hamm, B.; Rogalla, P.; Laule, M.; Borges, A.C.; Rutsch, W.

    2004-01-01

    Purpose: To investigate the potential of a new detection tool for multisliceCT (MSCT) coronary angiography with automatic display of curved multiplanar reformations and orthogonal cross-sections. Materials and Methods: Thirty-five patients were consecutively enrolled in a prospective intention-to-diagnose study and examined using a MSCT scanner with 16 x 0.5 mm detector collimation and 400 ms gantry rotation time (Aquilion, Toshiba). A multisegment algorithm using up to four segments was applied for ECG-gated reconstruction. Automatic and manual detection of coronary arteries was conducted using the coronary artery CT protocol of a workstation (Vitrea 2, Version 3.3, Vital Images) to detect significant stenoses (≥50%) in all segments of ≥1.5 mm in diameter. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of conventional coronary angiography. Results: The overall sensitivity, specificity, nondiagnostic rate, and accuracy of the automatic and manual approach were 90 vs. 94%, 89 vs. 84%, 6 vs. 6%, and 89 vs. 88%, respectively (p=n.s.). The vessel length detected with the automatic and manual approach were highly correlated for the left main/left anterior descending (143±30 vs. 146±24 mm, r=0.923, p [de

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

    Directory of Open Access Journals (Sweden)

    Maíla de Lima Claro

    2016-08-01

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

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

    NARCIS (Netherlands)

    Jennekens, W.; Ruijs, L.S.; Lommen, Ch.M.L.; Niemarkt, H.J.; Pasman, J.W.; van Kranen-Mastenbroek, V.H.J.M.; Wijn, P.F.F.; van Pul, C.; Andriessen, P.

    2011-01-01

    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

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

    NARCIS (Netherlands)

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

    2013-01-01

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

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

    African Journals Online (AJOL)

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

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

    Science.gov (United States)

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

    2017-06-26

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

  13. Detection of small surface defects using DCT based enhancement approach in machine vision systems

    Science.gov (United States)

    He, Fuqiang; Wang, Wen; Chen, Zichen

    2005-12-01

    Utilizing DCT based enhancement approach, an improved small defect detection algorithm for real-time leather surface inspection was developed. A two-stage decomposition procedure was proposed to extract an odd-odd frequency matrix after a digital image has been transformed to DCT domain. Then, the reverse cumulative sum algorithm was proposed to detect the transition points of the gentle curves plotted from the odd-odd frequency matrix. The best radius of the cutting sector was computed in terms of the transition points and the high-pass filtering operation was implemented. The filtered image was then inversed and transformed back to the spatial domain. Finally, the restored image was segmented by an entropy method and some defect features are calculated. Experimental results show the proposed small defect detection method can reach the small defect detection rate by 94%.

  14. Fuel defect detection, localization and removal in Bruce Power units 3 through 8

    International Nuclear Information System (INIS)

    Stone, R.; Armstrong, J.; Iglesias, F.; Oduntan, R.; Lewis, B.

    2005-01-01

    Fuel element defects are occurring in Bruce 'A' and Bruce 'B' Units. A root-cause investigation is ongoing, however, a solution is not yet in-hand. Fuel defect management efforts have been undertaken, therefore, in the interim. Fuel defect management tools are in-place for all Bruce Units. These tools can be categorized as analysis-based or operations-based. Analysis-based tools include computer codes used primarily for fuel defect characterization, while operations-based tools include Unit-specific delayed-neutron ('DN') monitoring systems and gaseous fission product ('GFP') monitoring systems. Operations-based tools are used for fuel defect detection, localization and removal activities. Fuel and Physics staff use defect detection, localization and removal methodologies and guidelines to disposition fuel defects. Methodologies are 'standardized' or 'routine' procedures for implementing analysis-based and operations-based tools to disposition fuel defects during Unit start-up operation and during operation at high steady-state power levels. Guidelines at present serve to supplement fuel defect management methodologies during Unit power raise. (author)

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

    Science.gov (United States)

    Shamir, Lior; Manning, Saundra; Wallin, John

    2014-09-01

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

  16. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

    Tomei-Le-Digarcher, Sandrine

    2009-01-01

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

  17. Automatic car driving detection using raw accelerometry data.

    Science.gov (United States)

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

    2016-09-21

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

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

    Science.gov (United States)

    Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

    2011-10-01

    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.

  19. Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

    Science.gov (United States)

    Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

    2007-11-01

    Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    International Nuclear Information System (INIS)

    Lu, Shan; Eiho, Shigeru

    1992-01-01

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

  4. Prospects of closed-circuit television in detecting surface defects

    International Nuclear Information System (INIS)

    Kaisler, L. et al.

    The use is discussed of closed-circuit television for optical in-service testing of surface defects of nuclear reactors. Experience gained by UJV Rez with in-service testing of the WWR-S reactor is briefly reported. Main attention is devoted to recognizability of defects and to determining the fundamental conditions of the applicability and limitations of the closed-circuit television method. In experiments, resolution of the method was tested and the role of the human factor was assessed in evaluating the results. The need was stressed of thorough training of operators. Based on the experiments conducted, considerations are presented regarding modifications of the individual elements of the tv chain aimed at improved quality of information and a limited role of the observer. (B.S.)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-06-15

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

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

    International Nuclear Information System (INIS)

    Qiu, J; Yang, D

    2015-01-01

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

  7. Rapid surface defect detection based on singular value decomposition using steel strips as an example

    Science.gov (United States)

    Sun, Qianlai; Wang, Yin; Sun, Zhiyi

    2018-05-01

    For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.

  8. Defect detection of wall thinning defect in pipes using lock-in photo-infrared thermography technique

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Su Ok; Park, Jong Hyun; Choi, Tae Ho; Jung, Hyun Chul; Kim, Kyoung Suk [Chosun Univ., Gwangju (Korea, Republic of)

    2008-07-01

    Piping in the Nuclear Power plants (NPP) are mostly consisted of carbon steel pipe. The wall thinning defect is mainly occurred by the affect of the Flow Accelerated Corrosion (FAC) of fluid which flows in carbon steel pipes. This type of defect becomes the cause of damage or destruction of piping. Therefore, it is very important to measure defect which is existed not only on the welding partbut also on the whole field of pipe. Over the years, Infrared Thermography (IRT) has been used as a non destructive testing methods of the various kinds of materials. This technique has many merits and applied to the industrial field but has limitation to the materials. Therefore, this method was combined with lock-in technique. So IRT detection resolution has been progressively improved using lock-in technique. In this paper, the quantitative analysis results of the location and the size of wall thinning defect that is artificially processed inside the carbon steel pipe by using IRT are obtained.

  9. Detection of structural defects in lecithin membranes by the small-angle neutron scattering method

    International Nuclear Information System (INIS)

    Bezzabotnov, V.Yu.; Gordelij, V.I.; Ostanevich, Yu.M.; Yaguzhinskij, L.S.

    1989-01-01

    Irregularities interpreted as interdomain defects have been detected in model lipid membranes of dipalmitoil lecithin in liquid L α -phase by the method of small-angle scattering (lateral diffraction). The dimensions and concentrations of the defects were about those supposed within the dynamic cluster model of bilayer (Ivkov, 1984). No irregularities were detected in the solid Lβ ' -phase (the diffusion scattering intensity was at least ten times less)

  10. Application of Ultrasonic Phased Array Technology to the Detection of Defect in Composite Stiffened-structures

    Science.gov (United States)

    Zhou, Yuan-Qi; Zhan, Li-Hua

    2016-05-01

    Composite stiffened-structure consists of the skin and stringer has been widely used in aircraft fuselage and wings. The main purpose of the article is to detect the composite material reinforced structure accurately and explore the relationship between defect formation and structural elements or curing process. Based on ultrasonic phased array inspection technology, the regularity of defects in the manufacture of composite materials are obtained, the correlation model between actual defects and nondestructive testing are established. The article find that the forming quality of deltoid area in T-stiffened structure is obviously improved by pre-curing, the defects of hat-stiffened structure are affected by the mandrel. The results show that the ultrasonic phased array inspection technology can be an effectively way for the detection of composite stiffened-structures, which become an important means to control the defects of composite and improve the quality of the product.

  11. Wafer defect detection by a polarization-insensitive external differential interference contrast module.

    Science.gov (United States)

    Nativ, Amit; Feldman, Haim; Shaked, Natan T

    2018-05-01

    We present a system that is based on a new external, polarization-insensitive differential interference contrast (DIC) module specifically adapted for detecting defects in semiconductor wafers. We obtained defect signal enhancement relative to the surrounding wafer pattern when compared with bright-field imaging. The new DIC module proposed is based on a shearing interferometer that connects externally at the output port of an optical microscope and enables imaging thin samples, such as wafer defects. This module does not require polarization optics (such as Wollaston or Nomarski prisms) and is insensitive to polarization, unlike traditional DIC techniques. In addition, it provides full control of the DIC shear and orientation, which allows obtaining a differential phase image directly on the camera (with no further digital processing) while enhancing defect detection capabilities, even if the size of the defect is smaller than the resolution limit. Our technique has the potential of future integration into semiconductor production lines.

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

    Science.gov (United States)

    Sa, Qila; Wang, Zhihui

    2018-03-01

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

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

    International Nuclear Information System (INIS)

    Lu Wei; Nystrom, Michelle M.; Parikh, Parag J.; Fooshee, David R.; Hubenschmidt, James P.; Bradley, Jeffrey D.; Low, Daniel A.

    2006-01-01

    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

  14. Detection of defects in electron-irradiated synthetic silica quartz probed by positron annihilation

    International Nuclear Information System (INIS)

    Watauchi, Satoshi; Uedono, Akira; Ujihira, Yusuke; Yoda, Osamu.

    1994-01-01

    Defects in amorphous SiO 2 films, formed on MOS(metal/oxide/semiconductor) devices as gates, perturb its operation. The positron annihilation techniques, were applied to the study of the annealing behavior of the defects, introduced in the high purity synthetic quartz glass by the irradiation of 3-MeV electrons up to the 1x10 18 e - /cm 2 dosage. It was proved that the positron annihilation techniques were sufficiently sensitive to detect the defects in the electron-irradiated silica glasses. Three types of open-space defects were detected by the positron lifetime measurements. These can be attributed to monovacancy or divacancy type defects, vacancy clusters, and open-volume defects. A high formation probability (∼90%) of positroniums(Ps) was found in unirradiated specimens. These Ps were considered to be formed in open-volume defects. The formation probability of Ps was drastically decreased by the electron irradiation. But the size of open-volume defects was kept unchanged by the irradiation. These facts suggest that vacancy-type defects were introduced by the electron irradiation and that positrons were trapped in these defects. By the isochronal annealing in nitrogen atmosphere, the lifetime component (τ 2 ) and its relative intensity (I 2 ), attributed to positrons trapped in monovacancy or divacancy type defects and annihilated there, changed remarkably. τ 2 was constant in the temperature range up to 300degC, getting slightly shorter between 300degC and 700degC, and constant above 700degC. I 2 decreased gradually up to 300degC, constant between 300degC and 550degC, decreased above 550degC, and constant above 700degC. This revealed that the behavior of the defects, in which positrons were trapped, change by the elevation of the annealing temperature. (author)

  15. Automatic detection of mycobacterium tuberculosis using artificial intelligence

    Science.gov (United States)

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

    2018-01-01

    Background Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. Methods We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Results Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. Conclusions TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified. PMID:29707349

  16. Automatic detection of mycobacterium tuberculosis using artificial intelligence.

    Science.gov (United States)

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

    2018-03-01

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

  17. 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.

  18. 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.

  19. A Novel Method for Surface Defect Detection of Photovoltaic Module Based on Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Xuewu Zhang

    2013-01-01

    Full Text Available This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.

  20. Final report on the UKAEA defect detection trials on test pieces 3 and 4. December 1982

    International Nuclear Information System (INIS)

    Lock, D.L.; Cowburn, K.J.

    1983-03-01

    The report follows ND-R-845(R) which described the scope, inspection procedures and results of the defect detection trials on test pieces 3 and 4. The work has been aimed at assessing the ability of non-destructive testing techniques, to detect and size defects near the clad/base metal interface of a flat piece and the inner radius of a PWR inlet nozzle. Following the destructive examination of the test pieces at Ispra, the true sizes of the intended defects are now known. These sizes are compared with that intended and the performance of the inspection teams reviewed. (author)

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

    Science.gov (United States)

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

    2016-02-01

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

  2. 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.

  3. Application of image recognition-based automatic hyphae detection in fungal keratitis.

    Science.gov (United States)

    Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

    2018-03-01

    The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal

  4. A fast button surface defects detection method based on convolutional neural network

    Science.gov (United States)

    Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

    2018-01-01

    Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-05-15

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

  7. 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.

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

    Science.gov (United States)

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

    1993-01-01

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  10. Automatic detection of frequent pronunciation errors made by L2-learners

    NARCIS (Netherlands)

    Truong, K.P.; Neri, A.; Wet, F. de; Cucchiarini, C.; Strik, H.

    2005-01-01

    In this paper, we present an acoustic-phonetic approach to automatic pronunciation error detection. Classifiers using techniques such as Linear Discriminant Analysis and Decision Trees were developed for three sounds that are frequently pronounced incorrectly by L2-learners of Dutch: /a/, /y/ and

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

    NARCIS (Netherlands)

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

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

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

    NARCIS (Netherlands)

    Hogeveen, H.

    2011-01-01

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

  13. Surface and near surface defect detection in thick copper EB-welds using eddy current testing

    International Nuclear Information System (INIS)

    Pitkaenen, J.; Lipponen, A.

    2010-01-01

    The surface inspection of thick copper electron beam (EB) welds plays an important role in the acceptance of nuclear fuel disposal. The main reasons to inspect these components are related to potential manufacturing and handling defects. In this work the data acquisition software, visualising tools for eddy current (EC) measurements and eddy current sensors were developed for detection of unwanted defects. The eddy current equipment was manufactured by IZFP and the visualising software in active co-operation with Posiva and IZFP for the inspections. The inspection procedure was produced during the development of the inspection techniques. The inspection method development aims to qualify the method for surface and near surface defect detection and sizing according to ENIQ. The study includes technical justification to be carried out, and compilation of a defect catalogue and experience from measurements within the Posiva's research on issues related to manufacturing. The depth of penetration in copper components in eddy current testing is rather small. To detect surface breaking defects the eddy current inspection is a good solution. A simple approach was adopted using two techniques: higher frequency was used to detect surface defects and to determine the dimensions of the defects except depth, lower frequency was used to detect defects having a ligament and for sizing of deeper surface breaking defects. The higher frequency was 30 kHz and the lower frequency was 200 Hz. The higher frequency probes were absolute bobbing coils and lower frequency probes combined transmitter - several receiver coils. To evaluate both methods, calibration blocks were manufactured by FNS for weld inspections. These calibration specimens mainly consisted of electron discharge machined notches and holes of varying shapes, lengths and diameters in the range of 1 mm to 20 mm of depth. Also one copper lid specimen with 152 defects was manufactured and used for evaluation of weld inspection

  14. Method for the detection of defective nuclear fuel assemblies

    International Nuclear Information System (INIS)

    Lawrie, W.E.; Womack, R.E.; White, N.W. Jr.

    1978-01-01

    There is applied an ultrasonic transmitter on a tape carrier by means of which the ultrasonic transmitter can be guided underwater between the fuel assemblies. If a fuel assembly is defective, i.e. filled with water, the reflection coefficient at the front interface between cladding and inner space of the fuel assembly will decrease. Essential parts of the ultrasonic signal will move through the liquid and will not be reflected until the backward liquid/metal interface of the fuel assembly. This impulse echo is different from that of the gas-filled fuel assembly. (DG) [de

  15. Detection and depth determination of corrosion defects in embedded bolts using ultrasonic testing technique

    International Nuclear Information System (INIS)

    Lin, Shan; Fukutomi, Hiroyuki; Yuya, Hideki; Ito, Keisuke

    2011-01-01

    A great number of anchor bolts are used to fix various components to concrete foundation in thermal and nuclear power plants. As aging power plants degrade, it is feared that defects resulted from corrosion may occur underground. In this paper, a measurement method utilizing the phased array technique is developed to detect such defects. Measurement results show that this method can detect local and circumferential corrosion defects introduced artificially, but defect echo position appears to be farther away from the bolt head than is actually the case. A finite element simulation of wave propagation shows that longitudinal waves excited by a phased array probe are mode converted and reflected at the defect and at bolt wall, which results in the position of the defect echo appearing to be farther away than the defect actually is. Moreover, an approach for determining the depth of defects using measurement results is also proposed based on numerical results. The depths determined by the proposed approach agree with the actual depths with a maximum error of 1.8 mm and a RMSE of 1.06 mm. (author)

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

    International Nuclear Information System (INIS)

    Verite, J.C.

    1973-01-01

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

  17. Fully automatic detection of corresponding anatomical landmarks in volume scans of different respiratory state

    International Nuclear Information System (INIS)

    Berlinger, Kajetan; Roth, Michael; Sauer, Otto; Vences, Lucia; Schweikard, Achim

    2006-01-01

    A method is described which provides fully automatic detection of corresponding anatomical landmarks in volume scans taken at different respiratory states. The resulting control points are needed for creating a volumetric deformation model for motion compensation in radiotherapy. Prior to treatment two CT volumes are taken, one scan during inhalation, one during exhalation. These scans and the detected control point pairs are taken as input for creating the four-dimensional model by using thin-plate splines

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

    OpenAIRE

    Bouma, H.; Baan, J.; Burghouts, G.J.; Eendebak, P.T.; Huis, J.R. van; Dijk, J.; Rest, J.H.C. van

    2014-01-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snat...

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

    OpenAIRE

    Martini, Alberto; Troncossi, Marco; Rivola, Alessandro

    2015-01-01

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

  20. Methods of the Detection and Identification of Structural Defects in Saturated Metallic Composite Castings

    Directory of Open Access Journals (Sweden)

    Gawdzińska K.

    2017-09-01

    Full Text Available Diagnostics of composite castings, due to their complex structure, requires that their characteristics are tested by an appropriate description method. Any deviation from the specific characteristic will be regarded as a material defect. The detection of defects in composite castings sometimes is not sufficient and the defects have to be identified. This study classifies defects found in the structures of saturated metallic composite castings and indicates those stages of the process where such defects are likely to be formed. Not only does the author determine the causes of structural defects, describe methods of their detection and identification, but also proposes a schematic procedure to be followed during detection and identification of structural defects of castings made from saturated reinforcement metallic composites. Alloys examination was conducted after technological process, while using destructive (macroscopic tests, light and scanning electron microscopy and non-destructive (ultrasonic and X-ray defectoscopy, tomography, gravimetric method methods. Research presented in this article are part of author’s work on castings quality.

  1. Analysis of CFRP Joints by Means of T-Pull Mechanical Test and Ultrasonic Defects Detection.

    Science.gov (United States)

    Casavola, Caterina; Palano, Fania; De Cillis, Francesco; Tati, Angelo; Terzi, Roberto; Luprano, Vincenza

    2018-04-18

    Defects detection within a composite component, with the aim of understanding and predicting its mechanical behavior, is of great importance in the aeronautical field because the irregularities of the composite material could compromise functionality. The aim of this paper is to detect defects by means of non-destructive testing (NDT) on T-pull samples made by carbon fiber reinforced polymers (CFRP) and to evaluate their effect on the mechanical response of the material. Samples, obtained from an industrial stringer having an inclined web and realized with a polymeric filler between cap and web, were subjected to ultrasonic monitoring and then to T-pull mechanical tests. All samples were tested with the same load mode and the same test configuration. An experimental set-up consisting of a semiautomatic C-scan ultrasonic mapping system with a phased array probe was designed and developed, optimizing control parameters and implementing image processing software. The present work is carried out on real composites parts that are characterized by having their intrinsic defectiveness, as opposed to the previous similar results in the literature mainly obtained on composite parts with artificially produced defects. In fact, although samples under study were realized free from defects, ultrasonic mapping found defectiveness inside the material. Moreover, the ultrasonic inspection could be useful in detecting both the location and size of defects. Experimental data were critically analyzed and qualitatively correlated with results of T-pull mechanical tests in order to better understand and explain mechanical behavior in terms of fracture mode.

  2. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study

    Energy Technology Data Exchange (ETDEWEB)

    Roos, A.-M., E-mail: annemarie.roos@gmail.com [Department of Obstetrics and Gynaecology, Mayday University Hospital, Croydon (United Kingdom); Abdool, Z. [Department of Obstetrics and Gynaecology, University of Pretoria, Pretoria (South Africa); Sultan, A.H.; Thakar, R. [Department of Obstetrics and Gynaecology, Mayday University Hospital, Croydon (United Kingdom)

    2011-07-15

    Aim: To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. Materials and methods: One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B and K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. Results: On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Conclusion: Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect.

  3. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study

    International Nuclear Information System (INIS)

    Roos, A.-M.; Abdool, Z.; Sultan, A.H.; Thakar, R.

    2011-01-01

    Aim: To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. Materials and methods: One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B and K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. Results: On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Conclusion: Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect.

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

    International Nuclear Information System (INIS)

    Costa, M.; Moura, L.

    1996-01-01

    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%

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

    Science.gov (United States)

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

    2017-03-01

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

  6. Automatic QRS complex detection using two-level convolutional neural network.

    Science.gov (United States)

    Xiang, Yande; Lin, Zhitao; Meng, Jianyi

    2018-01-29

    The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

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

    International Nuclear Information System (INIS)

    Basner, Mathias; Müller, Uwe; Elmenhorst, Eva-Maria; Kluge, Götz; Griefahn, Barbara

    2008-01-01

    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

  8. Using activity-related behavioural features towards more effective automatic stress detection.

    Directory of Open Access Journals (Sweden)

    Dimitris Giakoumis

    Full Text Available This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.

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

    Directory of Open Access Journals (Sweden)

    Vlad Turcu

    2012-09-01

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

  10. Detection of defects in formed sheet metal using medial axis transformation

    Science.gov (United States)

    Murmu, Naresh C.; Velgan, Roman

    2003-05-01

    In the metal forming processes, the sheet metals are often prone to various defects such as thinning, dents, wrinkles etc. In the present manufacturing environments with ever increasing demand of higher quality, detecting the defects of formed sheet metal using an effective and objective inspection system is the foremost norm to remain competitive in market. The defect detection using optical techniques aspire to satisfy its needs to be non-contact and fast. However, the main difficulties to achieve this goal remain essentially on the development of efficient evaluation technique and accurate interpretation of extracted data. The defect like thinning is detected by evaluating the deviations of the thickness in the formed sheet metal against its nominal value. The present evaluation procedure for determination of thickness applied on the measurements data is not without deficiency. To improve this procedure, a new evaluation approach based on medial axis transformation is proposed here. The formed sheet metals are digitized using fringe projection systems in different orientations, and afterwards registered into one coordinate frame. The medial axis transformation (MAT) is applied on the point clouds, generating the point clouds of MAT. This data is further processed and medial surface is determined. The thinning defect is detected by evaluating local wall thickness and other defects like wrinkles are determined using the shape recognition on the medial surface. The applied algorithm is simple, fast and robust.

  11. P-N defect in GaNP studied by optically detected magnetic resonance

    International Nuclear Information System (INIS)

    Chen, W.M.; Thinh, N.Q.; Vorona, I.P.; Buyanova, I.A.; Xin, H.P.; Tu, C.W.

    2003-01-01

    We provide experimental evidence for an intrinsic defect in GaNP from optically detected magnetic resonance (ODMR). This defect is identified as a P-N complex, exhibiting hyperfine structure due to interactions with a nuclear spin I=((1)/(2)) of one P atom and also a nuclear spin I=1 due to one N atom. The introduction of the defect is assisted by the incorporation of N within the studied N composition range of up to 3.1%, under non-equilibrium growth conditions during gas-source molecular beam epitaxy. The corresponding ODMR spectrum was found to be isotropic, suggesting an A 1 symmetry of the defect state. The localization of the electron wave function at the P-N defect in GaNP is found to be even stronger than that for the isolated P Ga antisite in its parent binary compound GaP

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

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

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

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

    DEFF Research Database (Denmark)

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

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG......, an automatic computerized REM detection algorithm has been implemented, using wavelet packet combined with artificial neural network. Results: When using the EEG, EOG and EMG modalities, it was possible to correctly classify REM sleep with an average Area Under Curve (AUC) equal to 0:900:03 for normal subjects...

  14. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

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

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... 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...

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

    Science.gov (United States)

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

    2018-01-01

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

  16. Generating Impact Maps from Automatically Detected Bomb Craters in Aerial Wartime Images Using Marked Point Processes

    Science.gov (United States)

    Kruse, Christian; Rottensteiner, Franz; Hoberg, Thorsten; Ziems, Marcel; Rebke, Julia; Heipke, Christian

    2018-04-01

    The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

  17. Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

    Science.gov (United States)

    Litkey, P.; Nurminen, K.; Honkavaara, E.

    2013-05-01

    The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

  18. Inspection of Defect Detection Trials Plate 3 by the Materials Physics Department, RNL

    International Nuclear Information System (INIS)

    Rogerson, A.; Poulter, L.N.J.; Dyke, A.V.; Tickle, H.

    1983-11-01

    In January 1982, Risley Nuclear Laboratories (RNL) performed an inspection of Plate 3 of the UKAEA sponsored Defect Detection Trials. A detailed description is given of the ultrasonic techniques and procedures adopted by RNL for this inspection. 0 0 and 70 0 longitudinal twin crystal probes and 70 0 shear probes were used for flaw detection and lateral dimensioning of defects. The time of flight technique was used for through thickness flaw sizing. Comparison is made of the reported inspection results and flaw sizes and locations obtained from destructive examination. All flaws were detected and the reported through thickness sizes were within +- 2 mm of the intended values. (author)

  19. Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

    Directory of Open Access Journals (Sweden)

    Tung-Ching Su

    2014-05-01

    Full Text Available As one of major underground pipelines, sewerage is an important infrastructure in any modern city. The most common problem occurring in sewerage is leaking, whose position and failure level is typically identified through closed circuit television (CCTV inspection in order to facilitate rehabilitation process. This paper proposes a novel method of computer vision, morphological segmentation based on edge detection (MSED, to assist inspectors in detecting pipeline defects in CCTV inspection images. In addition to MSED, other mathematical morphology-based image segmentation methods, including opening top-hat operation (OTHO and closing bottom-hat operation (CBHO, were also applied to the defect detection in vitrified clay sewer pipelines. The CCTV inspection images of the sewer system in the 9th district, Taichung City, Taiwan were selected as the experimental materials. The segmentation results demonstrate that MSED and OTHO are useful for the detection of cracks and open joints, respectively, which are the typical leakage defects found in sewer pipelines.

  20. Research on metallic material defect detection based on bionic sensing of human visual properties

    Science.gov (United States)

    Zhang, Pei Jiang; Cheng, Tao

    2018-05-01

    Due to the fact that human visual system can quickly lock the areas of interest in complex natural environment and focus on it, this paper proposes an eye-based visual attention mechanism by simulating human visual imaging features based on human visual attention mechanism Bionic Sensing Visual Inspection Model Method to Detect Defects of Metallic Materials in the Mechanical Field. First of all, according to the biologically visually significant low-level features, the mark of defect experience marking is used as the intermediate feature of simulated visual perception. Afterwards, SVM method was used to train the advanced features of visual defects of metal material. According to the weight of each party, the biometrics detection model of metal material defect, which simulates human visual characteristics, is obtained.

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

    International Nuclear Information System (INIS)

    Kurozumi, Y.

    2004-01-01

    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.)

  2. Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

    Directory of Open Access Journals (Sweden)

    Raphael Falque

    2017-10-01

    Full Text Available Remote-Field Eddy-Current (RFEC technology is often used as a Non-Destructive Evaluation (NDE method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

  3. Distributed detection and control of defective thermoelectric generation modules using sensor nodes

    DEFF Research Database (Denmark)

    Chen, Min

    2014-01-01

    are described, respectively. Defective and potentially healing conditions are dynamically monitored by a voltage sensor node and a temperature sensor node, both of which can judge the defective TEM and decide the related switching actions in a nearly independent way. The periodical wireless transmission from......To maximize the energy productivity, effective in-field detection and real-time control of defective thermoelectric modules (TEMs) are critical in constituting a thermoelectric generation system (TEGS). In this paper, autonomous and distributed sensor nodes are designed to implement the wireless...... a considerable power improvement is illustrated with the proposed measuring method and setup....

  4. Defect detection and classification of machined surfaces under multiple illuminant directions

    Science.gov (United States)

    Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

    2010-08-01

    Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

  5. Defect detectability of eddy current testing for underwater laser beam welding

    International Nuclear Information System (INIS)

    Ueno, Souichi; Kobayashi, Noriyasu; Ochiai, Makoto; Kasuya, Takashi; Yuguchi, Yasuhiro

    2011-01-01

    We clarified defect detectability of eddy current testing (ECT) as a surface inspection technique for underwater laser beam welding works of dissimilar metal welding (DMW) of reactor vessel nozzle. The underwater laser beam welding procedure includes groove caving as a preparation, laser beam welding in the grooves and welded surface grinding as a post treatment. Therefore groove and welded surface inspections are required in the underwater condition. The ECT is a major candidate as this inspection technique because a penetrant testing is difficult to perform in the underwater condition. Several kinds of experiments were curried out using a cross coil an ECT probe and ECT data acquisition system in order to demonstrate the ECT defect detectability. We used specimens, simulating groove and DMW materials at an RV nozzle, with electro-discharge machining (EDM) slits over it. Additionally, we performed a detection test for artificial stress corrosion cracking (SCC) defects. From these experimental results, we confirmed that an ECT was possible to detect EDM slits 0.3 mm or more in depth and artificial SCC defects 0.02 mm to 0.48 mm in depth on machined surface. Furthermore, the underwater ECT defect detectability is equivalent to that in air. We clarified an ECT is sufficiently usable as a surface inspection technique for underwater laser beam welding works. (author)

  6. Automatic supervision and fault detection of PV systems based on power losses analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chouder, A.; Silvestre, S. [Electronic Engineering Department, Universitat Politecnica de Catalunya, C/Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona (Spain)

    2010-10-15

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L{sub ct}) and miscellaneous capture losses (L{sub cm}). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R{sub C} and R{sub V}. Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally. (author)

  7. Automatic supervision and fault detection of PV systems based on power losses analysis

    International Nuclear Information System (INIS)

    Chouder, A.; Silvestre, S.

    2010-01-01

    In this work, we present a new automatic supervision and fault detection procedure for PV systems, based on the power losses analysis. This automatic supervision system has been developed in Matlab and Simulink environment. It includes parameter extraction techniques to calculate main PV system parameters from monitoring data in real conditions of work, taking into account the environmental irradiance and module temperature evolution, allowing simulation of the PV system behaviour in real time. The automatic supervision method analyses the output power losses, presents in the DC side of the PV generator, capture losses. Two new power losses indicators are defined: thermal capture losses (L ct ) and miscellaneous capture losses (L cm ). The processing of these indicators allows the supervision system to generate a faulty signal as indicator of fault detection in the PV system operation. Two new indicators of the deviation of the DC variables respect to the simulated ones have been also defined. These indicators are the current and voltage ratios: R C and R V . Analysing both, the faulty signal and the current/voltage ratios, the type of fault can be identified. The automatic supervision system has been successfully tested experimentally.

  8. 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.

  9. Particle Swarm Optimization approach to defect detection in armour ceramics.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2017-03-01

    In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

  10. Circular defects detection in welded joints using circular hough transform

    International Nuclear Information System (INIS)

    Hafizal Yazid; Mohd Harun; Shukri Mohd; Abdul Aziz Mohamed; Shaharudin Sayuti; Muhamad Daud

    2007-01-01

    Conventional radiography is one of the common non-destructive testing which employs manual image interpretation. The interpretation is very subjective and depends much on the inspector experience and working conditions. It is therefore useful to have pattern recognition system in order to assist human interpreter in evaluating the quality of the radiograph sample, especially radiographic image of welded joint. This paper describes a system to detect circular discontinuities that is present in the joints. The system utilizes together 2 different algorithms, which is separability filter to identify the best object candidate and Circular Hough Transform to detect the present of circular shape. The result of the experiment shows a promising output in recognition of circular discontinuities in a radiographic image. This is based on 81.82-100% of radiography film with successful circular detection by using template movement of 10 pixels. (author)

  11. Diagnosis of UAV Pitot Tube Defects Using Statistical Change Detection

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens; Adrian, Jens

    2010-01-01

    Unmanned Aerial Vehicles need a large degree of tolerance to faults. One of the most important steps towards this is the ability to detect and isolate faults in sensors and actuators in real time and make remedial actions to avoid that faults develop to failure. This paper analyses the possibilit......Unmanned Aerial Vehicles need a large degree of tolerance to faults. One of the most important steps towards this is the ability to detect and isolate faults in sensors and actuators in real time and make remedial actions to avoid that faults develop to failure. This paper analyses...... the possibilities of detecting faults in the pitot tube of a small unmanned aerial vehicle, a fault that easily causes a crash if not diagnosed and handled in time. Using as redundant information the velocity measured from an onboard GPS receiver, the air-speed estimated from engine throttle and the pitot tube...

  12. Fabric defect detection based on visual saliency using deep feature and low-rank recovery

    Science.gov (United States)

    Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

    2018-04-01

    Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

  13. 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.

  14. Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation.

    Science.gov (United States)

    Vitikainen, Anne-Mari; Mäkelä, Elina; Lioumis, Pantelis; Jousmäki, Veikko; Mäkelä, Jyrki P

    2015-09-30

    The use of navigated repetitive transcranial magnetic stimulation (rTMS) in mapping of speech-related brain areas has recently shown to be useful in preoperative workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver variability and non-optimal replicability of the rTMS results have been reported, and a need for additional development of the methodology is recognized. In TMS motor cortex mappings the evoked responses can be quantitatively monitored by electromyographic recordings; however, no such easily available setup exists for speech mappings. We present an accelerometer-based setup for detection of vocalization-related larynx vibrations combined with an automatic routine for voice onset detection for rTMS speech mapping applying naming. The results produced by the automatic routine were compared with the manually reviewed video-recordings. The new method was applied in the routine navigated rTMS speech mapping for 12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity and 71% specificity. Majority (63%) of the misdetections were related to visible throat movements, extra voices before the response, or delayed naming of the previous stimuli. The no-response errors were correctly detected in 88% of events. The proposed setup for automatic detection of voice onsets provides quantitative additional data for analysis of the rTMS-induced speech response modifications. The objectively defined speech response latencies increase the repeatability, reliability and stratification of the rTMS results. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. [An automatic peak detection method for LIBS spectrum based on continuous wavelet transform].

    Science.gov (United States)

    Chen, Peng-Fei; Tian, Di; Qiao, Shu-Jun; Yang, Guang

    2014-07-01

    Spectrum peak detection in the laser-induced breakdown spectroscopy (LIBS) is an essential step, but the presence of background and noise seriously disturb the accuracy of peak position. The present paper proposed a method applied to automatic peak detection for LIBS spectrum in order to enhance the ability of overlapping peaks searching and adaptivity. We introduced the ridge peak detection method based on continuous wavelet transform to LIBS, and discussed the choice of the mother wavelet and optimized the scale factor and the shift factor. This method also improved the ridge peak detection method with a correcting ridge method. The experimental results show that compared with other peak detection methods (the direct comparison method, derivative method and ridge peak search method), our method had a significant advantage on the ability to distinguish overlapping peaks and the precision of peak detection, and could be be applied to data processing in LIBS.

  16. 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.

  17. Review of candidate methods for detecting incipient defects due to aging of installed cables in nuclear power plants

    International Nuclear Information System (INIS)

    Martzloff, F.D.

    1988-01-01

    Several types of test methods have been proposed for detecting incipient defects due to aging in cable insulation systems, none offering certainty of detecting all possible types of defects. Some methods apply direct detection of a defect in the cable; other methods detect changes in electrical or non-electrical parameters from which inference can be drawn on the integrity of the cable. The paper summarizes the first year of a program conducted at the National Bureau of Standards to assess the potential of success for in situ detection of incipient defects by the most promising of these methods

  18. Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions

    Science.gov (United States)

    Montazeri, Sina; Gisinger, Christoph; Eineder, Michael; Zhu, Xiao xiang

    2018-05-01

    Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

  19. 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.

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

    DEFF Research Database (Denmark)

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

    1995-01-01

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

  1. Probability of defect detection of Posiva's electron beam weld

    International Nuclear Information System (INIS)

    Kanzler, D.; Mueller, C.; Pitkaenen, J.

    2013-12-01

    The report 'Probability of Defect Detection of Posiva's electron beam weld' describes POD curves of four NDT methods radiographic testing, ultrasonic testing, eddy current testing and visual testing. POD-curves are based on the artificial defects in reference blocks. The results are devoted to the demonstration of suitability of the methods for EB weld testing. Report describes methodology and procedure applied by BAM. Report creates a link from the assessment of the reliability and inspection performance to the risk assessment process of the canister final disposal project. Report ensures the confirmation of the basic quality of the NDT methods and their capability to describe the quality of the EB-weld. The probability of detection curves are determined based on the MIL-1823 standard and it's reliability guidelines. The MIL-1823 standard was developed for the determination of integrity of gas turbine engines for the US military. In the POD-process there are determined as a key parameter for the defect detectability the a90/95 magnitudes, i.e. the size measure a of the defect, for which the lower 95 % confidence band crosses the 90 % POD level. By this way can be confirmed that defects with a size of a90/95 will be detected with 90 % probability. In case the experiment will be repeated 5 % might fall outside this confidence limit. (orig.)

  2. Detection and classification of defects in ultrasonic NDE signals using time-frequency representations

    Science.gov (United States)

    Qidwai, Uvais; Costa, Antonio H.; Chen, C. H.

    2000-05-01

    The ultrasonic wave, generated by a piezoelectric transducer coupled to the test specimen, propagates through the material and part of its energy is reflected when it encounters an non-homogeneity or discontinuity in its path, while the remainder is reflected by the back surface of the test specimen. Defect echo signals are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic wave, and are corrupted by additive noise. This leads to difficulties in comparing and analyzing signals, particularly in automated defect identification systems employing different transducers. Further, the multi-component nature of material defects can add to the complexity of the defect identification criteria. With many one-dimensional (1-D) approaches, the multi-component defects can not be detected. Another drawback is that these techniques are not very robust for sharp ultrasonic peaks especially in a very hazardous environment. This paper proposes a technique based on the time-frequency representations (TFRs) of the real defect signals corresponding to artificially produced defects of various geometries in metals. Cohen's class (quadratic) TFRs with Gaussian kernels are then used to represent the signals in the time-frequency (TF) plane. Once the TFR is obtained, various image processing morphological techniques are applied to the TFR (e.g. region of interest masking, edge detection, and profile separation). Based on the results of these operations, a binary image is produced which, in turn, leads to a novel set of features. Using these new features, defects have not only been detected but also classified as flat-cut, angular-cut, and circular-drills. Moreover, with some modifications of the threshold levels of the TFR kernel design, our technique can be used in relatively hostile environments with SNRs as low as 0 dB. Another important characteristic of our approach is the detection of multiple defects. This consists of detection of

  3. Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets

    Directory of Open Access Journals (Sweden)

    B. Téllez

    2008-04-01

    Full Text Available The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation. The resulting maps are then used in the automatic outlier detection in meteorological datasets.

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

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  5. Importance of Defect Detectability in Positron Emission Tomography Imaging of Abdominal Lesions

    International Nuclear Information System (INIS)

    Yamashita, Shozo; Yokoyama, Kunihiko; Onoguchi, Masahisa; Yamamoto, Haruki; Nakaichi, Tetsu; Tsuji, Shiro; Nakajima, Kenichi

    2015-01-01

    This study was designed to assess defect detectability in positron emission tomography (PET) imaging of abdominal lesions. A National Electrical Manufactures Association International Electrotechnical Commission phantom was used. The simulated abdominal lesion was scanned for 10 min using dynamic list-mode acquisition method. Images, acquired with scan duration of 1-10 min, were reconstructed using VUE point HD and a 4.7 mm full-width at half-maximum (FWHM) Gaussian filter. Iteration-subset combinations of 2-16 and 2-32 were used. Visual and physical analyses were performed using the acquired images. To sequentially evaluate defect detectability in clinical settings, we examined two middle-aged male subjects. One had a liver cyst (approximately 10 mm in diameter) and the other suffered from pancreatic cancer with an inner defect region (approximately 9 mm in diameter). In the phantom study, at least 6 and 3 min acquisition durations were required to visualize 10 and 13 mm defect spheres, respectively. On the other hand, spheres with diameters ≥17 mm could be detected even if the acquisition duration was only 1 min. The visual scores were significantly correlated with background (BG) variability. In clinical settings, the liver cyst could be slightly visualized with an acquisition duration of 6 min, although image quality was suboptimal. For pancreatic cancer, the acquisition duration of 3 min was insufficient to clearly describe the defect region. The improvement of BG variability is the most important factor for enhancing lesion detection. Our clinical scan duration (3 min/bed) may not be suitable for the detection of small lesions or accurate tumor delineation since an acquisition duration of at least 6 min is required to visualize 10 mm lesions, regardless of reconstruction parameters. Improvements in defect detectability are important for radiation treatment planning and accurate PET-based diagnosis

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

    Science.gov (United States)

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

    2012-09-01

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

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

    Science.gov (United States)

    Marghany, Maged

    2016-10-01

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

  8. Fault prevention by early stage symptoms detection for automatic vehicle transmission using pattern recognition and curve fitting

    Science.gov (United States)

    Balbin, Jessie R.; Cruz, Febus Reidj G.; Abu, Jon Ervin A.; Siño, Carlo G.; Ubaldo, Paolo E.; Zulueta, Christelle Jianne T.

    2017-06-01

    Automobiles have become essential parts of our everyday lives. It can correlate many factors that may affect a vehicle primarily those which may inconvenient or in some cases harm lives or properties. Thus, focusing on detecting an automatic transmission vehicle engine, body and other parts that cause vibration and sound may help prevent car problems using MATLAB. By using sound, vibration, and temperature sensors to detect the defects of the car and with the help of the transmitter and receiver to gather data wirelessly, it is easy to install on to the vehicle. A technique utilized from Toyota Balintawak Philippines that every car is treated as panels(a, b, c, d, and e) 'a' being from the hood until the front wheel of the car and 'e' the rear shield to the back of the car, this was applied on how to properly place the sensors so that precise data could be gathered. Data gathered would be compared to the normal graph taken from the normal status or performance of a vehicle, data that would surpass 50% of the normal graph would be considered that a problem has occurred. The system is designed to prevent car accidents by determining the current status or performance of the vehicle, also keeping people away from harm.

  9. Radiographic detection of artificial intra-bony defects in the edentulous area.

    Science.gov (United States)

    Van Assche, N; Jacobs, R; Coucke, W; van Steenberghe, D; Quirynen, M

    2009-03-01

    Since intra-bony pathologies might jeopardize implant outcome, their preoperative detection is crucial. In sixteen human cadaver bloc sections from upper and lower jaws, artificial defects with progressively increasing size (n=7) have been created. From each respective defect, analogue and digital intra-oral radiographs were taken, the latter processed via a periodontal filter and afterwards presented in black-white as well as in colour, resulting in three sets of 7 images per bloc section. Eight observers were asked to diagnosis an eventual defect on randomly presented radiographs, and at another occasion to rank each set based on the defect size. The clinicians were only able to identify a defect, when the junctional area was involved, except for bony pieces with a very homogeneous structure. For longitudinal evaluation of healing bone (e.g. after tooth extraction), colour digital images can be recommended. These observations indicate that intra-oral radiographs are not always reliable for the detection of any intra-bony defect.

  10. Sensitive technique for detecting outer defect on tube with remote field eddy current testing

    International Nuclear Information System (INIS)

    Kobayashi, Noriyasu; Nagai, Satoshi; Ochiai, Makoto; Jimbo, Noboru; Komai, Masafumi

    2008-01-01

    In the remote field eddy current testing, we proposed the method of enhancing the magnetic flux density in the vicinity of an exciter coil by controlling the magnetic flux direction for increasing the sensitivity of detecting outer defects on a tube and used the flux guide made of a magnetic material for the method. The optimum structural shape of the flux guide was designed by the magnetic field analysis. On the experiment with the application of the flux guide, the magnetic flux density increased by 59% and the artificial defect detection signal became clear. We confirmed the proposed method was effective in a high sensitivity. (author)

  11. Presentation of the results of a Bayesian automatic event detection and localization program to human analysts

    Science.gov (United States)

    Kushida, N.; Kebede, F.; Feitio, P.; Le Bras, R.

    2016-12-01

    The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has been developing and testing NET-VISA (Arora et al., 2013), a Bayesian automatic event detection and localization program, and evaluating its performance in a realistic operational mode. In our preliminary testing at the CTBTO, NET-VISA shows better performance than its currently operating automatic localization program. However, given CTBTO's role and its international context, a new technology should be introduced cautiously when it replaces a key piece of the automatic processing. We integrated the results of NET-VISA into the Analyst Review Station, extensively used by the analysts so that they can check the accuracy and robustness of the Bayesian approach. We expect the workload of the analysts to be reduced because of the better performance of NET-VISA in finding missed events and getting a more complete set of stations than the current system which has been operating for nearly twenty years. The results of a series of tests indicate that the expectations born from the automatic tests, which show an overall overlap improvement of 11%, meaning that the missed events rate is cut by 42%, hold for the integrated interactive module as well. New events are found by analysts, which qualify for the CTBTO Reviewed Event Bulletin, beyond the ones analyzed through the standard procedures. Arora, N., Russell, S., and Sudderth, E., NET-VISA: Network Processing Vertically Integrated Seismic Analysis, 2013, Bull. Seismol. Soc. Am., 103, 709-729.

  12. NDT detection and quantification of induced defects on composite helicopter rotor blade and UAV wing sections

    Science.gov (United States)

    Findeis, Dirk; Gryzagoridis, Jasson; Musonda, Vincent

    2008-09-01

    Digital Shearography and Infrared Thermography (IRT) techniques were employed to test non-destructively samples from aircraft structures of composite material nature. Background information on the techniques is presented and it is noted that much of the inspection work reviewed in the literature has focused on qualitative evaluation of the defects rather than quantitative. There is however, need to quantify the defects if the threshold rejection criterion of whether the component inspected is fit for service has to be established. In this paper an attempt to quantify induced defects on a helicopter main rotor blade and Unmanned Aerospace Vehicle (UAV) composite material is presented. The fringe patterns exhibited by Digital Shearography were used to quantify the defects by relating the number of fringes created to the depth of the defect or flaw. Qualitative evaluation of defects with IRT was achieved through a hot spot temperature indication above the flaw on the surface of the material. The results of the work indicate that the Shearographic technique proved to be more sensitive than the IRT technique. It should be mentioned that there is "no set standard procedure" tailored for testing of composites. Each composite material tested is more likely to respond differently to defect detection and this depends generally on the component geometry and a suitable selection of the loading system to suit a particular test. The experimental procedure that is reported in this paper can be used as a basis for designing a testing or calibration procedure for defects detection on any particular composite material component or structure.

  13. Device for detecting defective nuclear reactor fuel rods

    International Nuclear Information System (INIS)

    Steven, J.

    1976-01-01

    A moisture sensor is provided for a nuclear fuel rod for water-cooled nuclear reactors wherein moisture can be present. The fuel rod has an end cap and a charge of nuclear fuel. The moisture sensor is disposed between the end cap and the charge and serves to detect a leak in the fuel rod. The moisture sensor includes a capsule-like housing having an inner space and having openings through which moisture can pass into the inner space in the event of a leak in the fuel rod. Ferromagnetic material is disposed in the inner space of the housing together with a moisture detector responsive to moisture for altering the diposition of the ferromagnetic material in the inner space. 5 claims, 6 drawing figures

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

    Directory of Open Access Journals (Sweden)

    О. В. Шишкін

    2013-07-01

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

  15. 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....

  16. Defect-detection algorithm for noncontact acoustic inspection using spectrum entropy

    Science.gov (United States)

    Sugimoto, Kazuko; Akamatsu, Ryo; Sugimoto, Tsuneyoshi; Utagawa, Noriyuki; Kuroda, Chitose; Katakura, Kageyoshi

    2015-07-01

    In recent years, the detachment of concrete from bridges or tunnels and the degradation of concrete structures have become serious social problems. The importance of inspection, repair, and updating is recognized in measures against degradation. We have so far studied the noncontact acoustic inspection method using airborne sound and the laser Doppler vibrometer. In this method, depending on the surface state (reflectance, dirt, etc.), the quantity of the light of the returning laser decreases and optical noise resulting from the leakage of light reception arises. Some influencing factors are the stability of the output of the laser Doppler vibrometer, the low reflective characteristic of the measurement surface, the diffused reflection characteristic, measurement distance, and laser irradiation angle. If defect detection depends only on the vibration energy ratio since the frequency characteristic of the optical noise resembles white noise, the detection of optical noise resulting from the leakage of light reception may indicate a defective part. Therefore, in this work, the combination of the vibrational energy ratio and spectrum entropy is used to judge whether a measured point is healthy or defective or an abnormal measurement point. An algorithm that enables more vivid detection of a defective part is proposed. When our technique was applied in an experiment with real concrete structures, the defective part could be extracted more vividly and the validity of our proposed algorithm was confirmed.

  17. Nondestructive Online Detection of Welding Defects in Track Crane Boom Using Acoustic Emission Technique

    Directory of Open Access Journals (Sweden)

    Yong Tao

    2014-04-01

    Full Text Available Nondestructive detection of structural component of track crane is a difficult and costly problem. In the present study, acoustic emission (AE was used to detect two kinds of typical welding defects, that is, welding porosity and incomplete penetration, in the truck crane boom. Firstly, a subsidiary test specimen with special preset welding defect was designed and added on the boom surface with the aid of steel plates to get the synchronous deformation of the main boom. Then, the AE feature information of the welding defect could be got without influencing normal operation of equipment. As a result, the rudimentary location analysis can be attained using the linear location method and the two kinds of welding defects can be distinguished clearly using AE characteristic parameters such as amplitude and centroid frequency. Also, through the comparison of two loading processes, we concluded that the signal produced during the first loading process was mainly caused by plastic deformation damage and during the second loading process the stress release and structure friction between sections in welding area are the main acoustic emission sources. Thus, the AE is an available tool for nondestructive online detection of latent welding defects of structural component of track crane.

  18. Study on the defects detection in composites by using optical position and infrared thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kwn, Koo Ahn; Choi, Man Yong; Park, Jeong Hak; Choi, Won Jae [Safety Measurement Center, Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of); Park, Hee Sang [Dept. of Research and Development, Korea Research Institute of Smart Material and Structures System Association, Daejeon (Korea, Republic of)

    2016-04-15

    Non-destructive testing methods for composite materials (e.g., carbon fiber-reinforced and glass fiber-reinforced plastic) have been widely used to detect damage in the overall industry. This study detects defects using optical infrared thermography. The transient heat transport in a solid body is characterized by two dynamic quantities, namely, thermal diffusivity and thermal effusivity. The first quantity describes the speed with thermal energy diffuses through a material, whereas the second one represents a type of thermal inertia. The defect detection rate is increased by utilizing a lock-in method and performing a comparison of the defect detection rates. The comparison is conducted by dividing the irradiation method into reflection and transmission methods and the irradiation time into 50 mHz and 100 mHz. The experimental results show that detecting defects at 50 mHz is easy using the transmission method. This result implies that low-frequency thermal waves penetrate a material deeper than the high-frequency waves.

  19. Analysis of CFRP Joints by Means of T-Pull Mechanical Test and Ultrasonic Defects Detection

    Directory of Open Access Journals (Sweden)

    Caterina Casavola

    2018-04-01

    Full Text Available Defects detection within a composite component, with the aim of understanding and predicting its mechanical behavior, is of great importance in the aeronautical field because the irregularities of the composite material could compromise functionality. The aim of this paper is to detect defects by means of non-destructive testing (NDT on T-pull samples made by carbon fiber reinforced polymers (CFRP and to evaluate their effect on the mechanical response of the material. Samples, obtained from an industrial stringer having an inclined web and realized with a polymeric filler between cap and web, were subjected to ultrasonic monitoring and then to T-pull mechanical tests. All samples were tested with the same load mode and the same test configuration. An experimental set-up consisting of a semiautomatic C-scan ultrasonic mapping system with a phased array probe was designed and developed, optimizing control parameters and implementing image processing software. The present work is carried out on real composites parts that are characterized by having their intrinsic defectiveness, as opposed to the previous similar results in the literature mainly obtained on composite parts with artificially produced defects. In fact, although samples under study were realized free from defects, ultrasonic mapping found defectiveness inside the material. Moreover, the ultrasonic inspection could be useful in detecting both the location and size of defects. Experimental data were critically analyzed and qualitatively correlated with results of T-pull mechanical tests in order to better understand and explain mechanical behavior in terms of fracture mode.

  20. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study.

    Science.gov (United States)

    Roos, A-M; Abdool, Z; Sultan, A H; Thakar, R

    2011-07-01

    To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B&K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Directory of Open Access Journals (Sweden)

    David Hewson

    2007-01-01

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

  3. 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%.

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

    Directory of Open Access Journals (Sweden)

    X. Liu

    2017-09-01

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

  5. Anomaly detection of microstructural defects in continuous fiber reinforced composites

    Science.gov (United States)

    Bricker, Stephen; Simmons, J. P.; Przybyla, Craig; Hardie, Russell

    2015-03-01

    Ceramic matrix composites (CMC) with continuous fiber reinforcements have the potential to enable the next generation of high speed hypersonic vehicles and/or significant improvements in gas turbine engine performance due to their exhibited toughness when subjected to high mechanical loads at extreme temperatures (2200F+). Reinforced fiber composites (RFC) provide increased fracture toughness, crack growth resistance, and strength, though little is known about how stochastic variation and imperfections in the material effect material properties. In this work, tools are developed for quantifying anomalies within the microstructure at several scales. The detection and characterization of anomalous microstructure is a critical step in linking production techniques to properties, as well as in accurate material simulation and property prediction for the integrated computation materials engineering (ICME) of RFC based components. It is desired to find statistical outliers for any number of material characteristics such as fibers, fiber coatings, and pores. Here, fiber orientation, or `velocity', and `velocity' gradient are developed and examined for anomalous behavior. Categorizing anomalous behavior in the CMC is approached by multivariate Gaussian mixture modeling. A Gaussian mixture is employed to estimate the probability density function (PDF) of the features in question, and anomalies are classified by their likelihood of belonging to the statistical normal behavior for that feature.

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

    Science.gov (United States)

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

    2017-07-01

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

  7. Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation

    Science.gov (United States)

    Wang, Xuejuan; Wu, Shuhang; Liu, Yunpeng

    2018-04-01

    This paper presents a new method for wood defect detection. It can solve the over-segmentation problem existing in local threshold segmentation methods. This method effectively takes advantages of visual saliency and local threshold segmentation. Firstly, defect areas are coarsely located by using spectral residual method to calculate global visual saliency of them. Then, the threshold segmentation of maximum inter-class variance method is adopted for positioning and segmenting the wood surface defects precisely around the coarse located areas. Lastly, we use mathematical morphology to process the binary images after segmentation, which reduces the noise and small false objects. Experiments on test images of insect hole, dead knot and sound knot show that the method we proposed obtains ideal segmentation results and is superior to the existing segmentation methods based on edge detection, OSTU and threshold segmentation.

  8. Quantitative defects detection in wind turbine blade using optical infrared thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kwaon, Koo Ahn [School of Aerospace System Engineering, UST, Daejeon (Korea, Republic of); Choi, Man Yong; Park, Hee Sang; Park, Jeong Hak; Huh, Yong Hak; Choi, Won Jai [Safety Measurement Center, Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of)

    2015-02-15

    A wind turbine blade is an important component in wind-power generation, and is generally exposed to harsh environmental conditions. Ultrasonic inspection is mainly used to inspect such blades, but it has been difficult to quantify defect sizes in complicated composite structures. Recently, active infrared thermography has been widely studied for inspecting composite structures, in which thermal energy is applied to an object, and an infrared camera detects the energy emitted from it. In this paper, a calibration method for active optical lock-in thermography is proposed to quantify the size. Inclusion, debonding and wrinkle defects, created in a wind blade for 100 kW wind power generation, were all successfully detected using this method. In particular, a 50.0 mm debonding defect was sized with 98.0% accuracy.

  9. Defect detection in industrial radiography: a multi-scale approach; Detection de defauts en radiographie industrielle: approches multiechelles

    Energy Technology Data Exchange (ETDEWEB)

    Lefevre, M

    1995-10-01

    Radiography is used by Electricite de France for pipe inspection in nuclear power plant in order to detect defects. For several years, the RD Division of EDF has undertaken research to define image processing methods well adapted to radiographic images. The main issues raised by these images are their low contrast, their high level of noise, the presence of a trend and the variable size of the defects. A data base of digitized radiographs of pipes has been gathered and the statistical, topological and geometrical properties of all of these images have been analyzed. From this study, a global indicator of the presence of defects and local features, leading to a classification of images into areas with or without defects, have been extracted. The defect localisation problem has been considered in a multi-scale framework based on the creation of a family of images with increasing regularity and defined as a solution of a partial differential equation. From a choice of axioms, a set of equations may be deduced which define various multi-scale analyses. The survey of the properties of such analysed, when applied to images altered with different types of noise, has lead to the selection of the digitized radiographs best adapted multi-scale analysis. The segmentation process, uses the geodesic information attached to defects via connection cost concept. The final decision is based on a summary of the information extracted at several scales. A fuzzy logic approach has been proposed to solve this part. We then developed methods and tools for expertise guidance and validated them on a complete data base of images. Some global indicators have been extracted and a detection and localisation process has been achieved for large defects. (author). 117 refs., 73 figs.

  10. 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.)

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

    Science.gov (United States)

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

    2018-03-01

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

  12. Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

    Science.gov (United States)

    Qian, Jinfang; Zhang, Changjiang

    2014-11-01

    An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.

  13. Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis.

    OpenAIRE

    Zappalá, D.; Tavner, P.J.; Crabtree, C.J.; Sheng, S.

    2014-01-01

    Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is th...

  14. Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images.

    Science.gov (United States)

    Dabbah, M A; Graham, J; Petropoulos, I; Tavakoli, M; Malik, R A

    2010-01-01

    Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

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

    Directory of Open Access Journals (Sweden)

    Mostafa Rabah

    2013-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Alberto Martini

    2015-01-01

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

  17. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.

    Science.gov (United States)

    Raja, D Siva Sundhara; Vasuki, S

    2015-01-01

    Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

  18. Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches

    Science.gov (United States)

    Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Thomas

    To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

  19. Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

    Science.gov (United States)

    Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

    2015-01-01

    It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

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

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-06-15

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

  3. Destructive examination of test plates 1 and 2 of the defects detection trials

    International Nuclear Information System (INIS)

    Crutzen, S.; Buergers, W.; Violin, F.; Di Piazza, L.; Cowburn, K.; Sargent, T.

    1983-01-01

    A further phase of the UKAEA defect detection trials (described previously) with PWR pressure vessel steels is reported. The evaluation of NDT exercise results must be based on destructive examination of the plates used during the exercise. Tests are described and results given. (U.K.)

  4. Risk reduction using DDP (Defect Detection and Prevention): Software support and software applications

    Science.gov (United States)

    Feather, M. S.

    2001-01-01

    Risk assessment and mitigation is the focus of the Defect Detection and Prevention (DDP) process, which has been applied to spacecraft technology assessments and planning, both hardware and software. DDP's major elements and their relevance to core requirement engineering concerns are summarized. The accompanying research demonstration illustrates DDP's tool support, and further customizations for application to software.

  5. A novel platform based on defect-rich knotted graphene nanotubes for detection of small biomolecules

    International Nuclear Information System (INIS)

    Lan, Shumin; Song, Yingpan; Chen, Qidi; Guo, Zhiyong; Zhan, Hongbing

    2016-01-01

    Highlights: • Curvature of the SC-CNTs’ cavities had more local pressure, leading to form k-GNTs. • k-GNTs are divided into sections by knots with abundant edge-plane sites/defects. • k-GNTs exhibited excellent catalytic activity, sensitivity and reproducibility. - Abstract: Detection of disease-related small biomolecules was of great significance for clinical diagnostics and treatment. In this work, we synthesized defect-rich knotted graphene nanotubes (k-GNTs) via chemical oxidative etching of stacked-up carbon nanotubes (SC-CNTs) followed by chemical reduction, to detect disease-related small biomolecules. We further studied the electrochemical properties using three representative redox probes and analyzed their biosensitivity using five biomolecules. The k-GNT-modified electrodes exhibited excellent electrochemical response, with the lowest ΔE p and the highest k 0 . Besides, the modified electrodes could simultaneously detect and discriminate between dopamine (DA), ascorbic acid and uric acid (UA), as well as differentiate phenethylamine (PEA) and epinephrine (EP) existed in newborn rat serum, providing the wide linear detection ranges with high sensitivities for DA, UA, PEA, and EP. These excellent electrocatalytic properties could be ascribe to the unique knotted graphene nanotube structure with high proportion of defect/edge sites, large, accessible, three-dimensional, accessible surface area, fewer oxygen-containing groups and doped N atoms. Our work reveals defect-rich k-GNTs as a promising platform for further applications in electrochemical biosensing and electrocatalysis.

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

    Directory of Open Access Journals (Sweden)

    Kemal Akyol

    2016-01-01

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

  7. Defect detection and classification of galvanized stamping parts based on fully convolution neural network

    Science.gov (United States)

    Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

    2018-04-01

    In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

  8. Detection of cardiac wall motion defects with combined amplitude/phase analysis

    International Nuclear Information System (INIS)

    Bacharach, S.L.; Green, M.V.; Bonow, R.O.; Pace, L.; Brunetti, A.; Larson, S.M.

    1985-01-01

    Fourier phase images have been used with some success to detect and quantify left ventricular (LV) wall motion defects. In abnormal regions of the LV, wall motion asynchronies often cause the time activity curve (TAC) to be shifted in phase. Such regional shifts are detected by analysis of the distribution function of phase values over the LV. However, not all wall motion defects result in detectable regional phase abnormalities. Such abnormalities may cause a reduction in the magnitude of contraction (and hence TAC amplitude) without any appreciable change in TAC shape(and hence phase). In an attempt to improve the sensitivity of the Fourier phase method for the detection of wall motion defects the authors analyzed the distribution function of Fourier amplitude as well as phase. 26 individuals with normal cardiac function and no history of cardiac disease served as controls. The goal was to detect and quantify wall motion as compared to the consensus of 3 independent observers viewing the scintigraphic cines. 26 subjects with coronary artery disease and mild wall motion defects (22 with normal EF) were studied ate rest. They found that analysis of the skew of thew amplitude distribution function improved the sensitivity for the detection of wall motion abnormalities at rest in the group from 65% to 85% (17/26 detected by phase alone, 22/26 by combined phase and amplitude analysis) while retaining a 0 false positive rate in the normal group. The authors conclude that analysis of Fourier amplitude distribution functions can significantly increase the sensitivity of phase imaging for detection of wall motion abnormalities

  9. Testing of defects in Si semiconductor apparatus by using single-photon detection

    International Nuclear Information System (INIS)

    Zhongliang, Pan; Ling, Chen; Guangju, Chen

    2013-01-01

    The failure analysis of semiconductor apparatus is very needed for ensuring product quality, which can find several types of defects in the semiconductor apparatus. A new testing method for the defects in Si semiconductor apparatus is presented in this paper, the method makes use of photon emissions to find out the failure positions or failure components by taking advantage of the infrared photo emission characteristics of semiconductor apparatus. These emitted photons carry the information of the apparatus structure. If there are defects in the apparatus, these photons can help in understanding the apparatus properties and detecting the defects. An algorithm for the generation of circuit input vectors are presented in this paper to enhance the strength of the emitted photons for the given components in the semiconductor apparatus. The multiple-valued logic, the static timing analysis and path sensitizations, are used in the algorithm. A lot of experimental results for the Si semiconductor apparatus show that many types of defects such as contact spiking and latchup failure etc., can be detected accurately by the method proposed in this paper

  10. Real-time portable system for fabric defect detection using an ARM processor

    Science.gov (United States)

    Fernandez-Gallego, J. A.; Yañez-Puentes, J. P.; Ortiz-Jaramillo, B.; Alvarez, J.; Orjuela-Vargas, S. A.; Philips, W.

    2012-06-01

    Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.

  11. ON-POWER DETECTION OF PIPE WALL-THINNED DEFECTS USING IR THERMOGRAPHY IN NPPS

    Directory of Open Access Journals (Sweden)

    JU HYUN KIM

    2014-04-01

    Full Text Available Wall-thinned defects caused by accelerated corrosion due to fluid flow in the inner pipe appear in many structures of the secondary systems in nuclear power plants (NPPs and are a major factor in degrading the integrity of pipes. Wall-thinned defects need to be managed not only when the NPP is under maintenance but also when the NPP is in normal operation. To this end, a test technique was developed in this study to detect such wall-thinned defects based on the temperature difference on the surface of a hot pipe using infrared (IR thermography and a cooling device. Finite element analysis (FEA was conducted to examine the tendency and experimental conditions for the cooling experiment. Based on the FEA results, the equipment was configured before the cooling experiment was conducted. The IR camera was then used to detect defects in the inner pipe of the pipe specimen that had artificially induced defects. The IR thermography developed in this study is expected to help resolve the issues related to the limitations of non-destructive inspection techniques that are currently conducted for NPP secondary systems and is expected to be very useful on the NPPs site.

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

    Directory of Open Access Journals (Sweden)

    Rostam Golmohamadi

    2014-04-01

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

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

  15. Automatic detection and quantitative analysis of cells in the mouse primary motor cortex

    Science.gov (United States)

    Meng, Yunlong; He, Yong; Wu, Jingpeng; Chen, Shangbin; Li, Anan; Gong, Hui

    2014-09-01

    Neuronal cells play very important role on metabolism regulation and mechanism control, so cell number is a fundamental determinant of brain function. Combined suitable cell-labeling approaches with recently proposed three-dimensional optical imaging techniques, whole mouse brain coronal sections can be acquired with 1-μm voxel resolution. We have developed a completely automatic pipeline to perform cell centroids detection, and provided three-dimensional quantitative information of cells in the primary motor cortex of C57BL/6 mouse. It involves four principal steps: i) preprocessing; ii) image binarization; iii) cell centroids extraction and contour segmentation; iv) laminar density estimation. Investigations on the presented method reveal promising detection accuracy in terms of recall and precision, with average recall rate 92.1% and average precision rate 86.2%. We also analyze laminar density distribution of cells from pial surface to corpus callosum from the output vectorizations of detected cell centroids in mouse primary motor cortex, and find significant cellular density distribution variations in different layers. This automatic cell centroids detection approach will be beneficial for fast cell-counting and accurate density estimation, as time-consuming and error-prone manual identification is avoided.

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

    Directory of Open Access Journals (Sweden)

    Kairi eKreegipuu

    2013-10-01

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

  17. Scanning Laser Polarimetry and Optical Coherence Tomography for Detection of Retinal Nerve Fiber Layer Defects

    Science.gov (United States)

    Oh, Jong-Hyun

    2009-01-01

    Purpose To compare the ability of scanning laser polarimetry with variable corneal compensation (GDx-VCC) and Stratus optical coherence tomography (OCT) to detect photographic retinal nerve fiber layer (RNFL) defects. Methods This retrospective cross-sectional study included 45 eyes of 45 consecutive glaucoma patients with RNFL defects in red-free fundus photographs. The superior and inferior temporal quadrants in each eye were included for data analysis separately. The location and presence of RNFL defects seen in red-free fundus photographs were compared with those seen in GDx-VCC deviation maps and OCT RNFL analysis maps for each quadrant. Results Of the 90 quadrants (45 eyes), 31 (34%) had no apparent RNFL defects, 29 (32%) had focal RNFL defects, and 30 (33%) had diffuse RNFL defects in red-free fundus photographs. The highest agreement between GDx-VCC and red-free photography was 73% when we defined GDx-VCC RNFL defects as a cluster of three or more color-coded squares (p<5%) along the traveling line of the retinal nerve fiber in the GDx-VCC deviation map (kappa value, 0.388; 95% confidence interval (CI), 0.195 to 0.582). The highest agreement between OCT and red-free photography was 85% (kappa value, 0.666; 95% CI, 0.506 to 0.825) when a value of 5% outside the normal limit for the OCT analysis map was used as a cut-off value for OCT RNFL defects. Conclusions According to the kappa values, the agreement between GDx-VCC deviation maps and red-free photography was poor, whereas the agreement between OCT analysis maps and red-free photography was good. PMID:19794943

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

    International Nuclear Information System (INIS)

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

    2013-01-01

    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 6 copies

  19. 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.

  20. Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform.

    Science.gov (United States)

    Sun, Shuping; Jiang, Zhongwei; Wang, Haibin; Fang, Yu

    2014-05-01

    This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E(T)) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E(T) and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E(T) can be expressed by multiplying the E(T) by an equivalent window (W(E)). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N=1s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%. Copyright © 2014 Elsevier Ireland Ltd. All

  1. Estimated accuracy of classification of defects detected in welded joints by radiographic tests

    International Nuclear Information System (INIS)

    Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A.; Caloba, L.P.; Mery, D.

    2004-01-01

    This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

  2. Estimated accuracy of classification of defects detected in welded joints by radiographic tests

    Energy Technology Data Exchange (ETDEWEB)

    Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A. [Federal Univ. of Rio de Janeiro, Dept., of Metallurgical and Materials Engineering, Rio de Janeiro (Brazil); Caloba, L.P. [Federal Univ. of Rio de Janeiro, Dept., of Electrical Engineering, Rio de Janeiro (Brazil); Mery, D. [Pontificia Unversidad Catolica de Chile, Escuela de Ingenieria - DCC, Dept. de Ciencia de la Computacion, Casilla, Santiago (Chile)

    2004-07-01

    This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

  3. Using the epigenetic field defect to detect prostate cancer in biopsy negative patients.

    Science.gov (United States)

    Truong, Matthew; Yang, Bing; Livermore, Andrew; Wagner, Jennifer; Weeratunga, Puspha; Huang, Wei; Dhir, Rajiv; Nelson, Joel; Lin, Daniel W; Jarrard, David F

    2013-06-01

    We determined whether a novel combination of field defect DNA methylation markers could predict the presence of prostate cancer using histologically normal transrectal ultrasound guided biopsy cores. Methylation was assessed using quantitative Pyrosequencing® in a training set consisting of 65 nontumor and tumor associated prostate tissues from University of Wisconsin. A multiplex model was generated using multivariate logistic regression and externally validated in blinded fashion in a set of 47 nontumor and tumor associated biopsy specimens from University of Washington. We observed robust methylation differences in all genes at all CpGs assayed (p prostate cancer (AUC 0.774, p = 0.001) and had a negative predictive value of 0.909. Comparison between 2 separate cores in patients in this validation set revealed similar methylation defects, indicating detection of a widespread field defect. A widespread epigenetic field defect can be used to detect prostate cancer in patients with histologically negative biopsies. To our knowledge this assay is unique, in that it detects alterations in nontumor cells. With further validation this marker combination (EVX1 and FGF1) has the potential to decrease the need for repeat prostate biopsies, a procedure associated with cost and complications. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  4. Automatic detection of lexical change: an auditory event-related potential study.

    Science.gov (United States)

    Muller-Gass, Alexandra; Roye, Anja; Kirmse, Ursula; Saupe, Katja; Jacobsen, Thomas; Schröger, Erich

    2007-10-29

    We investigated the detection of rare task-irrelevant changes in the lexical status of speech stimuli. Participants performed a nonlinguistic task on word and pseudoword stimuli that occurred, in separate conditions, rarely or frequently. Task performance for pseudowords was deteriorated relative to words, suggesting unintentional lexical analysis. Furthermore, rare word and pseudoword changes had a similar effect on the event-related potentials, starting as early as 165 ms. This is the first demonstration of the automatic detection of change in lexical status that is not based on a co-occurring acoustic change. We propose that, following lexical analysis of the incoming stimuli, a mental representation of the lexical regularity is formed and used as a template against which lexical change can be detected.

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

    Science.gov (United States)

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

    2017-03-01

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

  6. 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.

  7. 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.

  8. 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.

  9. The use of the cardiopulmonary flow index to detect cardiac defects in man and animal

    International Nuclear Information System (INIS)

    Cilliers, G.D.

    1982-01-01

    The efficiency of the cardiopulmonary flow index (CPFI) to detect cardiac defects and to evaluate therapy in man and animal is tested. The CPFI seems to be sensitive enough to evaluate vasodilator and inotropic therapy during cardiac failure with 'gousiekte' sheep. Pulmonary emboli in sheep is induced by injecting coagulated blood into the pulmonary circulation. These pulmonary emboli caused a decrease in the CPFI. CPFI recordings were made on patients, before and after aorta- and mitralvalve replacements. The CPFI is sensitive enough to detect the valve inefficiency and also to detect the improvement in the pump efficiency of the heart after the double valve replacement. The results obtained prove that the CPFI could have a proper place in modern cardiology to evaluate therapy (clinical and surgical) and also to distinguish between cardiac defects and pulmonary emboli

  10. Multifrequency eddy current examination for surface defects detection of hot steel products

    International Nuclear Information System (INIS)

    Hiroshima, Tatsuo; Sakamoto, Takahide; Takahashi, Akio; Miyata, Kenichi.

    1985-01-01

    Multifrequency eddy current testing method using probe coils has been studied for surface defects detection in hot steel products at high temperature over the magnetic Curie point. The conventional signal processing method is not available for suppression of an undesirable signal caused by lift-off variation or unevenness in inspected surfaces, because the undesirable signal pattern is similar to a defect signal pattern. In order to suppress the undesirable signal a new dual frequency signal processing method using three phase rotators has been developed, and was applied to several hot steel inspections. The results are as follows. 1. In the rotating eddy current machine for hot steel rods, the lift-off variation signal caused by a wobble of rods or the difference between rotating center and pass center of rods can be suppressed. A long seam or crack whose depth is more than 0.5mm can be detected. 2. In the hot inspection for continuously cast slabs, the signal caused by oscillation mark whose depth is under 1 mm can be suppressed. A fine transversal crack whose depth is 2 mm can be detected. 3. In the hot inspection for round billets, the lift-off variation signal caused by oval shape can be eliminated, and a crack which is deeper than 1.5 mm can be clearly detected. The detectability of defects can be improved by the analysis of dual frequency signal pattern. (author)

  11. Automatic Echographic Detection of Halloysite Clay Nanotubes in a Low Concentration Range.

    Science.gov (United States)

    Conversano, Francesco; Pisani, Paola; Casciaro, Ernesto; Di Paola, Marco; Leporatti, Stefano; Franchini, Roberto; Quarta, Alessandra; Gigli, Giuseppe; Casciaro, Sergio

    2016-04-11

    Aim of this work was to investigate the automatic echographic detection of an experimental drug delivery agent, halloysite clay nanotubes (HNTs), by employing an innovative method based on advanced spectral analysis of the corresponding "raw" radiofrequency backscatter signals. Different HNT concentrations in a low range (5.5-66 × 10 10 part/mL, equivalent to 0.25-3.00 mg/mL) were dispersed in custom-designed tissue-mimicking phantoms and imaged through a clinically-available echographic device at a conventional ultrasound diagnostic frequency (10 MHz). The most effective response (sensitivity = 60%, specificity = 95%), was found at a concentration of 33 × 10 10 part/mL (1.5 mg/mL), representing a kind of best compromise between the need of enough particles to introduce detectable spectral modifications in the backscattered signal and the necessity to avoid the losses of spectral peculiarity associated to higher HNT concentrations. Based on theoretical considerations and quantitative comparisons with literature-available results, this concentration could also represent an optimal concentration level for the automatic echographic detection of different solid nanoparticles when employing a similar ultrasound frequency. Future dedicated studies will assess the actual clinical usefulness of the proposed approach and the potential of HNTs for effective theranostic applications.

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

    Science.gov (United States)

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

    2014-12-01

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

  13. Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

    Science.gov (United States)

    Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

    2018-05-06

    In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2014-04-01

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

  15. Detection and quantification of defects in composite material by using thermal wave method

    International Nuclear Information System (INIS)

    Ranjit, Shrestha; Kim, Won Tae

    2015-01-01

    This paper explored the results of experimental investigation on carbon fiber reinforced polymer (CFRP) composite sample with thermal wave technique. The thermal wave technique combines the advantages of both conventional thermal wave measurement and thermography using a commercial Infrared camera. The sample comprises the artificial inclusions of foreign material to simulate defects of different shape and size at different depths. Lock-in thermography is employed for the detection of defects. The temperature field of the front surface of sample was observed and analysed at several excitation frequencies ranging from 0.562 Hz down to 0.032 Hz. Four-point methodology was applied to extract the amplitude and phase of thermal wave's harmonic component. The phase images are analyzed to find qualitative and quantitative information about the defects

  16. THz Imaging as a Method to Detect Defects of Aeronautical Coatings

    Science.gov (United States)

    Catapano, I.; Soldovieri, F.; Mazzola, L.; Toscano, C.

    2017-10-01

    Ice adhesion over critical aircraft surfaces is a serious potential hazard that runs the risk of causing accidents. To face this issue, the design and diagnostics of new multifunctional coatings with icephobic and aesthetical properties are demanded. In particular, diagnostic tools, capable of characterizing coating surface finishing and its defects, are needed. In this paper, terahertz (THz) imaging is considered as a high-resolution diagnostic tool useful for contactless surveys providing information on surface defects and material inner structure. Therefore, two composite specimens, one covered by a classical commercial livery coating and the other one by a new multifunctional coating with icephobic properties, are investigated by THz surveys carried out in normal environmental conditions of pressure and temperature. The results, obtained by processing the raw data properly, corroborate that THz imaging allows us to detect variations of the coating thickness, to localize hidden anomalies as well as to characterize surface defects at millimetric scale.

  17. Detection of Surface Defects and Servo Signal Restoration for a Compact Disc Player

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2006-01-01

    Compact disc (CD) players have been on the market for more than two decades, and the involved technologies, including control are very mature. Some problems, however, still remain with respect to playing CDs having to surface defects like scratches and fingerprints. Two servo control loops are used...... to keep the optical pick-up unit (OPU) focused and radially locked to the information track of the CD. The problem is to design servo controllers which are well suited for both handling surface defects and disturbances like mechanical shocks. The handling of surface defects requires a low-controller...... bandwidth which is in conflict with the requirement for the handling of disturbances. This control problem can be solved by the use of a fault tolerant control strategy, where the fault detection is very important. The OPU feeds the controllers with detector signals. Based on these, focus and radial...

  18. Detection and quantification of defects in composite material by using thermal wave method

    Energy Technology Data Exchange (ETDEWEB)

    Ranjit, Shrestha; Kim, Won Tae [Dept. of Mechanical Engineering, Kongju National University, Cheonan (Korea, Republic of)

    2015-12-15

    This paper explored the results of experimental investigation on carbon fiber reinforced polymer (CFRP) composite sample with thermal wave technique. The thermal wave technique combines the advantages of both conventional thermal wave measurement and thermography using a commercial Infrared camera. The sample comprises the artificial inclusions of foreign material to simulate defects of different shape and size at different depths. Lock-in thermography is employed for the detection of defects. The temperature field of the front surface of sample was observed and analysed at several excitation frequencies ranging from 0.562 Hz down to 0.032 Hz. Four-point methodology was applied to extract the amplitude and phase of thermal wave's harmonic component. The phase images are analyzed to find qualitative and quantitative information about the defects.

  19. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

    Directory of Open Access Journals (Sweden)

    Tao Ye

    2018-06-01

    Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  20. Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery.

    Science.gov (United States)

    Sandino, Juan; Wooler, Adam; Gonzalez, Felipe

    2017-09-24

    The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

  1. Automatic Detection and Classification of Audio Events for Road Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Noor Almaadeed

    2018-06-01

    Full Text Available This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

  2. A New Detecting Technology for External Anticorrosive Coating Defects of Pipelines Based on Ultrasonic Guided Wave

    Science.gov (United States)

    Liu, Shujun; Zuo, Yonggang; Zhang, Zhen

    2018-01-01

    The external anticorrosive coating is the shelter for preventing steel pipelines from Corrosive damage. A number of pipelines face severe corrosive problems for the performance decrease of the coating, especially during long-term services, which usually led to safety accidents. To solve the detection problem about the defect of anticorrosive layer for pipeline, a new detection method for anticorrosive layer of pipelines based on Ultrasonic Guided Wave was proposed in the paper. The results from the investigation show a possibility of using the Ultrasonic Guided Wave method for detecting the damage of pipeline’s External Anticorrosive Coating.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

  5. Detection of CFRP Composite Manufacturing Defects Using a Guided Wave Approach

    Science.gov (United States)

    Hudson, Tyler B.; Hou, Tan-Hung; Grimsley, Brian W.; Yuan, Fuh-Gwo

    2015-01-01

    NASA Langley Research Center is investigating a guided-wave based defect detection technique for as-fabricated carbon fiber reinforced polymer (CFRP) composites. This technique will be extended to perform in-process cure monitoring, defect detection and size determination, and ultimately a closed-loop process control to maximize composite part quality and consistency. The overall objective of this work is to determine the capability and limitations of the proposed defect detection technique, as well as the number and types of sensors needed to identify the size, type, and location of the predominant types of manufacturing defects associated with laminate layup and cure. This includes, porosity, gaps, overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focuses on detection of the porosity formed from variations in the matrix curing process, and on local overlaps intentionally introduced during layup of the prepreg. By terminating the cycle prematurely, three 24-ply unidirectional composite panels were manufactured such that each subsequent panel had a higher final degree of cure, and lower level of porosity. It was demonstrated that the group velocity, normal to the fiber direction, of a guided wave mode increased by 5.52 percent from the first panel to the second panel and 1.26 percent from the second panel to the third panel. Therefore, group velocity was utilized as a metric for degree of cure and porosity measurements. A fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for the detection and size determination of an overlap By transforming the plate response from the time-space domain to the frequency-wavenumber domain, the total wavefield was then separated into the incident and backscatter waves. The overlap region was accurately imaged by using a zero-lag cross-correlation (ZLCC) imaging condition, implying the incident and backscattered

  6. The effect of loading methods and parameters on defect detection in digital shearography

    Science.gov (United States)

    Yang, Fu; Ye, Xingchen; Qiu, Zisheng; Zhang, Borui; Zhong, Ping; Liang, ZhiYong; Sun, Zeyu; Zhu, Shu

    Digital Shearography Speckle Pattern Interferometry (DSSPI) is a non-destructive testing technique, which has a wide range of applications in industrial field due to the merits of non-contact, fast response, full-field measurement and high sensitivity. However, in the real application, the loading methods and parameters usually depend on the experience of the operator, which affect the effectiveness and accuracy of the test. Based on this background and the principle of DSSPI, a model using finite element analysis software and Matlab is established to simulate the defects detections of aluminum plate and composite laminates under different loading conditions. The simulation covers loading methods, shearing direction, shearing amount, loading intensity, defect size, defect depth and defect position. In order to quantify the testing effect, a parameter named the deviation D is first defined. And through the parameter D, the simulation system can evaluate the system detection ability. The work in this paper can provide systematic guidance for the choice of loading methods and parameters in the real DSSPI experiment system.

  7. Numerical simulation in the process defect detection and diagnosis. Application: control of electronuclear power plants

    International Nuclear Information System (INIS)

    Fan, Jian

    1989-01-01

    As devices are needed to help the operator in understanding and controlling the behaviour of a nuclear power plant, and in reducing the probability of human errors, this research thesis aims at developing a methodology which allows a model to be obtained which is an actual reference of the controlled installation. The author first discusses the model type to be adopted by providing a classification of existing simulation models, and then outlines the importance of the reference model and its relationships with the different simulation models: four selection criteria are proposed. Then, the author discusses how to use the chosen model and the identification of model errors. As the two main control tasks are the detection and diagnosis of defects, and as the reference model is a simulation model in which errors are compensated by an identification model, the detection of defects by means of the reference model is based on a comparison of its calculations with installation measurements. Thanks to model error modelling, this comparison can be directly obtained on a second order, i.e. between predicted errors and variances noticed between the model and the installation. In order to address defect diagnosis, the author proposes a solution to identify the origin of the defect within the installation

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

    Science.gov (United States)

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

    2015-03-01

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

  9. Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

    Science.gov (United States)

    Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

    2009-02-01

    Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

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

    Directory of Open Access Journals (Sweden)

    Simona M.C. Porto

    2013-09-01

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

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

    Directory of Open Access Journals (Sweden)

    C. A. SATHIYAMOORTHY

    2016-04-01

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

  12. A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

    Science.gov (United States)

    Salehi, Leila; Azmi, Reza

    2014-07-01

    Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

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

    Science.gov (United States)

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

    2005-04-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    N. Zarrinpanjeh

    2017-09-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

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

    Science.gov (United States)

    Zarrinpanjeh, N.; Dadrassjavan, F.

    2017-09-01

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

  18. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2016-11-21

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

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

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

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

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

    Science.gov (United States)

    Xu, Lili; Luo, Shuqian

    2010-11-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2018-01-15

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

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

    Directory of Open Access Journals (Sweden)

    Rupali Kamathe

    2017-08-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Bohui Zhu

    2013-01-01

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

  6. The Detection of Burn-Through Weld Defects Using Noncontact Ultrasonics

    Directory of Open Access Journals (Sweden)

    Zeynab Abbasi

    2018-01-01

    Full Text Available Nearly all manufactured products in the metal industry involve welding. The detection and correction of defects during welding improve the product reliability and quality, and prevent unexpected failures. Nonintrusive process control is critical for avoiding these defects. This paper investigates the detection of burn-through damage using noncontact, air-coupled ultrasonics, which can be adapted to the immediate and in-situ inspection of welded samples. The burn-through leads to a larger volume of degraded weld zone, providing a resistance path for the wave to travel which results in lower velocity, energy ratio, and amplitude. Wave energy dispersion occurs due to the increase of weld burn-through resulting in higher wave attenuation. Weld sample micrographs are used to validate the ultrasonic results.

  7. Research on defect detection from incomplete scanning of X-ray

    International Nuclear Information System (INIS)

    Zhang Shunli; Zhang Dinghua; Cheng Yunyong; Li Xiaolin

    2011-01-01

    Computed tomography (CT) is an advanced means of non-destructive testing, which has been widely used in medical and industrial fields. Aiming at the non-destructive testing problem of large industrial components, It presents a defect detection method from incomplete scanning of X-ray. Firstly, a set of incomplete scanning projection data before using the component has been obtained, then reconstruct them by algebraic re- construction technique (ART), and take the reconstructed images as the norm images. Then, the incomplete projection data of different times during the use of the component has been obtained, and reconstruct them by ART algorithm. Finally, It makes digital subtraction operation by the reconstructed images and the norm images, the defection can be detected clearly and intuitively from the subtraction image. Experimental result shows the proposed method is effective. (authors)

  8. A study on detection of internal defects of pressure vessel by digital shearography

    International Nuclear Information System (INIS)

    Kang, Young Jun; Park, Sung Tae; Lee, Hae Moo; Nam, Seung Hun

    1999-01-01

    Pipelines in power plants, nuclear facilities and chemical industries are often affected by corrosion effects. The inspection of internal defects of these pipelines is important to guarantee safe operational condition. Conventional NDT methods have been taken relatively much time, money, and manpower because of performing as the method of contact with objects to be inspected. Digital shearography is a laser-based optical method which allows full-field observation of surface displacement derivatives. This method has many advantages in practical use, such as low sensitivity to environmental noise, simple optical configuration and real time measurement. Therefore it is a good method to use for detecting internal defects. In this paper, the experiment was performed with some pressure vessels which has different internal cracks. We detected internal cracks of the pressure vessels at a real time and evaluated qualitatively these results. We also performed qualitative measurement of shearo fringe by using phase shifting method.

  9. Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

    Science.gov (United States)

    Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

    2017-07-01

    Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

  10. Defect Detection in Alphonso using Statistical Method and Principal Component Analysis: A Non-Destructive Approach

    OpenAIRE

    Sandeep S. Musale; Pradeep M. Patil

    2014-01-01

    Natural image analysis uses textural property of the surface. Texture is defined as a spatial arrangement of local intensity attributes that are correlated within areas of visual scene corresponding to surface regions. Texture exhibits some sort of periodicity of the basic pattern of Spongy Tissue in alphonso mango. This leads to use textural property to identify different patterns of Spongy Tissue in alphonso for detection of defects in alphonso mango. Visual assessment of texture made by hu...

  11. A model that accounts for the interdependence of extent and severity in the automatic evaluation of myocardial defects

    DEFF Research Database (Denmark)

    El-Ali, Henrik Hussein; Palmer, John; Edenbrandt, Lars

    2006-01-01

    The extent and severity are two important parameters when describing a regional defect in myocardial single-photon emission computed tomography (SPECT) perfusion imaging. Intuitively, these two parameters should be independent of each other, but we have shown in a previous study that there is an ...

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

    Directory of Open Access Journals (Sweden)

    Tazeem Fatma

    2016-10-01

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

  13. Sensitivity Analysis Based SVM Application on Automatic Incident Detection of Rural Road in China

    Directory of Open Access Journals (Sweden)

    Xingliang Liu

    2018-01-01

    Full Text Available Traditional automatic incident detection methods such as artificial neural networks, backpropagation neural network, and Markov chains are not suitable for addressing the incident detection problem of rural roads in China which have a relatively high accident rate and a low reaction speed caused by the character of small traffic volume. This study applies the support vector machine (SVM and parameter sensitivity analysis methods to build an accident detection algorithm in a rural road condition, based on real-time data collected in a field experiment. The sensitivity of four parameters (speed, front distance, vehicle group time interval, and free driving ratio is analyzed, and the data sets of two parameters with a significant sensitivity are chosen to form the traffic state feature vector. The SVM and k-fold cross validation (K-CV methods are used to build the accident detection algorithm, which shows an excellent performance in detection accuracy (98.15% of the training data set and 87.5% of the testing data set. Therefore, the problem of low incident reaction speed of rural roads in China could be solved to some extent.

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

    Science.gov (United States)

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

    2010-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-10-09

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

  16. Continuous-wave radar to detect defects within heat exchangers and steam generator tubes.

    Energy Technology Data Exchange (ETDEWEB)

    Nassersharif, Bahram (New Mexico State University, Las Cruces, NM); Caffey, Thurlow Washburn Howell; Jedlicka, Russell P. (New Mexico State University, Las Cruces, NM); Garcia, Gabe V. (New Mexico State University, Las Cruces, NM); Rochau, Gary Eugene

    2003-01-01

    A major cause of failures in heat exchangers and steam generators in nuclear power plants is degradation of the tubes within them. The tube failure is often caused by the development of cracks that begin on the outer surface of the tube and propagate both inwards and laterally. A new technique was researched for detection of defects using a continuous-wave radar method within metal tubing. The experimental program resulted in a completed product development schedule and the design of an experimental apparatus for studying handling of the probe and data acquisition. These tests were completed as far as the prototypical probe performance allowed. The prototype probe design did not have sufficient sensitivity to detect a defect signal using the defined radar technique and did not allow successful completion of all of the project milestones. The best results from the prototype probe could not detect a tube defect using the radar principle. Though a more precision probe may be possible, the cost of design and construction was beyond the scope of the project. This report describes the probe development and the status of the design at the termination of the project.

  17. Optical probe for porosity defect detection on inner diameter surfaces of machined bores

    Science.gov (United States)

    Kulkarni, Ojas P.; Islam, Mohammed N.; Terry, Fred L.

    2010-12-01

    We demonstrate an optical probe for detection of porosity inside spool bores of a transmission valve body with diameters down to 5 mm. The probe consists of a graded-index relay rod that focuses a laser beam spot onto the inner surface of the bore. Detectors, placed in the specular and grazing directions with respect to the incident beam, measure the change in scattered intensity when a surface defect is encountered. Based on the scattering signatures in the two directions, the system can also validate the depth of the defect and distinguish porosity from bump-type defects coming out of the metal surface. The system can detect porosity down to a 50-μm lateral dimension and ~40 μm in depth with >3-dB contrast over the background intensity fluctuations. Porosity detection systems currently use manual inspection techniques on the plant floor, and the demonstrated probe provides a noncontact technique that can help automotive manufacturers meet high-quality standards during production.

  18. Study of MR sequence in detecting hyaline cartilage defects of the knee joint

    International Nuclear Information System (INIS)

    Li Songbai; He Cuiju; Sun Wenge; Li Chunkui; Qi Xixun; Li Yanliang; Xu Ke; Bai Xizhuang; Wu Zhenhua

    2003-01-01

    Objective: To evaluate the value of various MR imaging sequences for detecting hyaline cartilage defects. Methods: Ten animal models of cartilage defect were established in 5 pig knees. 5 knees were examined with nine different MR sequences. The signal noise ratio of cartilage and contrast noise ratio were calculated and compared between cartilage and adjacent tissue. Measurement of the defect depth and width on the imaging was correlated with the actual measurement before imaging. 23 patients with hyaline cartilage defects of the knee were evaluated with MR imaging. All these patients underwent subsequent arthroscopy. MR imaging protocol included the selected sequences in the experimental study. Results: The cartilage SNR was better in FSE PD, FS 3D FSPGR, and FS FSE PD sequences. CNR between cartilage and subcartilaginous bone was best in FS 3D FSPGR and FS FSE PD sequences. CNR between cartilage and joint fluid was best in FS 3D FSPGR and FS FSE T 2 WI sequences. CNR between cartilage and meniscus and ligament was best in FS 3D FSPGR, FS FSE PD, SE T 1 WI, and IR TI700 sequences. CNR between cartilage and fat was best in FS 3D FSPGR and SE T 1 WI sequences. The width and depth correlation was best in IR TI700 sequence, which showed the statistical significance (P 2 WI sequence, 68%, 99%, and 0.74, respectively with IR TI700 sequence. Conclusion: The sensitivity of FS 3D FSPGR sequence in detecting hyaline cartilage defect is the highest. T 1 WI of spin echo sequence and T 2 WI/PDWI of fast spin-echo with fat saturation should be the standard sequence in the examination of knee joint. T 1 WI of IR sequence has potential clinical value for cartilage examination

  19. Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations.

    Science.gov (United States)

    Van De Gucht, Tim; Saeys, Wouter; Van Meensel, Jef; Van Nuffel, Annelies; Vangeyte, Jurgen; Lauwers, Ludwig

    2018-01-01

    Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2017-08-25

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

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Hamid Reza Pourreza

    2009-03-01

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

  3. 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.

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

    Science.gov (United States)

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

    2017-06-01

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

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

    Science.gov (United States)

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

    2017-06-01

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

  6. Feature extraction for ultrasonic sensor based defect detection in ceramic components

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2014-02-01

    High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

  7. Signal enhancement in amperometric peroxide detection by using graphene materials with low number of defects

    International Nuclear Information System (INIS)

    Zöpfl, Alexander; Matysik, Frank-Michael; Hirsch, Thomas; Sisakthi, Masoumeh; Eroms, Jonathan; Strunk, Christoph

    2016-01-01

    Two-dimensional carbon nanomaterials ranging from single-layer graphene to defective structures such as chemically reduced graphene oxide were studied with respect to their use in electrodes and sensors. Their electrochemical properties and utility in terms of fabrication of sensing devices are compared. Specifically, the electrodes have been applied to reductive amperometric determination of hydrogen peroxide. Low-defect graphene (SG) was obtained through mechanical exfoliation of natural graphite, while higher-defect graphenes were produced by chemical vapor deposition (CVDG) and by chemical oxidation of graphite and subsequent reduction (rGO). The carbonaceous materials were mainly characterized by Raman microscopy. They were applied as electrode material and the electrochemical behavior was investigated by chronocoulometry, cyclic voltammetry, electrochemical impedance spectroscopy and amperometry and compared to a carbon disc electrode. It is shown that the quality of the graphene has an enormous impact on the amperometric performance. The use of carbon materials with many defects (like rGO) does not result in a significant improvement in signal compared to a plain carbon disc electrode. The sensitivity is 173 mA · M −1  · cm −2 in case of using CVDG which is about 50 times better than that of a plain carbon disc electrode and about 7 times better than that of rGO. The limit of detection for hydrogen peroxide is 15.1 μM (at a working potential of −0.3 V vs SCE) for CVDG. It is concluded that the application of two-dimensional carbon nanomaterials offers large perspectives in amperometric detection systems due to electrocatalytic effects that result in highly sensitive detection. (author)

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  11. Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

    Science.gov (United States)

    Tabrizi, Pooneh R.; Mansoor, Awais; Biggs, Elijah; Jago, James; Linguraru, Marius George

    2018-02-01

    Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 +/- 0.9 mm, 6.34 +/- 4.32 degrees, and 1.73 +/- 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

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

    Science.gov (United States)

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

    2013-04-01

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

  13. 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.

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

    Science.gov (United States)

    Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter

    2017-03-01

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

  15. Automatic quantification of defect size using normal templates: a comparative clinical study of three commercially available algorithms

    International Nuclear Information System (INIS)

    Sutter, J. de; Wiele, C. van de; Bondt, P. de; Dierckx, R.; D'Asseler, Y.; Backer, G. de; Rigo, P.

    2000-01-01

    Infarct size assessed by myocardial single-photon emission tomography (SPET) imaging is an important prognostic parameter after myocardial infarction (MI). We compared three commercially available automatic quantification algorithms that make use of normal templates for the evaluation of infarct extent and severity in a large population of patients with remote MI. We studied 100 consecutive patients (80 men, mean age 63±11 years, mean LVEF 47%±15%) with a remote MI who underwent resting technetium-99m tetrofosmin gated SPET study for infarct extent and severity quantification. The quantification algorithms used for comparison were a short-axis algorithm (Cedars-Emory quantitative analysis software, CEqual), a vertical long-axis algorithm (VLAX) and a three-dimensional fitting algorithm (Perfit). Semiquantitative visual infarct extent and severity assessment using a 20-segment model with a 5-point score and the relation of infarct extent and severity with rest LVEF determined by quantitative gated SPET (QGS) were used as standards to compare the different algorithms. Mean infarct extent was similar for visual analysis (30%±21%) and the VLAX algorithm (25%±17%), but CEqual (15%±11%) and Perfit (5%±6%) mean infarct extents were significantly lower compared with visual analysis and the VLAX algorithm. Moreover, infarct extent determined by Perfit was significantly lower than infarct extent determined by CEqual. Correlations between automatic and visual infarct extent and severity evaluations were moderate (r=0.47, P 2 , n=32) compared with anterior infarctions and non-obese patients for all three algorithms. In this large series of post-MI patients, results of infarct extent and severity determination by automatic quantification algorithms that make use of normal templates were not interchangeable and correlated only moderately with semiquantitative visual analysis and LVEF. (orig.)

  16. Applications of Flexible Ultrasonic Transducer Array for Defect Detection at 150 °C

    Directory of Open Access Journals (Sweden)

    Jiunn-Woei Liaw

    2013-01-01

    Full Text Available In this study, the feasibility of using a one dimensional 16-element flexible ultrasonic transducer (FUT array for nondestructive testing at 150 °C is demonstrated. The FUT arrays were made by a sol-gel sprayed piezoelectric film technology; a PZT composite film was sprayed on a titanium foil of 75 µm thickness. Since the FUT array is flexible, it was attached to a steel pipe with an outer diameter of 89 mm and a wall thickness of 6.5 mm at 150 °C. Using the ultrasonic pulse-echo mode, pipe thickness measurements could be performed. Moreover, using the ultrasonic pulse-echo and pitch-catch modes of each element of FUT array, the defect detection was performed on an Al alloy block of 30 mm thickness with a side-drilled hole (SDH of f3 mm at 150 °C. In addition, a post-processing algorithm based on the total focusing method was used to process the full matrix of these A-scan signals of each single transmitter and multi-receivers, and then the phase-array image was obtained to indicate this defect- SDH. Both results show the capability of FUT array being operated at 150 °C for the corrosion and defect detections.

  17. Detecting a Defective Casing Seal at the Top of a Bedrock Aquifer.

    Science.gov (United States)

    Richard, Sandra K; Chesnaux, Romain; Rouleau, Alain

    2016-03-01

    An improperly sealed casing can produce a direct hydraulic connection between two or more originally isolated aquifers with important consequences regarding groundwater quantity and quality. A recent study by Richard et al. (2014) investigated a monitoring well installed in a fractured rock aquifer with a defective casing seal at the soil-bedrock interface. A hydraulic short circuit was detected that produced some leakage between the rock and the overlying deposits. A falling-head permeability test performed in this well showed that the usual method of data interpretation is not valid in this particular case due to the presence of a piezometric error. This error is the direct result of the preferential flow originating from the hydraulic short circuit and the subsequent re-equilibration of the piezometric levels of both aquifers in the vicinity of the inlet and the outlet of the defective seal. Numerical simulations of groundwater circulation around the well support the observed impact of the hydraulic short circuit on the results of the falling-head permeability test. These observations demonstrate that a properly designed falling-head permeability test may be useful in the detection of defective casing seals. © 2015, National Ground Water Association.

  18. Active Thermography for the Detection of Defects in Powder Metallurgy Compacts

    International Nuclear Information System (INIS)

    Benzerrouk, Souheil; Ludwig, Reinhold; Apelian, Diran

    2007-01-01

    Active thermography is an established NDE technique that has become the method of choice in many industrial applications which require non-contact access to the parts under test. Unfortunately, when conducting on-line infrared (IR) inspection of powder metallic compacts, complications can arise due the generally low emissivity of metals and the thermally noisy environment typically encountered in manufacturing plants. In this paper we present results of an investigation that explores the suitability of active IR imaging of powder metallurgy compacts for the detection of surface and sub-surface defects in the pre-sinter state and in an on-line manufacturing setting to ensure complete quality assurance. Additional off-line tests can be carried out for statistical quality analyses. In this research, the IR imaging of sub-surface defects is based on a transient instrumentation approach that relies on an electric control system which synchronizes and monitors the thermal response due to an electrically generated heat source. Preliminary testing reveals that this newly developed pulsed thermography system can be employed for the detection of subsurface defects in green-state parts. Practical measurements agree well with theoretical predictions. The inspection approach being developed can be used for the testing of green-state compacts as they exit the compaction press at speeds of up to 1,000 parts per hour

  19. On-line defect detection of aluminum coating using fiber optic sensor

    Science.gov (United States)

    Patil, Supriya S.; Shaligram, A. D.

    2015-03-01

    Aluminum metallization using the sprayed coating for exhaust mild steel (MS) pipes of tractors is a standard practice for avoiding rusting. Patches of thin metal coats are prone to rusting and are thus considered as defects in the surface coating. This paper reports a novel configuration of the fiber optic sensor for on-line checking the aluminum metallization uniformity and hence for defect detection. An optimally chosen high bright 440 nm BLUE LED (light-emitting diode) launches light into a transmitting fiber inclined at the angle of 60° to the surface under inspection placed adequately. The reflected light is transported by a receiving fiber to a blue enhanced photo detector. The metallization thickness on the coated surface results in visually observable variation in the gray shades. The coated pipe is spirally inspected by a combination of linear and rotary motions. The sensor output is the signal conditioned and monitored with RISHUBH DAS. Experimental results show the good repeatability in the defect detection and coating non-uniformity measurement.

  20. An interactive machine-learning approach for defect detection in computed tomogaraphy (CT) images of hardwood logs

    Science.gov (United States)

    Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt; Philip A. Araman

    2005-01-01

    This paper describes recent progress in the analysis of computed tomography (CT) images of hardwood logs. The long-term goal of the work is to develop a system that is capable of autonomous (or semiautonomous) detection of internal defects, so that log breakdown decisions can be optimized based on defect locations. The problem is difficult because wood exhibits large...

  1. 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.

  2. 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.

  3. 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...

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  5. NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    G. Máttyus

    2013-05-01

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

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

    Science.gov (United States)

    Máttyus, G.

    2013-05-01

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

  7. Method of detecting defects in ion exchange membranes of electrochemical cells by chemochromic sensors

    Science.gov (United States)

    Brooker, Robert Paul; Mohajeri, Nahid

    2016-01-05

    A method of detecting defects in membranes such as ion exchange membranes of electrochemical cells. The electrochemical cell includes an assembly having an anode side and a cathode side with the ion exchange membrane in between. In a configuration step a chemochromic sensor is placed above the cathode and flow isolation hardware lateral to the ion exchange membrane which prevents a flow of hydrogen (H.sub.2) between the cathode and anode side. The anode side is exposed to a first reactant fluid including hydrogen. The chemochromic sensor is examined after the exposing for a color change. A color change evidences the ion exchange membrane has at least one defect that permits H.sub.2 transmission therethrough.

  8. 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

  9. Detecting the honeycomb sandwich composite material's moisture impregnating defects by using infrared thermography technique

    International Nuclear Information System (INIS)

    Kwon, Koo Ahn; Choi, Man Yong; Park, Jeong Hak; Choi, Won Jae; Park, Hee Sang

    2017-01-01

    Many composite materials are used in the aerospace industry because of their excellent mechanical properties. However, the nature of aviation exposes these materials to high temperature and high moisture conditions depending on climate, location, and altitude. Therefore, the molecular arrangement chemical properties, and mechanical properties of composite materials can be changed under these conditions. As a result, surface disruptions and cracks can be created. Consequently, moisture-impregnating defects can be induced due to the crack and delamination of composite materials as they are repeatedly exposed to moisture absorption moisture release, fatigue environment, temperature changes, and fluid pressure changes. This study evaluates the possibility of detecting the moisture-impregnating defects of CFRP and GFRP honeycomb structure sandwich composite materials, which are the composite materials in the aircraft structure, by using an active infrared thermography technology among non-destructive testing methods. In all experiments, it was possible to distinguish the area and a number of CFRP composite materials more clearly than those of GFRP composite material. The highest detection rate was observed in the heating duration of 50 mHz and the low detection rate was at the heating duration of over 500 mHz. The reflection method showed a higher detection rate than the transmission method

  10. Pulsed eddy current differential probe to detect the defects in a stainless steel pipe

    Science.gov (United States)

    Angani, C. S.; Park, D. G.; Kim, C. G.; Leela, P.; Kishore, M.; Cheong, Y. M.

    2011-04-01

    Pulsed eddy current (PEC) is an electromagnetic nondestructive technique widely used to detect and quantify the flaws in conducting materials. In the present study a differential Hall-sensor probe which is used in the PEC system has been fabricated for the detection of defects in stainless steel pipelines. The differential probe has an exciting coil with two Hall-sensors. A stainless steel test sample with electrical discharge machining (EDM) notches under different depths of 1-5 mm was made and the sample was laminated by plastic insulation having uniform thickness to simulate the pipelines in nuclear power plants (NPPs). The driving coil in the probe is excited by a rectangular current pulse and the resultant response, which is the difference of the two Hall-sensors, has been detected as the PEC probe signal. The discriminating time domain features of the detected pulse such as peak value and time to zero are used to interpret the experimental results with the defects in the test sample. A feature extraction technique such as spectral power density has been devised to infer the PEC response.

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

    Directory of Open Access Journals (Sweden)

    Elaheh Aghabalaei Khordehchi

    2017-06-01

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

  12. Development of visual field defect after first-detected optic disc hemorrhage in preperimetric open-angle glaucoma.

    Science.gov (United States)

    Kim, Hae Jin; Song, Yong Ju; Kim, Young Kook; Jeoung, Jin Wook; Park, Ki Ho

    2017-07-01

    To evaluate functional progression in preperimetric glaucoma (PPG) with disc hemorrhage (DH) and to determine the time interval between the first-detected DH and development of glaucomatous visual field (VF) defect. A total of 87 patients who had been first diagnosed with PPG were enrolled. The medical records of PPG patients without DH (Group 1) and with DH (Group 2) were reviewed. When glaucomatous VF defect appeared, the time interval from the diagnosis of PPG to the development of VF defect was calculated and compared between the two groups. In group 2, the time intervals from the first-detected DH to VF defect of the single- and recurrent-DH were compared. Of the enrolled patients, 45 had DH in the preperimetric stage. The median time interval from the diagnosis of PPG to the development of VF defect was 73.3 months in Group 1, versus 45.4 months in Group 2 (P = 0.042). The cumulative probability of development of VF defect after diagnosis of PPG was significantly greater in Group 2 than in Group 1. The median time interval from first-detected DH to the development of VF defect was 37.8 months. The median time interval from DH to VF defect and cumulative probability of VF defect after DH did not show a statistical difference between single and recurrent-DH patients. The median time interval between the diagnosis of PPG and the development of VF defect was significantly shorter in PPG with DH. The VF defect appeared 37.8 months after the first-detected DH in PPG.

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  15. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features.

    Science.gov (United States)

    Su, Yanni; Wang, Yuanyuan; Jiao, Jing; Guo, Yi

    2011-01-01

    Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-01

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

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

    Science.gov (United States)

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

    2012-12-01

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

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

    Science.gov (United States)

    Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

    2013-09-01

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

  19. Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach.

    Science.gov (United States)

    Yuan, Yixuan; Li, Dengwang; Meng, Max Q-H

    2017-07-31

    In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast based saliency and object-center based saliency together. The contrast based saliency map highlights image parts that show different appearances compared with surrounding areas while the object-center based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with Multiple Kernel Boosting (MKB) is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multi-level WBU saliency maps. We finally integrate these two stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs favorably against state-of-the-art saliency methods to detect polyps.

  20. 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.

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  2. Deep Defect Detection within Thick Multilayer Aircraft Structures Containing Steel Fasteners Using a Giant-Magneto Resistive (GMR) Sensor (Preprint)

    National Research Council Canada - National Science Library

    Ko, Ray T; Steffes, Gary J

    2007-01-01

    Defect detection within thick multilayer structures containing steel fasteners is a challenging task in eddy current testing due to the magnetic permeability of the fasteners and overall thickness of the structure...

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

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2017-04-01

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

  4. Optimization of Rb-82 PET acquisition and reconstruction protocols for myocardial perfusion defect detection

    Science.gov (United States)

    Tang, Jing; Rahmim, Arman; Lautamäki, Riikka; Lodge, Martin A.; Bengel, Frank M.; Tsui, Benjamin M. W.

    2009-05-01

    The purpose of this study is to optimize the dynamic Rb-82 cardiac PET acquisition and reconstruction protocols for maximum myocardial perfusion defect detection using realistic simulation data and task-based evaluation. Time activity curves (TACs) of different organs under both rest and stress conditions were extracted from dynamic Rb-82 PET images of five normal patients. Combined SimSET-GATE Monte Carlo simulation was used to generate nearly noise-free cardiac PET data from a time series of 3D NCAT phantoms with organ activities modeling different pre-scan delay times (PDTs) and total acquisition times (TATs). Poisson noise was added to the nearly noise-free projections and the OS-EM algorithm was applied to generate noisy reconstructed images. The channelized Hotelling observer (CHO) with 32× 32 spatial templates corresponding to four octave-wide frequency channels was used to evaluate the images. The area under the ROC curve (AUC) was calculated from the CHO rating data as an index for image quality in terms of myocardial perfusion defect detection. The 0.5 cycle cm-1 Butterworth post-filtering on OS-EM (with 21 subsets) reconstructed images generates the highest AUC values while those from iteration numbers 1 to 4 do not show different AUC values. The optimized PDTs for both rest and stress conditions are found to be close to the cross points of the left ventricular chamber and myocardium TACs, which may promote an individualized PDT for patient data processing and image reconstruction. Shortening the TATs for <~3 min from the clinically employed acquisition time does not affect the myocardial perfusion defect detection significantly for both rest and stress studies.

  5. Detection of Subsurface Defects in Levees in Correlation to Weather Conditions Utilizing Ground Penetrating Radar

    Science.gov (United States)

    Martinez, I. A.; Eisenmann, D.

    2012-12-01

    Ground Penetrating Radar (GPR) has been used for many years in successful subsurface detection of conductive and non-conductive objects in all types of material including different soils and concrete. Typical defect detection is based on subjective examination of processed scans using data collection and analysis software to acquire and analyze the data, often requiring a developed expertise or an awareness of how a GPR works while collecting data. Processing programs, such as GSSI's RADAN analysis software are then used to validate the collected information. Iowa State University's Center for Nondestructive Evaluation (CNDE) has built a test site, resembling a typical levee used near rivers, which contains known sub-surface targets of varying size, depth, and conductivity. Scientist at CNDE have developed software with the enhanced capabilities, to decipher a hyperbola's magnitude and amplitude for GPR signal processing. With this enhanced capability, the signal processing and defect detection capabilities for GPR have the potential to be greatly enhanced. This study will examine the effects of test parameters, antenna frequency (400MHz), data manipulation methods (which include data filters and restricting the range of depth in which the chosen antenna's signal can reach), and real-world conditions using this test site (such as varying weather conditions) , with the goal of improving GPR tests sensitivity for differing soil conditions.

  6. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

    Directory of Open Access Journals (Sweden)

    Hermanus Vermaak

    2016-01-01

    Full Text Available The dual-tree complex wavelet transform (DTCWT solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT. It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG, are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

  7. 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

  8. AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2016-06-01

    Full Text Available 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

  9. An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

    Science.gov (United States)

    Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

    2018-01-01

    In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

  10. A robust segmentation approach based on analysis of features for defect detection in X-ray images of aluminium castings

    DEFF Research Database (Denmark)

    Lecomte, G.; Kaftandjian, V.; Cendre, Emmanuelle

    2007-01-01

    A robust image processing algorithm has been developed for detection of small and low contrasted defects, adapted to X-ray images of castings having a non-uniform background. The sensitivity to small defects is obtained at the expense of a high false alarm rate. We present in this paper a feature...... three parameters and taking into account the fact that X-ray grey-levels follow a statistical normal law. Results are shown on a set of 684 images, involving 59 defects, on which we obtained a 100% detection rate without any false alarm....

  11. Destructive examination of test plates 3 and test piece 4 of the defects detection trials (DDT)

    International Nuclear Information System (INIS)

    Buegers, W.; Crutzen, S.; Pisoni, A.; Violin, F.; Di Piazza, L.; Lock, D.; Sargent, T.

    1984-01-01

    The evaluation of NDT exercises results has been based on destructive examination of the plates or test blocks used during the exercise. The PISC I Programme has shown that in all cases the indications given by the NDT instrumentation were corresponding to some particular defects or structure aspects in the steel or were explained by particular positions of reflectors. Generally the introduction of defects using techniques such as: - implantation of modules, - introduction of non metallic material, - introduction of poison in the weld, do not produce a final ''detective zone or area'' which is strictly corresponding to the intended defect. The DDT exercise management has thus decided to perform a complete destructive examination of the four plates involved in this exercise because of its experience (the PISC I exercise) and independance of commercial interest, the JRC of the CEC, Ispra Establishment, has been asked to do the work in collaboratione with the Risley Nuclear Power development Laboratories (RNL). A collaboration agreement has been signed between RNL and JRC. Operating Agent of the PISC II programme, is interested in having a direct access to data to be added to those furnished by PISC. The present report describes the results of the destructive examination of the DDT plates 3 and 4

  12. Detection of defects on the metal surface using the modulated microwave

    International Nuclear Information System (INIS)

    Joo, Gwang Tae; Jeong, Sung Hae; Song, Ki Young; Kim, Jin Ouk

    1996-01-01

    The defects on the metal surface, like as ended circular pressed hole, penetrated circular drilled hole and linear hollow lane(ended linear crack), are tested by method of reflection, transmission, fixed carrier frequency and mod-demodulation techniques using microwave horn antenna and rectangular waveguide on 9.2 GHz carrier and 3 kHz modulation frequency. In the cases of ended circular hole and penetrated hole defects, the magnitude of reflection signals changed extremely, and the results on the defects' sizes are enlarge d by about 2.5 times at the ended hole and decreased by about 75% at the penetrate d hole. And in the cases of linear hollow lane, depths are 0.45 mm, 1.2 mm and 2.4 mm, the measured results on average increasing rate of detected reflection signals according to crack widths are 0.46 mV/mm, 0.32 mV/mm and 0.23 mV/mm each, for length of lane 150 mm.

  13. Estimation of the defect detection probability for ultrasonic tests on thick sections steel weldments. Technical report

    International Nuclear Information System (INIS)

    Johnson, D.P.; Toomay, T.L.; Davis, C.S.

    1979-02-01

    An inspection uncertainty analysis of published PVRC Specimen 201 data is reported to obtain an estimate of the probability of recording an indication as a function of imperfection height for ASME Section XI Code ultrasonic inspections of the nuclear reactor vessel plate seams and to demonstrate the advantages of inspection uncertainty analysis over conventional detection/nondetection counting analysis. This analysis found the probability of recording a significant defect with an ASME Section XI Code ultrasonic inspection to be very high, if such a defect should exist in the plate seams of a nuclear reactor vessel. For a one-inch high crack, for example, this analysis gives a best estimate recording probability of .985 and a 90% lower confidence bound recording probabilty of .937. It is also shown that inspection uncertainty analysis gives more accurate estimates and gives estimates over a much greater flaw size range than is possible with conventional analysis. There is reason to believe that the estimation procedure used is conservative, the estimation is based on data generated several years ago, on very small defects, in an environment that is different from the actual in-service inspection environment

  14. Beam Expansion of Blind Spot Detection Radar Antennas Using a Radome with Defected Corrugated Inner Wall

    Directory of Open Access Journals (Sweden)

    Hayeon Kim

    2017-01-01

    Full Text Available A beam expanding radome for 76.5 GHz automotive radar antennas is presented whose inner surface is engraved with corrugations. The radar used for blind spot detection (BSD requires a very wide beam width to ensure longer time for tracking out-of-sight objects. It is found that the corrugations modulate the phase velocities of the waves along the surface, which increases beam width in the far field. In addition, defects in the corrugation increase beam width even further. The presented structure satisfies the beam width requirement while keeping a low profile.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-04-15

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2014-06-01

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

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

    International Nuclear Information System (INIS)

    Quintana, J.C.; Caceres, F.; Vargas, P.

    2002-01-01

    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

  19. A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data

    Science.gov (United States)

    Liya Thomas; R. Edward. Thomas

    2011-01-01

    We have developed an automated defect detection system and a state-of-the-art Graphic User Interface (GUI) for hardwood logs. The algorithm identifies defects at least 0.5 inch high and at least 3 inches in diameter on barked hardwood log and stem surfaces. To summarize defect features and to build a knowledge base, hundreds of defects were measured, photographed, and...

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

    Directory of Open Access Journals (Sweden)

    Edward Hill

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