WorldWideScience

Sample records for automatic defect detection

  1. 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.

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

    Science.gov (United States)

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

    2015-05-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  4. 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.

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

    Science.gov (United States)

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

    2015-07-01

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

  6. Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2011-09-01

    Full Text Available Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

  7. Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

    Science.gov (United States)

    Liu, Yi-Hung; Chen, Yan-Jen

    2011-01-01

    Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

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

    Directory of Open Access Journals (Sweden)

    Pedro J. Navarro

    2016-07-01

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

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

    CERN Document Server

    Rahaman, G M Atiqur

    2009-01-01

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

  10. 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.

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

    OpenAIRE

    Rahaman, G. M. Atiqur; Hossain, Md. Mobarak

    2009-01-01

    Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Again, price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an automated defect detection and classification technique has been proposed in this report that can have ensured the better quality of tiles in manufacturing process as well as pr...

  12. 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

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

    Science.gov (United States)

    Henrique Castelo Branco, Luiz; César Lima Segantine, Paulo

    2015-09-01

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

  14. Software Defect Detection with Rocus

    Institute of Scientific and Technical Information of China (English)

    Yuan Jiang; Ming Li; Zhi-Hua Zhou

    2011-01-01

    Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system. Although many machine learning methods have been successfully applied to the task, most of them fail to consider two practical yet important issues in software defect detection. First, it is rather difficult to collect a large amount of labeled training data for learning a well-performing model; second, in a software system there are usually much fewer defective modules than defect-free modules, so learning would have to be conducted over an imbalanced data set. In this paper, we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus. This method exploits the abundant unlabeled examples to improve the detection accuracy, as well as employs under-sampling to tackle the class-imbalance problem in the learning process. Experimental results of real-world software defect detection tasks show that Rocgs is effective for software defect detection. Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.

  15. Defect Automatic Identification of Eddy Current Pulsed Thermography

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2014-01-01

    Full Text Available Eddy current pulsed thermography (ECPT is an effective nondestructive testing and evaluation (NDT&E technique, and has been applied for a wide range of conductive materials. Manual selected frames have been used for defects detection and quantification. Defects are indicated by high/low temperature in the frames. However, the variation of surface emissivity sometimes introduces illusory temperature inhomogeneity and results in false alarm. To improve the probability of detection, this paper proposes a two-heat balance states-based method which can restrain the influence of the emissivity. In addition, the independent component analysis (ICA is also applied to automatically identify defect patterns and quantify the defects. An experiment was carried out to validate the proposed methods.

  16. Studying post-etching silicon crystal defects on 300mm wafer by automatic defect review AFM

    Science.gov (United States)

    Zandiatashbar, Ardavan; Taylor, Patrick A.; Kim, Byong; Yoo, Young-kook; Lee, Keibock; Jo, Ahjin; Lee, Ju Suk; Cho, Sang-Joon; Park, Sang-il

    2016-03-01

    Single crystal silicon wafers are the fundamental elements of semiconductor manufacturing industry. The wafers produced by Czochralski (CZ) process are very high quality single crystalline materials with known defects that are formed during the crystal growth or modified by further processing. While defects can be unfavorable for yield for some manufactured electrical devices, a group of defects like oxide precipitates can have both positive and negative impacts on the final device. The spatial distribution of these defects may be found by scattering techniques. However, due to limitations of scattering (i.e. light wavelength), many crystal defects are either poorly classified or not detected. Therefore a high throughput and accurate characterization of their shape and dimension is essential for reviewing the defects and proper classification. While scanning electron microscopy (SEM) can provide high resolution twodimensional images, atomic force microscopy (AFM) is essential for obtaining three-dimensional information of the defects of interest (DOI) as it is known to provide the highest vertical resolution among all techniques [1]. However AFM's low throughput, limited tip life, and laborious efforts for locating the DOI have been the limitations of this technique for defect review for 300 mm wafers. To address these limitations of AFM, automatic defect review AFM has been introduced recently [2], and is utilized in this work for studying DOI on 300 mm silicon wafer. In this work, we carefully etched a 300 mm silicon wafer with a gaseous acid in a reducing atmosphere at a temperature and for a sufficient duration to decorate and grow the crystal defects to a size capable of being detected as light scattering defects [3]. The etched defects form a shallow structure and their distribution and relative size are inspected by laser light scattering (LLS). However, several groups of defects couldn't be properly sized by the LLS due to the very shallow depth and low

  17. Improve mask inspection capacity with Automatic Defect Classification (ADC)

    Science.gov (United States)

    Wang, Crystal; Ho, Steven; Guo, Eric; Wang, Kechang; Lakkapragada, Suresh; Yu, Jiao; Hu, Peter; Tolani, Vikram; Pang, Linyong

    2013-09-01

    As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continues to diminish. The adoption of RET techniques like aggressive OPC, sub-resolution assist features combined with the requirements to detect even smaller defects on masks due to increasing MEEF, poses considerable challenges for mask inspection operators and engineers. Therefore a comprehensive approach is required in handling defects post-inspections by correctly identifying and classifying the real killer defects impacting the printability on wafer, and ignoring nuisance defect and false defects caused by inspection systems. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at the SMIC mask shop for the 40nm technology node. Traditionally, each defect is manually examined and classified by the inspection operator based on a set of predefined rules and human judgment. At SMIC mask shop due to the significant total number of detected defects, manual classification is not cost-effective due to increased inspection cycle time, resulting in constrained mask inspection capacity, since the review has to be performed while the mask stays on the inspection system. Luminescent Technologies Automated Defect Classification (ADC) product offers a complete and systematic approach for defect disposition and classification offline, resulting in improved utilization of the current mask inspection capability. Based on results from implementation of ADC in SMIC mask production flow, there was around 20% improvement in the inspection capacity compared to the traditional flow. This approach of computationally reviewing defects post mask-inspection ensures no yield loss by qualifying reticles without the errors associated with operator mis-classification or human error. The ADC engine retrieves the high resolution inspection images and uses a decision-tree flow to classify a given defect. Some identification mechanisms adopted by ADC to

  18. Improved Genetic Algorithm Application in Textile Defect Detection

    Institute of Scientific and Technical Information of China (English)

    GENG Zhao-feng; Li Bei-bei; ZHAO Zhi-hong

    2007-01-01

    Based on an efficient improved genetic algorithm,a pattern recognition approach is represented for textile defects inspection. An image process is developed to automatically detect the drawbacks on textile caused by three circumstances: break, dual, and jump of yams. By statistic method, some texture feature values of the image with defects points can be achieved. Therefore, the textile defects are classified properly. The advanced process of the defect image is done. Image segmentation is realized by an improved genetic algorithm to detect the defects. This method can be used to automatically classify and detect textile defects. According to different users' requirements, ifferent types of textile material can be detected.

  19. Mask Blank Defect Detection

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, M A; Sommargren, G E

    2000-02-04

    Mask blanks are the substrates that hold the master patterns for integrated circuits. Integrated circuits are semiconductor devices, such as microprocessors (mPs), dynamic random access memory (DRAMs), and application specific integrated circuits (ASICs) that are central to the computer, communication, and electronics industries. These devices are fabricated using a set of master patterns that are sequentially imaged onto light-sensitive coated silicon wafers and processed to form thin layers of insulating and conductive materials on top of the wafer. These materials form electrical paths and transistors that control the flow of electricity through the device. For the past forty years the semiconductor industry has made phenomenal improvements in device functionality, compactness, speed, power, and cost. This progress is principally due to the exponential decrease in the minimum feature size of integrated circuits, which has been reduced by a factor of {radical}2 every three years. Since 1992 the Semiconductor Industry Association (SIA) has coordinated the efforts of producing a technology roadmap for semiconductors. In the latest document, ''The International Technology Roadmap for Semiconductors: 1999'', future technology nodes (minimum feature sizes) and targeted dates were specified and are summarized in Table 1. Lithography is the imaging technology for producing a de-magnified image of the mask on the wafer. A typical de-magnification factor is 4. Mask blank defects as small as one-eighth the equivalent minimum feature size are printable and may cause device failure. Defects might be the result of the surface preparation, such as polishing, or contamination due to handling or the environment. Table 2 shows the maximum tolerable defect sizes on the mask blank for each technology node. This downward trend puts a tremendous burden on mask fabrication, particularly in the area of defect detection and reduction. A new infrastructure for mask

  20. Automatic Sarcasm Detection: A Survey

    OpenAIRE

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

    2016-01-01

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

  1. 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.

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

    Science.gov (United States)

    Jian, Bo-Lin; Peng, Chao-Chung

    2017-06-15

    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(V)1 and AN/AVS-6(V)2. 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.

  3. Automatic detection of laughter

    NARCIS (Netherlands)

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

    2005-01-01

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

  4. 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

  5. Key issues in automatic classification of defects in post-inspection review process of photomasks

    Science.gov (United States)

    Pereira, Mark; Maji, Manabendra; Pai, Ravi R.; B. V. R., Samir; Seshadri, R.; Patil, Pradeepkumar

    2012-11-01

    The mask inspection and defect classification is a crucial part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with high detection sensitivity becomes greater. However, due to the high sensitivity, in addition to the detection of smaller defects on finer geometries, the inspection machine could report large number of false defects. The total number of defects becomes significantly high and the manual classification of these defects, where the operator should review each of the defects and classify them, may take huge amount of time. Apart from false defects, many of the very small real defects may not print on the wafer and user needs to spend time on classifying them as well. Also, sometimes, manual classification done by different operators may not be consistent. So, need for an automatic, consistent and fast classification tool becomes more acute in more advanced nodes. Automatic Defect Classification tool (NxADC) which is in advanced stage of development as part of NxDAT1, can automatically classify defects accurately and consistently in very less amount of time, compared to a human operator. Amongst the prospective defects as detected by the Mask Inspection System, NxADC identifies several types of false defects such as false defects due to registration error, false defects due to problems with CCD, noise, etc. It is also able to automatically classify real defects such as, pin-dot, pin-hole, clear extension, multiple-edges opaque, missing chrome, chrome-over-MoSi, etc. We faced a large set of algorithmic challenges during the course of the development of our NxADC tool. These include selecting the appropriate image alignment algorithm to detect registration errors (especially when there are sub-pixel registration errors or misalignment in repetitive patterns such as line space), differentiating noise from

  6. Automatic spikes detection in seismogram

    Institute of Scientific and Technical Information of China (English)

    王海军; 靳平; 刘贵忠

    2003-01-01

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

  7. Automatic classification and accurate size measurement of blank mask defects

    Science.gov (United States)

    Bhamidipati, Samir; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2015-07-01

    A blank mask and its preparation stages, such as cleaning or resist coating, play an important role in the eventual yield obtained by using it. Blank mask defects' impact analysis directly depends on the amount of available information such as the number of defects observed, their accurate locations and sizes. Mask usability qualification at the start of the preparation process, is crudely based on number of defects. Similarly, defect information such as size is sought to estimate eventual defect printability on the wafer. Tracking of defect characteristics, specifically size and shape, across multiple stages, can further be indicative of process related information such as cleaning or coating process efficiencies. At the first level, inspection machines address the requirement of defect characterization by detecting and reporting relevant defect information. The analysis of this information though is still largely a manual process. With advancing technology nodes and reducing half-pitch sizes, a large number of defects are observed; and the detailed knowledge associated, make manual defect review process an arduous task, in addition to adding sensitivity to human errors. Cases where defect information reported by inspection machine is not sufficient, mask shops rely on other tools. Use of CDSEM tools is one such option. However, these additional steps translate into increased costs. Calibre NxDAT based MDPAutoClassify tool provides an automated software alternative to the manual defect review process. Working on defect images generated by inspection machines, the tool extracts and reports additional information such as defect location, useful for defect avoidance[4][5]; defect size, useful in estimating defect printability; and, defect nature e.g. particle, scratch, resist void, etc., useful for process monitoring. The tool makes use of smart and elaborate post-processing algorithms to achieve this. Their elaborateness is a consequence of the variety and

  8. A Sensor System for Detection of Hull Surface Defects

    Directory of Open Access Journals (Sweden)

    Juan Suardíaz

    2010-07-01

    Full Text Available This paper presents a sensor system for detecting defects in ship hull surfaces. The sensor was developed to enable a robotic system to perform grit blasting operations on ship hulls. To achieve this, the proposed sensor system captures images with the help of a camera and processes them in real time using a new defect detection method based on thresholding techniques. What makes this method different is its efficiency in the automatic detection of defects from images recorded in variable lighting conditions. The sensor system was tested under real conditions at a Spanish shipyard, with excellent results.

  9. 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.

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

    Science.gov (United States)

    Peng, Xiaoling; Hou, Wenguang; Ding, Mingyue

    2009-10-01

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

  11. 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...

  12. Defect detection for end surface of ferrite magnetic tile

    Science.gov (United States)

    Tao, Jiayuan; Wang, Yuwei; Wang, Keyi

    2016-09-01

    A visual automatic detection method is proposed for defect detection on end surface of ferrite magnetic tile to tackle the disadvantages generated by human work which has low efficiency and unstable accuracy. Because the defects on end surface of ferrite magnetic tile with dark colors and low contrasts are negative for defect detection, uniform illumination is provided by LED light source and a dedicated optical system is designed to extract defects conveniently. The approach uses comparison of the fitting and actual edge curves to detect defects mainly with most defects located on the edge. Firstly improved adaptive median filter is used as the image preprocessing. Subsequently the appropriate threshold is calculated by Otsu algorithm based on the extreme points in the gray-level histogram to segment the preprocessing image. Then the Sobel operator can be used to extract the edge of end surface precisely. Finally through comparing the ideal fitting and actual edge curves of end surface, to detect the defects with some relevant features. Experimental results show that the proposed scheme could detect defects on the end surface of ferrite magnetic tile efficiency and accurately with 93.33% accuracy rate, 2.30% false acceptance rate and 8.45% correct rejection rate.

  13. Automatic Target Detection Using Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Ganesan L

    2004-01-01

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

  14. Automatic Clustering of Rolling Element Bearings Defects with Artificial Neural Network

    Science.gov (United States)

    Antonini, M.; Faglia, R.; Pedersoli, M.; Tiboni, M.

    2006-06-01

    The paper presents the optimization of a methodology for automatic clustering based on Artificial Neural Networks to detect the presence of defects in rolling bearings. The research activity was developed in co-operation with an Italian company which is expert in the production of water pumps for automotive use (Industrie Saleri Italo). The final goal of the work is to develop a system for the automatic control of the pumps, at the end of the production line. In this viewpoint, we are gradually considering the main elements of the water pump, which can cause malfunctioning. The first elements we have considered are the rolling bearing, a very critic component for the system. The experimental activity is based on the vibration measuring of rolling bearings opportunely damaged; vibration signals are in the second phase elaborated; the third and last phase is an automatic clustering. Different signal elaboration techniques are compared to optimize the methodology.

  15. 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.

  16. Automatic Hazard Detection for Landers

    Science.gov (United States)

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

    2008-01-01

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

  17. Detection of Surface Defects on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2007-01-01

    Online detection of surface defects on optical discs is of high importance for the accommodation schemes handling these defects. These surface defects introduce fault components to the position measurements of focus and radial tracking positions. The respective controllers will accordingly try to...... in order to inspect the importance and consequences of the size of the detection delay, from which it can be seen that focus and radial position errors increase significantly due to the fault as the detection delay increases....

  18. FABRIC DEFECT DETECTION USING STEERABLE PYRAMID

    Directory of Open Access Journals (Sweden)

    S. Mythili

    2011-05-01

    Full Text Available In this paper, a novel idea is proposed for fabric defect detection. De- fects are detected in the fabric using steerable pyramid along with a defect detection algorithm. Various steerable pyramid of four size 256*256, 128*128, 64*64, 32*32 and with four orientation bands 00,450, 900, 1350 are used. Utilizing a Steerable pyramid proved ade- quate in the representation of fabric images in multi-scale and multi- orientations; thus allowing defect detection algorithms to run more effectively. Defect detection algorithm identifies and locates the im- perfection in the defective sample using the statistics mean and stan- dard deviation. This statistics represents the relative amount of inten- sity in the texture and is sufficient to measure defects in the current model .The obtained result are compared with the existing methods wavelet based system and with Gaussian and Laplacian pyramid.

  19. Automatic Fall Detection using Smartphone Acceleration Sensor

    Directory of Open Access Journals (Sweden)

    Tran Tri Dang

    2016-12-01

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

  20. Defect Detection Techniques for Airbag Production Sewing Stages

    Directory of Open Access Journals (Sweden)

    Raluca Brad

    2014-01-01

    Full Text Available Airbags are subjected to strict quality control in order to ensure passengers safety. The quality of fabric and sewing thread influences the final product and therefore, sewing defects must be early and accurately detected, in order to remove the item from production. Airbag seams assembly can take various forms, using linear and circle primitives, with threads of different colors and length densities, creating lockstitch or double threads chainstitch. The paper presents a framework for the automatic detection of defects occurring during the airbag sewing stage. Types of defects as skipped stitch, missed stitch, or superimposed seam for lockstitch and two threads chainstitch are detected and marked. Using image processing methods, the proposed framework follows the seams path and determines if a color pattern of the considered stitches is valid.

  1. Automatic Detection of Electric Power Troubles (ADEPT)

    Science.gov (United States)

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

    1988-01-01

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

  2. Automatic landslides detection on Stromboli volcanic Island

    Science.gov (United States)

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

    2016-04-01

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

  3. Line matching for automatic change detection algorithm

    Science.gov (United States)

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

    2012-06-01

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

  4. An automatic cells detection and segmentation

    Science.gov (United States)

    Han, Ligong; Le, T. Hoang Ngan; Savvides, Marios

    2017-03-01

    This paper presents an end-to-end framework for automatically detecting and segmenting blood cells including normal red blood cells (RBCs), connected RBCs, abnormal RBCs (i.e. tear drop, burr cell, helmet, etc.) and white blood cells (WBCs). Our proposed system contains several components to solve different problems regarding RBCs and WBCs. We first design a novel blood cell color representation which is able to emphasize the RBCs and WBCs in separate channels. Template matching technique is then employed to individually detect RBCs and WBCs in our proposed representation. In order to automatically segment the RBCs and nuclei from WBCs, we develop an adaptive level set-based segmentation method which makes use of both local and global information. The detected and segmented RBCs, however, can be a single RBC, a connected RBC or an abnormal RBC. Therefore, we first separate and reconstruct RBCs from the connected RBCs by our suggested modified template matching. Shape matching by inner distance is later used to classify the abnormal RBCs from the normal RBCs. Our proposed method has been tested and evaluated on different images from ALL-IDB,10 WebPath,24 UPMC,23 Flicker datasets, and the one used by Mohamed et al.14 The precision and recall of RBCs detection are 98.43% and 94.99% respectively, whereas those of WBCs detection are 99.12% and 99.12%. The F-measure of our proposed WBCs segmentation gets up to 95.8%.

  5. Automatic basal slice detection for cardiac analysis

    Science.gov (United States)

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

    2016-03-01

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

  6. 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...

  7. Test-Data Generation Guided by Static Defect Detection

    Institute of Scientific and Technical Information of China (English)

    Dan Hao; Lu Zhang; Ming-Hao Liu; He Li; Jia-Su Sun

    2009-01-01

    Software testing is an important technique to assure the quality of software systems, especially high-confidence systems. To automate the process of software testing, many automatic test-data generation techniques have been proposed.To generate effective test data, we propose a test-data generation technique guided by static defect detection in this paper.Using static defect detection analysis, our approach first identifies a set of suspicious statements which are likely to contain faults, then generates test data to cover these suspicious statements by converting the problem of test-data generation to the constraint satisfaction problem. We performed a case study to validate the effectiveness of our approach, and made a simple comparison with another test-data generation on-line tool, JUnit Factory. The results show that, compared with JUnit Factory, our approach generates fewer test data that are competitive on fault detection.

  8. Assessing facial wrinkles: automatic detection and quantification

    Science.gov (United States)

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

    2009-02-01

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

  9. Automatic Detection of Omissions in Translations

    CERN Document Server

    Melamed, I D

    1996-01-01

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

  10. Automatic Sarcasm Detection in Twitter Messages

    OpenAIRE

    Ræder, Johan Georg Cyrus Mazaher

    2016-01-01

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

  11. 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.

  12. Automatic Smoker Detection from Telephone Speech Signals

    DEFF Research Database (Denmark)

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

    2017-01-01

    on Gaussian mixture model means and weights respectively. Each framework is evaluated using different classification algorithms to detect the smoker speakers. Finally, score-level fusion of the i-vector-based and the NFA-based recognizers is considered to improve the classification accuracy. The proposed......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...

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

    National Research Council Canada - National Science Library

    Bo-Lin Jian; Chao-Chung Peng

    2017-01-01

    .... 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.

  14. 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.

  15. Research on automatic inspection system for defects on precise optical surface based on machine vision

    Institute of Scientific and Technical Information of China (English)

    WANG Xue; XIE Zhi-jiang

    2006-01-01

    In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products' surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

  16. 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.

  17. Model Tests of Pile Defect Detection

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The pile, as an important foundation style, is being used in engineering practice. Defects of different types and damages of different degrees easily occur during the process of pile construction. So,dietecting defects of the pile is very important. As so far, there are some difficult problems in pile defect detection. Based on stress wave theory, some of these typical difficult problems were studied through model tests. The analyses of the test results are carried out and some significant results of the low-strain method are obtained, when a pile has a gradually-decreasing crosssection part, the amplitude of the reflective signal originating from the defect is dependent on the decreasing value of the rate of crosssection β. No apparent signal reflected from the necking appeares on the velocity response curve when the value of β is less than about 3.5 %.

  18. AUTOMATIC RETINAL VESSEL DETECTION AND TORTUOSITY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Temitope Mapayi

    2016-07-01

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

  19. Vision-based in-line fabric defect detection using yarn-specific shape features

    Science.gov (United States)

    Schneider, Dorian; Aach, Til

    2012-01-01

    We develop a methodology for automatic in-line flaw detection in industrial woven fabrics. Where state of the art detection algorithms apply texture analysis methods to operate on low-resolved ({200 ppi) image data, we describe here a process flow to segment single yarns in high-resolved ({1000 ppi) textile images. Four yarn shape features are extracted, allowing a precise detection and measurement of defects. The degree of precision reached allows a classification of detected defects according to their nature, providing an innovation in the field of automatic fabric flaw detection. The design has been carried out to meet real time requirements and face adverse conditions caused by loom vibrations and dirt. The entire process flow is discussed followed by an evaluation using a database with real-life industrial fabric images. This work pertains to the construction of an on-loom defect detection system to be used in manufacturing practice.

  20. Automatic Detect and Trace of Solar Filaments

    Science.gov (United States)

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

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

  1. 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.

  2. Biological models for automatic target detection

    Science.gov (United States)

    Schachter, Bruce

    2008-04-01

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

  3. Automatic detection of aircraft emergency landing sites

    Science.gov (United States)

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

    2011-06-01

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

  4. Weaving defect detection by Fourier imaging

    Science.gov (United States)

    Ciamberlini, Claudio; Francini, Franco; Longobardi, Giuseppe; Poggi, Pasquale; Sansoni, Paola; Tiribilli, Bruno

    1996-08-01

    An optical configuration for the detection of faults was developed and tested. The optical fourier transformation is the basic working principle of the system. When good fabric passes in front of the optical system the Fourier image, captured by the camera, shows well defined spots corresponding to the spatial frequencies of the tissue. If a defect occurs during production on the loom, the pattern changes significantly and a defect is easily detected in real time. A very simple electronic image processing based on thresholding and binary histograms allows to obtain very encouraging performance for its applicability to the looms. A compact device has been realized and tested in real working conditions on the loom.

  5. 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...... recruited to complete an emotional oddball task featuring on happy and one fearful condition. The measurement of event-related potential was carried out via electroencephalography and electrooculography recording, to detect visual mismatch negativity (vMMN) with regard to the automatic detection of changes...... 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....

  6. Detection of surface cutting defect on magnet using Fourier image reconstruction

    Institute of Scientific and Technical Information of China (English)

    王福亮; 左博

    2016-01-01

    A magnet is an important component of a speaker, as it makes the coil move back forth, and it is commonly used in mobile information terminals. Defects may appear on the surface of the magnet while cutting it into smaller slices, and hence, automatic detection of surface cutting defect detection becomes an important task for magnet production. In this work, an image-based detection system for magnet surface defect was constructed, a Fourier image reconstruction based on the magnet surface image processing method was proposed. The Fourier transform was used to get the spectrum image of the magnet image, and the defect was shown as a bright line in it. The Hough transform was used to detect the angle of the bright line, and this line was removed to eliminate the defect from the original gray image;then the inverse Fourier transform was applied to get the background gray image. The defect region was obtained by evaluating the gray-level differences between the original image and the background gray image. Further, the effects of several parameters in this method were studied and the optimized values were obtained. Experiment results show that the proposed method can detect surface cutting defects in a magnet automatically and efficiently.

  7. Automatic Fringe Detection Of Dynamic Moire Patterns

    Science.gov (United States)

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

    1989-10-01

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

  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. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

    Science.gov (United States)

    Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

    2016-05-01

    Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.

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

    NARCIS (Netherlands)

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

    2008-01-01

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

  11. 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

  12. 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.

  13. Automatic invariant detection in dynamic web applications

    NARCIS (Netherlands)

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

    2010-01-01

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

  14. Method and instrumentation for detection of rail defects, in particular rail top defects

    NARCIS (Netherlands)

    Li, Z.; Molodova, M.

    2011-01-01

    A method and instrumentation for detection of rail defects, in particular rail top defects, in a railway-track by measuring an axle box acceleration signal of a rail vehicle, wherein a longitudinal axle box acceleration signal is used as a measure to detect the occurrence of said rail defects, in pa

  15. Weld Defect Extraction Based on Adaptive Morphology Filtering and Edge Detection by Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    WANGDonghua; ZHOUYuanhua; GANGTie

    2003-01-01

    One of the most key steps in X-ray au-tomatic inspection and intelligent recognition systems is how to extract defects and detect their edges effectively.In this paper, a novel method of defect extraction based on the adaptive morphology filtering (DEAMF) is pro-posed, whose structuring elements can be changed with the sizes of defects adaptively. By this method, defects in X-ray weld inspection images are extracted with well-kept shapes and high speeds. Then according to the theory of edge detection based on wavelet transform modulus max-ima, a locally supported wavelet with good antisymmetry is developed to extract edges of defects and the results are satisfying.

  16. Automatic Detection of Cyberbullying on Social Media

    OpenAIRE

    Engman, Love

    2016-01-01

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

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

    NARCIS (Netherlands)

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

    2007-01-01

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

  18. OntoDiagram: Automatic Diagram Generation for Congenital Heart Defects in Pediatric Cardiology

    OpenAIRE

    Vishwanath, Kartik; Viswanath, Venkatesh; Drake, William; Lee, Yugyung

    2005-01-01

    In pediatric cardiology as well as many other medical specialties, the accurate portrayal of a large volume of patient information is crucial to providing good patient care. Our research aims at utilizing clinical and spatial ontologies representing the human heart, to automatically generate a Mullins-like diagram [6] based on a patient's information in the cardiology databases. Our ontology allows an intuitive way of modeling congenital defects with the structure of the hum...

  19. The Role of Attentional Resources in Automatic Detection.

    Science.gov (United States)

    1981-01-01

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

  20. Aneuploidy among prenatally detected neural tube defects

    Energy Technology Data Exchange (ETDEWEB)

    Hume, R.F. Jr.; Lampinen, J.; Martin, L.S.; Johnson, M.P.; Evans, M.I. [Wayne State Univ., Detroit, MI (United States)] [and others

    1996-01-11

    We have reported previously a 10% aneuploidy detection rate among 39 cases of fetal neural tube defects (NTD). Subsequently we amassed an additional experience of over 17,000 prenatal diagnosis cases over a 5-year period. During this period 106 cases of NTDs were identified; 44 with anencephaly, 62 with open spina bifida. The average maternal age of this population with NTDs was 29 years (15-40); 6 patients declined amniocentesis. Six of 100 cytogenetic studies were aneuploid; on anencephalic fetus had inherited a maternal marker chromosome, and 5 NTD cases had trisomy 18. The average maternal age of the aneuploid cases was 21 (19-40); 3 were 35 years or older. Four of 5 trisomy 18 cases had multiple congenital anomalies (MCA). The overall aneuploidy detection rate in our cohort was 5-6, while aneuploidy occurred in 2% of the isolated NTD cases, and 24% of the MCA cases. Combining the earlier experience, 4/39 aneuploidy (2 trisomy 18, 4p+, del 13q) yields an aneuploidy detection frequency of 10/145 (7%), of which most (7/10) had trisomy 18. These data support fetal karyotyping for accurate diagnosis, prognosis, and recurrence-risk counseling. 5 refs., 2 tabs.

  1. 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.

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

    Science.gov (United States)

    Ding, Yong; Dai, Hang; Zhang, Hang

    2014-01-01

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

  3. Electrothermal Defect Detection in Powder Metallurgy Compacts

    Science.gov (United States)

    Benzerrouk, Souheil; Ludwig, Reinhold; Apelian, Diran

    2006-03-01

    Faced with increasing market pressures, metal part manufacturers have turned to new processes and fabrication technologies. One of these processes is powder metallurgy (P/M), which is employed for low-cost, high-volume precision part manufacturing. Despite many advantages, the P/M process has created a number of challenges, including the need for high-speed quality assessment and control, ideally for each compact. Consequently, sophisticated quality assurance is needed to rapidly detect flaws early in the manufacturing cycle and at minimal cost. In this paper we will discuss our progress made in designing and refining an active infrared (IR) detection system for P/M compacts. After discussing the theoretical background in terms of underlying equations and boundary conditions, analytical and numerical solutions are presented that are capable of predicting temperature responses for various defect sizes and orientations of a dynamic IR testing system. Preliminary measurements with controlled and industrial samples have shown that this active IR methodology can successfully be employed to test both green-state and sintered P/M compacts. The developed system can overcome many limitations observed with a standard IR testing methodology such as emissivity, background calibration, and contact resistance.

  4. Automatic Fiber Orientation Detection for Sewed Carbon Fibers

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  5. Automatic particle detection and sorting in an electrokinetic microfluidic chip.

    Science.gov (United States)

    Song, Yongxin; Peng, Ran; Wang, Junsheng; Pan, Xinxiang; Sun, Yeqing; Li, Dongqing

    2013-03-01

    This paper reports a lab-on-a-chip device that can automatically detect and sort particles based on their size differences with a high resolution. The PDMS-glass microfluidic chip is made by soft-lithography technique. A differential resistive pulse sensor is employed to electrically detect the sizes of the particles in EOF generated by applying DC voltages across channels. The detected resistive pulse sensor signals, whose amplitudes are proportional to particles' sizes, will automatically trigger the sorting process that is controlled by applying a voltage pulse (36 V) whenever a target particle is detected. This method was applied to automatically detect and sort polystyrene particles and microalgae in aqueous solutions. Sorting 5 μm polymer particle from a mixture of 4- and 5-μm polystyrene particles in aqueous solution, i.e. 1 μm sorting resolution, was demonstrated. The device described in this paper is simple, automatic, and label-free with high sorting resolution. It has wide applications in sample pretreatment and target particles detection. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Science.gov (United States)

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

    2014-08-01

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

  7. Defect detection in wire welded joints using thermography investigations

    Energy Technology Data Exchange (ETDEWEB)

    Swiatczak, T., E-mail: tomasz.swiatczak@p.lodz.pl [Institute of Electronics, Technical University of Lodz, Lodz (Poland); Tomczyk, M. [Institute of Electrical Engineering Systems, Technical University of Lodz, Lodz (Poland); Wiecek, B. [Institute of Electronics, Technical University of Lodz, Lodz (Poland); Pawlak, R. [Institute of Electrical Engineering Systems, Technical University of Lodz, Lodz (Poland); Olbrycht, R. [Institute of Electronics, Technical University of Lodz, Lodz (Poland)

    2012-09-01

    The formation of gas voids inside the wire joints during laser welding may cause internal defects (cracks and porosity), that are invisible from outside. Authors propose the application of active thermography for detection of such defects. Thermal camera was used to acquire sequences of thermograms showing the joints during transient heating. Fourier analysis enabled phase value calculation, which is different for defective and non-defective samples. Laboratory results were confirmed by simulations on prepared two-dimensional model.

  8. Automatic Detection of Seizures with Applications

    Science.gov (United States)

    Olsen, Dale E.; Harris, John C.; Cutchis, Protagoras N.; Cristion, John A.; Lesser, Ronald P.; Webber, W. Robert S.

    1993-01-01

    There are an estimated two million people with epilepsy in the United States. Many of these people do not respond to anti-epileptic drug therapy. Two devices can be developed to assist in the treatment of epilepsy. The first is a microcomputer-based system designed to process massive amounts of electroencephalogram (EEG) data collected during long-term monitoring of patients for the purpose of diagnosing seizures, assessing the effectiveness of medical therapy, or selecting patients for epilepsy surgery. Such a device would select and display important EEG events. Currently many such events are missed. A second device could be implanted and would detect seizures and initiate therapy. Both of these devices require a reliable seizure detection algorithm. A new algorithm is described. It is believed to represent an improvement over existing seizure detection algorithms because better signal features were selected and better standardization methods were used.

  9. 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…

  10. Novel automatic eye detection and tracking algorithm

    Science.gov (United States)

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

    2015-04-01

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

  11. 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…

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

    Science.gov (United States)

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

    2015-10-01

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

  13. Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

    DEFF Research Database (Denmark)

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

    2010-01-01

    An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose....... The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial...... 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....

  14. Fabric Defect Detection Based on Regional Growing PCNN

    OpenAIRE

    2012-01-01

    This paper presents an adaptive image segmentation method based on a new Regional Growing Pulse Coupled Neural Network (PCNN) model for detecting fabric defects. In this method, the pixels of analyzed image are mapped on the neurons in a pulse coupled neural network. Improved PCNN model and regional growing theory are combined in the light of the requirements for fabric defect detection. And the mean and variance value of the defect-free images are introduced into this model. The validation t...

  15. Evaluation of automatic mastitis detection equipment.

    Science.gov (United States)

    Gebre-Egziabher, A; Wood, H C; Robar, J D; Blankenagel, G

    1979-07-01

    An electronic sensor was evaluated as an instrument for early detection of mastitis. This method involved measuring the conductivity of milk continuously throughout the milking process and then establishing a conductivity ratio. The lowest conductivity measurement of the four quarters was a basis for assessing the degree of mastitis in the other quarters. This assumed that at least one of the quarters was normal at examination and the lowest reading was normal conductivity. The conductivity ratio was evaluated by comparison with the leukocyte concentration and combined leukocyte concentrations and cultural examiniations of milk samples from 1028 quarters. In healthy cows conductivities of milk from each of the quarters were similar. If, however, one or more quarters were infected, this milk showed higher conductivity compared to the noninfected quarter of the same cow. The conductivity ratio correctly identified 69% of the established cases of mastitis. For the Wisconsin Mastitis Test, 93.2% of the normal quarters were detected correctly by the conductivity ratio. Leukocyte counts were frequently high when there was no other evidence of mastitis. We believe the conductivity ratio is effective in detecting mastitis at an early stage of infection caused by most of the pathogenic microorganisms.

  16. Automatic Detection of Adenocarcinoma using Active Contours

    Directory of Open Access Journals (Sweden)

    NeelapalaAnilKumar

    2013-09-01

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

  17. Imaging technique for detection of internal defects of pickling cucumbers

    Science.gov (United States)

    Pickling cucumbers are susceptible to damage during harvest and postharvest handling and processing. While it is easier to detect external defects, it is difficult to detect internal defects such as bruises and hollow or split cucumbers. Hyperspectral imaging technique under transmittance mode was i...

  18. New detection method for rolling element and bearing defects

    Science.gov (United States)

    Burchill, R. F.; Frarey, J. L.

    1972-01-01

    Instrument for detecting defects in rolling elements of bearings is described. Detection depends on rate at which rolling elements impact defect and establishes envelope amplitude of ball resonant frequency. Block diagram of instrument is provided and results obtained in conducting tests are reported.

  19. Automatic player detection and identification for sports entertainment applications

    NARCIS (Netherlands)

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

    2014-01-01

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

  20. 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...

  1. 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

  2. Comparing different approaches for automatic pronunciation error detection

    NARCIS (Netherlands)

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

    2009-01-01

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

  3. Automatic player detection and identification for sports entertainment applications

    NARCIS (Netherlands)

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

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

  4. 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....

  5. Automatic Broken Rail Crack Detection Scheme

    Directory of Open Access Journals (Sweden)

    Komal B. Dandge

    2014-11-01

    Full Text Available In India, as the fuel cost continues to rise, railway transport plays an important role. Although there are, so many options of transportation are available like flights, trains, buses etc but most of the people prefer trains only as it is cost effective and comfortable way of travelling and hence in today’s world railway becomes the lifeline of India. When anybody goes through the daily news, they come across many accidents which are related to railroad. So there must be good railway safety for the people as the rail accidents are often dangerous in terms of the severity and death etc, when compared with the other transportation. There are several reasons present for railroad related accidents but the major reason is cracks in rails. It is the main cause of railway derailments and has the capacity to induce major damage to economy of the world. Therefore more efforts are necessary for achieving the good rail safety. This system introduced a method for rail crack detection. The proposed system is LED-LDR and Arduino based rail track detection scheme. It is cost effective and simple way of monitoring the condition of the rails on a continual basis for the improving the railway safety which consists of GSM module and Encoder.

  6. Background removal and weld defect detection based on energy distribution of image

    Institute of Scientific and Technical Information of China (English)

    Chi Dazhao; Gang Tie; Gao Shuangsheng

    2007-01-01

    The lateral wave in ultrasonic TOFD (time of flight diffraction) image has a tail in transit time, which disturbs the detection and evaluation of shallow weld defect. Meanwhile, the lateral wave and back-wall echo that act as background add redundant data in digital image processing. In order to separate defect wave from lateral wave and prepare the way for following image processing, an algorithm of background removal method named as mean-subtraction is developed. Based on this, an improved method by statistic of the energy distribution in the image is proposed. The results show that by choosing proper threshold value according to the axial energy distribution of the image, the background can be removed automatically and the defect section becomes predominant. Meanwhile, diffractive wave of shallow weld defect can be separated from lateral wave effectively.

  7. 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).

  8. Automatic Moth Detection from Trap Images for Pest Management

    OpenAIRE

    Ding, Weiguang; Taylor, Graham

    2016-01-01

    Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to ...

  9. Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration.

    Science.gov (United States)

    de la Escalera, Arturo; Armingol, Jose María

    2010-01-01

    There are increasing applications that require precise calibration of cameras to perform accurate measurements on objects located within images, and an automatic algorithm would reduce this time consuming calibration procedure. The method proposed in this article uses a pattern similar to that of a chess board, which is found automatically in each image, when no information regarding the number of rows or columns is supplied to aid its detection. This is carried out by means of a combined 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.

  10. @INGVterremoti: Tweeting the Automatic Detection of Earthquakes

    Science.gov (United States)

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

    2014-12-01

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

  11. Automatic Brain Tumour Detection Using Symmetry Information

    Directory of Open Access Journals (Sweden)

    Mr.Mubarak Jamadar

    2015-07-01

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

  12. 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.

  13. Method for automatic detection of wheezing in lung sounds

    Directory of Open Access Journals (Sweden)

    R.J. Riella

    2009-07-01

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

  14. Myocardial defect detection using PET-CT: phantom studies.

    Directory of Open Access Journals (Sweden)

    Eugene S Mananga

    Full Text Available It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP, Ordinary Poisson Ordered Subset Expectation Maximization (OP-OSEM, and Point Spread Function corrected OSEM (PSF-OSEM. We used the Channelized Hotelling Observer (CHO for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings.

  15. Myocardial defect detection using PET-CT: phantom studies.

    Science.gov (United States)

    Mananga, Eugene S; El Fakhri, Georges; Schaefferkoetter, Joshua; Bonab, Ali A; Ouyang, Jinsong

    2014-01-01

    It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP-OSEM), and Point Spread Function corrected OSEM (PSF-OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings.

  16. Fabric Defect Detection Using Modified Local Binary Patterns

    Directory of Open Access Journals (Sweden)

    A. Sheikhi

    2008-01-01

    Full Text Available Local binary patterns (LBPs are one of the features which have been used for texture classification. In this paper, a method based on using these features is proposed for fabric defect detection. In the training stage, at first step, LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each of these windows. Based on comparison with the reference feature vector, a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is multiresolution and gray scale invariant and can be used for defect detection in patterned and unpatterned fabrics. Because of its simplicity, online implementation is possible as well.

  17. 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...

  18. Spike Detection Based on Normalized Correlation with Automatic Template Generation

    Directory of Open Access Journals (Sweden)

    Wen-Jyi Hwang

    2014-06-01

    Full Text Available A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting.

  19. One step automated unpatterned wafer defect detection and classification

    Science.gov (United States)

    Dou, Lie; Kesler, Daniel; Bruno, William; Monjak, Charles; Hunt, Jim

    1998-11-01

    Automated detection and classification of crystalline defects on micro-grade silicon wafers is extremely important for integrated circuit (IC) device yield. High training cost, limited capability of classifying defects, increasing possibility of contamination, and unexpected human mistakes necessitate the need to replace the human visual inspection with automated defect inspection. The Laser Scanning Surface Inspection Systems (SSISs) equipped with the Reconvergent Specular Detection (RSD) apparatus are widely used for final wafer inspection. RSD, more commonly known as light channel detection (LC), is capable of detecting and classifying material defects by analyzing information from two independent phenomena, light scattering and reflecting. This paper presents a new technique including a new type of light channel detector to detect and classify wafer surface defects such as slipline dislocation, Epi spikes, Pits, and dimples. The optical system to study this technique consists of a particle scanner to detect and quantify light scattering events from contaminants on the wafer surface and a RSD apparatus (silicon photo detector). Compared with the light channel detector presently used in the wafer fabs, this new light channel technique provides higher sensitivity for small defect detection and more defect scattering signatures for defect classification. Epi protrusions (mounds and spikes), slip dislocations, voids, dimples, and some other common defect features and contamination on silicon wafers are studied using this equipment. The results are compared quantitatively with that of human visual inspection and confirmed by microscope or AFM. This new light channel technology could provide the real future solution to the wafer manufacturing industry for fully automated wafer inspection and defect characterization.

  20. Automatic landmarks detection in breast reconstruction aesthetic assessment.

    Science.gov (United States)

    Núñez-Benjumea, Francisco J; Serrano, Carmen; Acha, Begoña

    2015-01-01

    This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.

  1. 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.

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

    Science.gov (United States)

    Hess, Phillip; Colaninno, Robin C.

    2017-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-07-11

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

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

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

  6. Weighted ensemble based automatic detection of exudates in fundus photographs.

    Science.gov (United States)

    Prentasic, Pavle; Loncaric, Sven

    2014-01-01

    Diabetic retinopathy (DR) is a visual complication of diabetes, which has become one of the leading causes of preventable blindness in the world. Exudate detection is an important problem in automatic screening systems for detection of diabetic retinopathy using color fundus photographs. In this paper, we present a method for detection of exudates in color fundus photographs, which combines several preprocessing and candidate extraction algorithms to increase the exudate detection accuracy. The first stage of the method consists of an ensemble of several exudate candidate extraction algorithms. In the learning phase, simulated annealing is used to determine weights for combining the results of the ensemble candidate extraction algorithms. The second stage of the method uses a machine learning-based classification for detection of exudate regions. The experimental validation was performed using the DRiDB color fundus image set. The validation has demonstrated that the proposed method achieved higher accuracy in comparison to state-of-the art methods.

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

    Science.gov (United States)

    Yuan, Bo; He, Xiangqing; Liu, Ying

    2013-12-01

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

  8. A Weld Defects Detection System Based on a Spectrometer

    Directory of Open Access Journals (Sweden)

    Sadek C. A. Alfaro

    2009-04-01

    Full Text Available Improved product quality and production methods, and decreased production costs are important objectives of industries. Welding processes are part of this goal. There are many studies about monitoring and controlling welding process. This work presents a non-intrusive on-line monitoriment system and some algorithms capable of detecting GTAW weld defects. Some experiments were made to simulate weld defects by disturbing the electric arc. The data comes from a spectrometer which captures perturbations on the electric arc by the radiation emission of chosen lines. Algorithms based on change detection methods are used to indicate the presence and localization of those defects.

  9. Exploring combined dark and bright field illumination to improve the detection of defects on specular surfaces

    Science.gov (United States)

    Forte, Paulo M. F.; Felgueiras, P. E. R.; Ferreira, Flávio P.; Sousa, M. A.; Nunes-Pereira, Eduardo J.; Bret, Boris P. J.; Belsley, Michael S.

    2017-01-01

    An automatic optical inspection system for detecting local defects on specular surfaces is presented. The system uses an image display to produce a sequence of structured diffuse illumination patterns and a digital camera to acquire the corresponding sequence of images. An image enhancement algorithm, which measures the local intensity variations between bright- and dark-field illumination conditions, yields a final image in which the defects are revealed with a high contrast. Subsequently, an image segmentation algorithm, which compares statistically the enhanced image of the inspected surface with the corresponding image for a defect-free template, allows separating defects from non-defects with an adjusting decision threshold. The method can be applied to shiny surfaces of any material including metal, plastic and glass. The described method was tested on the plastic surface of a car dashboard system. We were able to detect not only scratches but also dust and fingerprints. In our experiment we observed a detection contrast increase from about 40%, when using an extended light source, to more than 90% when using a structured light source. The presented method is simple, robust and can be carried out with short cycle times, making it appropriate for applications in industrial environments.

  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. 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.

  12. Automatic event detection based on artificial neural networks

    Science.gov (United States)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

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

  13. Fabric Defect Detection Based on Regional Growing PCNN

    Directory of Open Access Journals (Sweden)

    Xiaoshu Si

    2012-10-01

    Full Text Available This paper presents an adaptive image segmentation method based on a new Regional Growing Pulse Coupled Neural Network (PCNN model for detecting fabric defects. In this method, the pixels of analyzed image are mapped on the neurons in a pulse coupled neural network. Improved PCNN model and regional growing theory are combined in the light of the requirements for fabric defect detection. And the mean and variance value of the defect-free images are introduced into this model. The validation tests on the developed algorithm were performed with fabric images from TILDA database and results showed that the proposed method is feasible and efficient for fabric defect detection

  14. Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging.

    Science.gov (United States)

    Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan

    2016-10-24

    Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l₁-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.

  15. Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging

    Directory of Open Access Journals (Sweden)

    Yichun Zhang

    2016-10-01

    Full Text Available Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.

  16. 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.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  18. Smart optical distance sensor for automatic welding detection

    Science.gov (United States)

    Kahl, Michael; Rinner, Stefan; Ettemeyer, Andreas

    2015-05-01

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

  19. 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.

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

    Science.gov (United States)

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

    2012-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-08-29

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

  2. Automatic Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

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

  3. Automatic Constraint Detection for 2D Layout Regularization

    KAUST Repository

    Jiang, Haiyong

    2015-09-18

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

  4. Automatic 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. Hyperspectral Imaging for Defect Detection of Pickling Cucumber

    Science.gov (United States)

    This book chapter reviews the recent progress on hyperspectral imaging technology for defect inspection of pickling cucumbers. The chapter first describes near-infrared hyperspectral reflectance imaging technique for the detection of bruises on pickling cucumbers. The technique showed good detection...

  6. Automatic Sea Bird Detection from High Resolution Aerial Imagery

    Science.gov (United States)

    Mader, S.; Grenzdörffer, G. J.

    2016-06-01

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

  7. Automatic detection of scoliotic curves in posteroanterior radiographs.

    Science.gov (United States)

    Duong, Luc; Cheriet, Farida; Labelle, Hubert

    2010-05-01

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

  8. Focusing Automatic Code Inspections

    NARCIS (Netherlands)

    Boogerd, C.J.

    2010-01-01

    Automatic Code Inspection tools help developers in early detection of defects in software. A well-known drawback of many automatic inspection approaches is that they yield too many warnings and require a clearer focus. In this thesis, we provide such focus by proposing two methods to prioritize

  9. Submicron Defect Detection in Periodic Structures Using Photorefractive Holography

    Science.gov (United States)

    Uhrich, Craig Edward

    Detection of defects in periodic objects is an important step in the manufacture of integrated circuits, particularly memory chips which are highly repetitive. As feature sizes shrink below 1 mum, automated detection of sub-micron defects becomes necessary. We present a real-time holographic system which allows the detection of sub-micron defects in periodic structures. The inspection technique is a marriage of Fourier spatial filtering and real-time phase conjugate holography. The system output is a near diffraction limited image of any defects in the pattern (defined to be any deviations from periodicity). Real-time holographic recording allows the system to adapt to the orientation and period of the object. The early chapters introduce the photorefractive effect and the generation of the phase conjugate of an optical wavefront using Bi_{12} SiO_{20}. Relevant properties of the optical Fourier transform are also discussed. The following sections present the system design rules and experimental techniques which allow the system to reliably detect sub-micron defects. The most important improvements over previous demonstrations are (1) a novel write/read holographic process which increases phase conjugate reflectivity by orders of magnitude, (2) control of the light polarization to allow efficient object illumination and collection of the diffracted signal, (3) avoidance of noise by moving the crystal away from the origin of the Fourier plane, and (4) compensation for the crystal aberrations with a holographic optical element. The final chapter presents results which clearly show enhancement of sub-micron defects and near perfect suppression of the surrounding periodic patterns. An area larger than 1 mm^2 is inspected in 20-60 seconds. Over 90% of the 0.5 mu m diameter defects on glass masks (viewed in reflection) are detected, and approximately 80% of defects in the 0.2 -0.3 μm range are found. On silicon wafers, the results are even better with over a 95% success

  10. Automatic behavior sensing for a bomb-detecting dog

    Science.gov (United States)

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

    2015-05-01

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

  11. Detection of Subsurface Defects in Concrete Bridge Deck Joints

    Directory of Open Access Journals (Sweden)

    Wonchang Choi

    2011-01-01

    Full Text Available Problem statement: The integrity of deck joints in highway bridges plays a major role to determine overall performance of bridge system. As the bridge maintenance program, the defects in deck joints have historically been detected by conventional non-destructive testing and evaluation methods such as visual inspection, chain-dragging and by the detecting sounds under the traffic. Future bridge maintenance challenges will demand the development of techniques and procedures to detect and monitor such defects before they become apparent. Approach: Two non-destructive methods; namely Ground Penetration Radar (GPR and Seismic Properties Analyzer (SPA were employed to assess the integrity of deck joins installed in North Carolina bridges. Results: The results obtained with the GPR and SPA allows to quantify the subsurface defects in bridge deck joints. Conclusion: The practical application and limitations of each method are discussed in this study.

  12. Gyroscope Pivot Bearing Dimension and Surface Defect Detection

    OpenAIRE

    2011-01-01

    Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumin...

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

    Science.gov (United States)

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

    2014-07-01

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

  14. BgCut: automatic ship detection from UAV images.

    Science.gov (United States)

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

    2014-01-01

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

  15. Automatic Detection of Magnetic delta in Sunspot Groups

    CERN Document Server

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

    2015-01-01

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

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

    CERN Document Server

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

    2014-01-01

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

  17. Research on Defects Detection by Image Processing of Thermographic Images

    Directory of Open Access Journals (Sweden)

    Shrestha Ranjit

    2015-10-01

    Full Text Available This paper presents the results of experimental investigation of thermal phenomena in a square shape (180 mm *180 mm STS 304 specimen with 10 mm thickness and artificial defects with circular cut-outs of varying depth and diameter at the back side. The material is aimed to be tested by means of thermal wave thermography. Lock-in thermography is employed for the detection of defects. The temperature field of the front surface of material tested is observed and analysed. The four point correlation algorithms are applied to extract phase angle of thermal wave’s harmonic component. Phase image are analyzed to find the qualitative information about the defects. Phase contrast method was used for better identification and analysis of the existing defects of the specimen.

  18. Variations in programmed phase defect size and its impact on defect detection signal intensity using at-wavelength inspection system

    Science.gov (United States)

    Amano, Tsuyoshi; Takagi, Noriaki; Abe, Tsukasa

    2015-10-01

    A programmed phase defect Extreme Ultraviolet (EUV) mask was fabricated and measurement repeatability of the defect size using a scanning probe microscope (SPM) was evaluated. The SPM measurement results indicated that the defect size variation as registered by the measurement repeatability were much smaller than the defect-to-defect variations. It means the defect-to-defect variation in size actually does exist. Some defects were found where their sizes before a multilayer coating (on quartz) were all the same but after the coat their sizes varied quite significantly when observed on the multilayer. This result indicated that it is difficult to estimate the phase defect size on quartz, whereas they can be accurately measured on multilayer. Influences of the defect size variation on defect detection signal intensity (DSI) using an actinic blank inspection (ABI) system were examined; their influences on the wafer printability were also examined. The DSI was strongly correlated with defect depth on the multilayer, and it was also indicated that the ABI can detect small variations in defect sizes. It was also confirmed that the impact of the phase defects on wafer printed CDs were proportional to the DSIs, and that the ABI has a potential to detect phase defect that could cause 5 % of the CD error when printing 16 nm dense lines.

  19. Detection and Characterization of Package Defects and Integrity Failure using Dynamic Scanning Infrared Thermography (DSIRT).

    Science.gov (United States)

    Morris, Scott A

    2016-02-01

    A dynamic scanning infrared thermography (DSIRT) system developed at the Univ. of Illinois Urbana-Champaign (UIUC) Packaging Lab relies on variation in transient thermal artifacts to indicate defects, and offers the possibility of characterization of many types of materials and structures. These include newer polymer and laminate-based structures for shelf-stable foods that lack a reliable, nondestructive method for inspection, which is a continuing safety issue. Preliminary trials were conducted on a polyester/aluminum foil/polypropylene retort pouch laminate containing artificially-induced failed seal and insulating inclusion defects ranging from 1 to 10 mm wide in the plane of the seal. The samples were placed in relative motion to a laterally positioned infrared laser, inducing heating through the plane of the seal. The emergent thermal artifact on the obverse side was sensed using either a bolometer camera or a thermopile sensor, with thermal anomalies indicating potential defects and the results of each sensors were compared. The bolometer camera detected defects to the limit of its measured optical resolution-approximately 1 mm at 20 cm-although the lower-resolution thermopile sensors were only capable of detecting 5 mm defects even at closer distances of approximately 5 mm. In addition, a supplementary magnification system was fitted to the bolometer camera which increased resolution but reduced field of view and would require a much higher frame rate to be useful. Automatic processing of the image data rapidly detected the model defects and can lead to development of an automated inspection system.  Much higher material throughput speeds are feasible using faster instruments, and the system is scalable. © 2015 Institute of Food Technologists®

  20. 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.

  1. Surface defect detection method for glass substrate using improved Otsu segmentation.

    Science.gov (United States)

    He, Zhiyong; Sun, Lining

    2015-11-20

    The image quality degradation caused by noise makes the automatic optical inspection of surface defects difficult. This paper develops a method based on thresholding segmentation to detect the surface defects in a glass substrate. Traditional Otsu segmentation has poor anti-noise ability. In order to improve the traditional Otsu method, a straight-line intercept histogram is established directly from the two-dimensional information of an image, and then Otsu criteria can be used to find the best intercept threshold from the one-dimensional histogram established. The improved Otsu algorithm not only is simpler than the two-dimensional Otsu methods, but also has a robust anti-noise ability. In the surface defect detection, the contrast feature between object and background is simply extracted after the segmentation based on the improved Otsu method, and surface defects can be decided by the threshold of the contrast feature. The data used in the experiments include the surface images acquired by a line-scan CCD camera. The experimental results demonstrate that the proposed method is effective and computationally efficient.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    M Hassaballah

    2011-07-01

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

  4. 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.

  5. Automatic failure detection of serial products using novelty filter

    Directory of Open Access Journals (Sweden)

    Márcia Helena Veleda Moita

    2013-08-01

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

  6. Automatic character detection and segmentation in natural scene images

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

  7. 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.

  8. Automatic probe artifact detection in MRI-guided cryoablation

    Science.gov (United States)

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

    2013-03-01

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

  9. 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

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

    Science.gov (United States)

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

    2011-06-01

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

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

    CERN Document Server

    Leloux, Jonathan; Luna, Alberto; Desportes, Adrien

    2014-01-01

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

  12. Ultrasonic sensor based defect detection and characterisation of ceramics.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh; Zhang, Tonzhua; Crouch, Ian

    2014-01-01

    Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. An approach to detecting deliberately introduced defects and micro-defects in 3D printed objects

    Science.gov (United States)

    Straub, Jeremy

    2017-05-01

    In prior work, Zeltmann, et al. demonstrated the negative impact that can be created by defects of various sizes in 3D printed objects. These defects may make the object unsuitable for its application or even present a hazard, if the object is being used for a safety-critical application. With the uses of 3D printing proliferating and consumer access to printers increasing, the desire of a nefarious individual or group to subvert the desired printing quality and safety attributes of a printer or printed object must be considered. Several different approaches to subversion may exist. Attackers may physically impair the functionality of the printer or launch a cyber-attack. Detecting introduced defects, from either attack, is critical to maintaining public trust in 3D printed objects and the technology. This paper presents an alternate approach. It applies a quality assurance technology based on visible light sensing to this challenge and assesses its capability for detecting introduced defects of multiple sizes.

  14. Detection of Wall-Thinned Defects Using IR Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ju Hyun; No, Young Gyu; Park, Soon Ho; Na, Man Gyun; Kim, Jin Weon; Jung, Hyun Chul; Kim, Kyeong Suk [Chosun University, Gwangju (Korea, Republic of)

    2011-10-15

    Recently, the number of the life-extended nuclear power plants (NPPs) is increasing. Thus, the degradation can occur in the various structures of the NPP secondary systems caused by the fatigue or corrosion, etc. Among these problems, the wall-thinned defect by the fluids of the inner wall can break the pipe due to the local stress concentrations. This cases have already emerged as an important issue in terms of ensuring the soundness and safety in NPPs. There are many NDT techniques to detect the wall-thinned defect from the inner wall. The infrared thermography which is one of these techniques provides real-time images by scanning the temperature of the target surface and then, converting it to the temperature. This technique can solve the existing problems by identifying the presence or absence of the defect through observation of the temperature difference

  15. A Multi Resolution Method for Detecting Defects in Fabric Images

    Directory of Open Access Journals (Sweden)

    Jianyun Ni

    2013-02-01

    Full Text Available This study proposes a novel technique for detecting defects in fabric image based on the features extracted using a new multi resolution analysis tool called Digital Curvelet Transform. The direction features of curvelet coefficients and texture features based on GLCM of curvelet coefficients act as the feature-sets for a k-nearest neighbor classifier. The validation tests on the developed algorithms were performed with images from TILDA’s Textile Texture Database. A comparative study between the GLCM-based, wavelet-based and the curvelet-based techniques has also been included. The high accuracy achieved by the proposed method suggests an efficient solution for fabric defect. Furthermore, the algorithm has good robustness to white noise. Note that, this study is the first documented attempt to explore the possibilities of a new multi resolution analysis tool called digital Curvelet Transform to address the problem of fabric defect.

  16. Detection of delamination defect inside timber by sonic IR

    Science.gov (United States)

    Choi, M. Y.; Park, J. H.; Kim, W. T.; Kang, K. S.

    2008-03-01

    In ultrasound excitation thermography, the injected ultrasound to an object is transformed to heat by thermo-structure effect and internal friction. The advantage of this technique is selectively sensitive to thermally active defects. The appearance of defects, which can be visualized by thermography camera, depends strongly on the method of excitation. In preliminary studies, ultrasonic excitation horns of ultrasonic manufacturing process are widely adopted for a polymer structure. However, it is needed that these horns are modified for improving the defect detection capability. This paper proposes a new ultrasonic excitation horns with tuning fork shape in NDT of wood material. Geometric conditions are optimized by FEA and application results by the developed horn are described and compared with those by a previous horn.

  17. Improvement to defect detection by ultrasonic data processing: the DTVG method; Amelioration de detection de defaut par ultrasons: la methode DTVG

    Energy Technology Data Exchange (ETDEWEB)

    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.

  18. 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.

  19. Defect-free functionalized graphene sensor for formaldehyde detection

    Science.gov (United States)

    Tang, Xiaohui; Mager, Nathalie; Vanhorenbeke, Beatrice; Hermans, Sophie; Raskin, Jean-Pierre

    2017-02-01

    Graphene has attracted much attention for sensing applications in recent years. Its largest surface-to-volume ratio makes graphene sensors able to potentially detect a single molecule and its extremely high carrier mobility ensures low electrical noise and energy consumption. However, pristine graphene is chemically inert and weakly adsorbs gas molecules, while defective and/or doped graphene has stronger adsorption ability (high sensitivity). The high sensitivity is related to the increased number of defects or traps in graphene where the gas molecules can be readily grafted, changing the sensor resistance. Nonetheless, similar resistance changes could be induced under exposure to different gases, resulting in a lack of selectivity. Functional groups differ drastically from defects or traps since the former selectively anchor specific molecules. Here, we comparatively investigate three functionalization routes and optimize a defect-free one (2,3,5,6,-Tetrafluorohydroquinone, TFQ molecules) for the fabrication of graphene gas sensors. We use TFQ organic molecules as chemical recognition links between graphene and formaldehyde, the most common indoor pollutant gas. The sensor demonstrates a high response and a good selectivity for formaldehyde compared with interfering organic vapours. Particularly, the sensor has a strong immunity to humidity. Our results highlight that defect-free functionalization based on organic molecules not only increases the sensor’s response but also its selectivity, paving the way to the design of efficient graphene-based sensors.

  20. Automatic Detection of Magnetic δ in Sunspot Groups

    Science.gov (United States)

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

    2016-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    张建荣; 李闻捷; 徐德安

    2002-01-01

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

  2. Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection.

    Science.gov (United States)

    Logeswaran, Rajasvaran

    2010-07-28

    To sufficiently improve magnetic resonance cholangiopancreatography (MRCP) quality to enable reliable computer-aided diagnosis (CAD). A set of image enhancement strategies that included filters (i.e. Gaussian, median, Wiener and Perona-Malik), wavelets (i.e. contourlet, ridgelet and a non-orthogonal noise compensation implementation), graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow, and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise, artifacts, partial volume effect and large inter- and intra-patient image intensity variations, all of which pose problems in application development. Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them, with the purpose of biliary disease detection. The results varied as expected since each algorithm capitalized on different characteristics of the images. For denoising, the Perona-Malik and contourlet approaches were found to be the most suitable. In terms of extraction of the significant biliary structures and removal of background, the thresholding approaches performed well. The interactive scheme performed the best, especially by using the strengths of the graph-cut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection. Tests show promising results for some techniques, but not others, as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.

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

    Science.gov (United States)

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

    2016-07-08

    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.

  4. Automatic GPRS Rainfall Detecting Set Based on P89C669

    Institute of Scientific and Technical Information of China (English)

    Yang,Lei; Wu,Kun

    2005-01-01

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

  5. [Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

    Science.gov (United States)

    Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

    2015-11-01

    Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect

  6. 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

  7. Air coupled ultrasonic detection of surface defects in food cans

    Science.gov (United States)

    Seco, Fernando; Ramón Jiménez, Antonio; del Castillo, María Dolores

    2006-06-01

    In this paper, we describe an ultrasonic inspection system used for detection of surface defects in food cans. The system operates in the pulse-echo mode and analyses the 220 kHz ultrasonic signal backscattered by the object. The classification of samples into valid or defective is achieved with χ2 statistics and the k nearest neighbour method, applied to features computed from the envelope of the ultrasonic echo. The performance of the system is demonstrated empirically in detection of the presence of the pull tab on the removable lid of easy-open food cans, in a production line. It is found that three factors limit the performance of the classification: the misalignment of the samples, their separation of the ultrasonic transducer, and the vibration of the conveyor belt. When these factors are controlled, classification success rates between 94% and 99% are achieved.

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

    Directory of Open Access Journals (Sweden)

    Hans Frimmel

    2001-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-01-01

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

  10. A Multi Resolution Method for Detecting Defects in Fabric Images

    OpenAIRE

    Jianyun Ni; Jing Luo; Zaiping Chen; Enzeng Dong

    2013-01-01

    This study proposes a novel technique for detecting defects in fabric image based on the features extracted using a new multi resolution analysis tool called Digital Curvelet Transform. The direction features of curvelet coefficients and texture features based on GLCM of curvelet coefficients act as the feature-sets for a k-nearest neighbor classifier. The validation tests on the developed algorithms were performed with images from TILDA’s Textile Texture Database. A comparative study between...

  11. 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...

  12. automatic data collection design for neural networks detection of ...

    African Journals Online (AJOL)

    Dr Obe

    University of Nigeria, Nsukka. E-mail: ... paper examines some formal procedures for data collection and proposes designing an automatic .... 4.2 Proposed Architecture for Automatic .... specific application through a learning process. ... space R. D to match (represent) regions that include relatively large amount of samples.

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

    Science.gov (United States)

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

    2010-03-01

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

  14. Statistical language analysis for automatic exfiltration event detection.

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, David Gerald

    2010-04-01

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

  15. Textural defect detect using a revised ant colony clustering algorithm

    Science.gov (United States)

    Zou, Chao; Xiao, Li; Wang, Bingwen

    2007-11-01

    We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

  16. QR码印刷品缺陷检测%Defect Detection of Printed QR Code Image

    Institute of Scientific and Technical Information of China (English)

    陈星; 徐迎晖; 肖青海

    2015-01-01

    Propose an effective solution of the defect detection of printed QR code image to resolve the problems of print quality than white line,black line,white block or black block appears in printed QR code image. Applying machine vision to the QR code defect in-spection of printing products,can automatically identify defective sample,so as to solve the problem brought by the artificial detection. With the reference functions of bar code decoding and image processing in HALCON,the solution uses Visual C++ to accomplish these key steps of generating neighborhood model,homogeneous projective transformation and image matching,to implement the effective de-tection for QR code printed in black and white arrows or piece of printing problems,such as black and white. The experimental results show that the solution can automatically extract bar code in test sample,quickly and accurately execute image matching,effectively detect printed defect and has good robustness.%针对QR码印刷品中出现的黑白拉线或黑白块等印刷问题,文中提出了一种有效的QR码印刷品缺陷检测的解决方案。将机器视觉应用到QR码印刷制品的缺陷检测中去,可以自动识别带有缺陷的样本,从而解决了人工检测所带来的问题。结合HALCON的条码识读和图像处理相关算子,以Visual C++编程实现解决方案中的邻域模板生成、条码区域矫正和图像匹配等关键步骤,来实现对QR码印刷品中出现的黑白拉线或黑白块等印刷问题进行有效的检测。实验结果表明,此方案能从测试样本图像中自动提取条码,并且快速而又精确地完成图像匹配,有效地检测出印刷缺陷,并具有良好的鲁棒性。

  17. Detection Mechanism of Parallel Defect using Scanning Inductive Thermography

    Science.gov (United States)

    Zuo, Xianzhang; Song, Benchu; Hu, Yongjiang; He, Yunze

    2017-06-01

    Aiming at the requirement of workpiece integrity for parts processing line, on-line detection using inductive heating thermography for the moving workpieces on the assembly line is studied. In this paper, the detection mechanism of pulsed eddy current thermography for moving workpieces defects is analysed. A two-dimensional model of a magnetic material (45 steel), on which there is a crack parallel to the coil is established by the finite element software named COMSOL 5.2. By analysing the changes of the temperature curves, normalized curves and the temperature difference curves, the optimal detection area for parallel cracks is proposed. The consistency of the conclusions is verified by the experimental platform. The paper can provide a theoretical guidance for quantitative detection using eddy current thermography.

  18. 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

  19. Review of thermal imaging systems in composite defect detection

    Science.gov (United States)

    Jorge Aldave, I.; Venegas Bosom, P.; Vega González, L.; López de Santiago, I.; Vollheim, B.; Krausz, L.; Georges, M.

    2013-11-01

    Thermal imaging technologies are widely used at present in many industrial areas, while being nowadays more and more employed in R&D&i activities. This article focuses on the comparison of the results obtained with commercially available non-experimental infrared (IR) cameras in the field of non-destructive defect detection. One of the cameras belongs to the FLIR SC5000 series, which is a Medium Wavelength Infrared (MWIR) camera, and the other two cameras are from the high-end ImageIR series manufactured by InfraTec GmbH: the ImageIR 8300 also belongs to the class of MWIR cameras and the ImageIR 8800 is a Long Wavelength Infrared (LWIR) camera. The comparative study is carried out by means of inspecting three different calibrated and induced defect samples with these three cameras using similar excitation sources, so that the configuration and lay out of the tests are comparable with each other. Additionally, after every inspection, a mathematical post-processing is applied to the resulting raw thermal images in order to enhance the detection of defects present in the samples.

  20. 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.

  1. Automatic classification and defect verification based on inspection technology with lithography simulation

    Science.gov (United States)

    Kato, Masaya; Inuzuka, Hideki; Kosuge, Takeshi; Yoshikawa, Shingo; Kanno, Kayoko; Imai, Hidemichi; Miyashita, Hiroyuki; Vacca, Anthony; Fiekowsky, Peter; Fiekowsky, Dan

    2015-10-01

    Even small defects on the main patterns can create killer defects on the wafer, whereas the same defect on or near the decorative patterns may be completely benign to the wafer functionality. This ambiguity often causes operators and engineers to put a mask "on hold" to be analyzed by an AIMS™ tool which slows the manufacturing time and increases mask cost. In order to streamline the process, mask shops need a reliable way to quickly identify the wafer impact of defects during mask inspection review reducing the number of defects requiring AIMS™ analysis. Source Mask Optimization (SMO) techniques are now common on sub 20nm node critical reticle patterns These techniques create complex reticle patterns which often makes it difficult for inspection tool operators to identify the desired wafer pattern from the surrounding nonprinting patterns in advanced masks such as SMO, Inverse Lithography Technology (ILT), Negative Tone Development (NTD). In this study, we have tested a system that generates aerial simulation images directly from the inspection tool images. The resulting defect dispositions from a program defect test mask along with numerous production mask defects have been compared to the dispositions attained from AIMS™ analysis. The results of our comparisons are presented, as well as the impact to mask shop productivity.

  2. Gyroscope Pivot Bearing Dimension and Surface Defect Detection

    Directory of Open Access Journals (Sweden)

    Xudong Li

    2011-03-01

    Full Text Available Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective.

  3. Gyroscope pivot bearing dimension and surface defect detection.

    Science.gov (United States)

    Ge, Wenqian; Zhao, Huijie; Li, Xudong

    2011-01-01

    Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective.

  4. 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

  5. Review of thermal imaging systems in composite defect detection

    OpenAIRE

    Jorge, Iagoba; Venegas, Pablo; Vega, Laura; Lopez, Ion; Vollheim, Birgit; Krausz, Lennard; Georges, Marc

    2013-01-01

    Thermal imaging technologies are widely used at present in many industrial areas, while being nowadays more and more employed in R&D&i activities. This article focuses on the comparison of the results obtained with commercially available non-experimental infrared (IR) cameras in the field of non-destructive defect detection. One of the cameras belongs to the FLIR SC5000 series, which is a medium wavelength infrared (MWIR) camera, and the other two cameras are from the high-end ImageIR series ...

  6. 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.

  7. 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 ...

  8. Defect Prevention and Detection in Software for Automated Test Equipment

    Energy Technology Data Exchange (ETDEWEB)

    E. Bean

    2006-11-30

    Software for automated test equipment can be tedious and monotonous making it just as error-prone as other software. Active defect prevention and detection are also important for test applications. Incomplete or unclear requirements, a cryptic syntax used for some test applications—especially script-based test sets, variability in syntax or structure, and changing requirements are among the problems encountered in one tester. Such problems are common to all software but can be particularly problematic in test equipment software intended to test another product. Each of these issues increases the probability of error injection during test application development. This report describes a test application development tool designed to address these issues and others for a particular piece of test equipment. By addressing these problems in the development environment, the tool has powerful built-in defect prevention and detection capabilities. Regular expressions are widely used in the development tool as a means of formally defining test equipment requirements for the test application and verifying conformance to those requirements. A novel means of using regular expressions to perform range checking was developed. A reduction in rework and increased productivity are the results. These capabilities are described along with lessons learned and their applicability to other test equipment software. The test application development tool, or “application builder”, is known as the PT3800 AM Creation, Revision and Archiving Tool (PACRAT).

  9. 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...

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

    Science.gov (United States)

    Chen, Li-dan; Chen, Kai-kao

    2012-01-01

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

  11. Thermal imaging for detection of SM45C subsurface defects using active infrared thermography techniques

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Yoon Jae; Ranjit, Shrestha; Kim, Won Tae [Kongju National University, Cheonan (Korea, Republic of)

    2015-06-15

    Active thermography techniques have the capability of inspecting a broad range simultaneously. By evaluating the phase difference between the defected area and the healthy area, the technique indicates the qualitative location and size of the defect. Previously, the development of the defect detection method used a variety of materials and the test specimen was done. In this study, the proposed technique of lock-in is verified with artificial specimens that have different size and depth of subsurface defects. Finally, the defect detection capability was evaluated using comparisons of the phase image and the amplitude image according to the size and depth of defects.

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

    NARCIS (Netherlands)

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

    2012-01-01

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

  13. Automatic Detection of Sub-Kilometer Craters in High Resolution Images of Mars

    Science.gov (United States)

    Urbach, E. R.; Stepinski, T. F.

    2008-03-01

    A method for automatic detection of impact craters in high resolution images of Mars is presented. This new method enables detection of sub-kilometer craters that are too small to be cataloged by previous methods and too numerous for manual detection.

  14. Fabric Defect Detection Technique Based on Two-double Neural Network

    Institute of Scientific and Technical Information of China (English)

    XIE Chun-ping; XU Bo-jun; CHEN Jun-jie

    2008-01-01

    This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimensional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.

  15. Real-time color-based texture analysis for sophisticated defect detection on wooden surfaces

    Science.gov (United States)

    Polzleitner, Wolfgang; Schwingshakl, Gert

    2004-10-01

    We describe a scanning system developed for the classification and grading of surfaces of wooden tiles. The system uses color imaging sensors to analyse the surfaces of either hard- or softwood material in terms of the texture formed by grain lines (orientation, spatial frequency, and color), various types of colorization, and other defects like knots, heart wood, cracks, holes, etc. The analysis requires two major tracks: the assignment of a tile to its texture class (like A, B, C, 1, 2, 3, Waste), and the detection of defects that decrease the commercial value of the tile (heart wood, knots, etc.). The system was initially developed under the international IMS program (Intelligent Manufacturing Systems) by an industry consortium. During the last two years it has been further developed, and several industrial systems have been installed, and are presently used in production of hardwood flooring. The methods implemented reflect some of the latest developments in the field of pattern recognition: genetic feature selection, two-dimensional second order statistics, special color space transforms, and classification by neural networks. In the industrial scenario we describe, many of the features defining a class cannot be described mathematically. Consequently a focus was the design of a learning architecture, where prototype texture samples are presented to the system, which then automatically finds the internal representation necessary for classification. The methods used in this approach have a wide applicability to problems of inspection, sorting, and optimization of high-value material typically used in the furniture, flooring, and related wood manufacturing industries.

  16. Review of automatic detection of pig behaviours by using image analysis

    Science.gov (United States)

    Han, Shuqing; Zhang, Jianhua; Zhu, Mengshuai; Wu, Jianzhai; Kong, Fantao

    2017-06-01

    Automatic detection of lying, moving, feeding, drinking, and aggressive behaviours of pigs by means of image analysis can save observation input by staff. It would help staff make early detection of diseases or injuries of pigs during breeding and improve management efficiency of swine industry. This study describes the progress of pig behaviour detection based on image analysis and advancement in image segmentation of pig body, segmentation of pig adhesion and extraction of pig behaviour characteristic parameters. Challenges for achieving automatic detection of pig behaviours were summarized.

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

    Institute of Scientific and Technical Information of China (English)

    LI Deren; SUI Haigang; XIAO Ping

    2003-01-01

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

  18. Defect detection in metals using electronic speckle pattern interferometry

    Energy Technology Data Exchange (ETDEWEB)

    Andres Zarate, Esteban; Custodio G, Eden [Universidad Juarez Autonoma de Tabasco, DACB, Cunduacan, Tabasco, 86680 (Mexico); Trevino-Palacios, Carlos G. [Instituto Nacional de Astrofisica, Optica y Electronica, Puebla 72000 (Mexico); Rodriguez-Vera, Ramon; Puga-Soberanes, Hector J. [Centro de Investigaciones en Optica, Loma del Bosque 115, Leon (Mexico)

    2005-07-15

    We use the out-of-plane electronic speckle pattern interferometry (ESPI) technique to observe cracks and fracture defects on 6061 aluminum plates under thermal stress. The geometrical shape of the ESPI pattern confirmed the existence of defects. We were able to differentiate between cracks and fracture defects using a non-contact and non-destructive technique.

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

    CERN Document Server

    Kundu, Srimanta; Nasipuri, Mita

    2011-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    李林; 强秀华; 邹斌

    2012-01-01

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

  1. Measurement uncertainty on subsurface defects detection using active infrared thermographic technique

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Yoon Jae; Kim [Kongju National University, Cheonan (Korea, Republic of); Choi, Won Jae [Center for Safety Measurements, Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of)

    2015-10-15

    Active infrared thermography methods have been known to possess good fault detection capabilities for the detection of defects in materials compared to the conventional passive thermal infrared imaging techniques. However, the reliability of the technique has been under scrutiny. This paper proposes the lock-in thermography technique for the detection and estimation of artificial subsurface defect size and depth with uncertainty measurement.

  2. Application of DBM tool for detection of EUV mask defect

    Science.gov (United States)

    Yoo, Gyun; Kim, Jungchan; Park, Chanha; Lee, Taehyeong; Ji, Sunkeun; Yang, Hyunjo; Yim, Donggyu; Park, Byeongjun; Maruyama, Kotaro; Yamamoto, Masahiro

    2013-04-01

    Extreme ultraviolet lithography (EUVL) is one of the most leading lithography technologies for high volume manufacturing. The EUVL is based on reflective optic system therefore critical patterning issues are arisen from the surface of photomask. Defects below and inside of the multilayer or absorber of EUV photomask is one of the most critical issues to implement EUV lithography in mass production. It is very important to pick out and repair printable mask defects. Unfortunately, however, infrastructure for securing the defect free photomask such as inspection tool is still under development furthermore it does not seem to be ready soon. In order to overcome the lack of infrastructures for EUV mask inspection, we will discuss an alternative methodology which is based on wafer inspection results using DBM (Design Based Metrology) tool. It is very challenging for metrology to quantify real mask defect from wafer inspection result since various sources are possible contributor. One of them is random defect comes from poor CD uniformity. It is probable that those random defects are majority of a defect list including real mask defects. It is obvious that CD uniformity should be considered to pick out only a real mask defect. In this paper, the methodology to determine real mask defect from the wafer inspection results will be discussed. Experiments are carried out on contact layer and on metal layer using mask defect inspection tool, Teron(KLA6xx) and DBM (Design Based Metrology) tool, NGR2170™.

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

    Science.gov (United States)

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

    2009-11-01

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

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

    Science.gov (United States)

    Qu, Ming

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

  5. 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.

  6. 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.

  7. Study on Defect Detecting and Locating Method of Tubular Cylindrical Conductor

    Directory of Open Access Journals (Sweden)

    Yinchuan Wu

    2013-06-01

    Full Text Available This paper is to present a defect detecting and locating method of tubular cylindrical conductor based on alternating current impedance measurement theory. A defect estimation  can be made through the impedance measurement of some distance on the inner and the outer surfaces of the tubular cylindrical conductor. A defect in the thickness direction can be located by the skin effect influence on impedance and the frequency change in exciting current. The results show that: this method can effectively identify and locate a defect, and therefore should be promoted in the defect detecting of metal materials in other shapes.

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

    NARCIS (Netherlands)

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

    2007-01-01

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

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

    NARCIS (Netherlands)

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

    2007-01-01

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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    Science.gov (United States)

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

  12. 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

  13. 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

    Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially...

  14. Yuma proving grounds automatic UXO detection using biomorphic robots

    Energy Technology Data Exchange (ETDEWEB)

    Tilden, M.W.

    1996-07-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-11-15

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

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

    Science.gov (United States)

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

    2007-11-01

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

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

    Science.gov (United States)

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

    2015-03-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

  1. Ultrasonic defect detection and size approximation using thin annulus probes

    Energy Technology Data Exchange (ETDEWEB)

    Light, G.M.; Fisher, J.L.; Tennis, R.F.; Stolte, J.S.; Hendrix, G.J. [Southwest Research Inst., San Antonio, TX (United States)

    1994-12-31

    Concern has been generated recently about the capabilities of performing nondestructive evaluation (NDE) of the closure head penetrations in nuclear-reactor pressure vessels (PV). These penetrations, primarily for instrumentation and control rod drive mechanisms (CRDMs), are usually thick-walled Inconel tubes, shrink-fitted into the steel closure head. The penetrations are then welded between the outside surface of the penetration and inside surface of the closure head. Stress corrosion cracks initiating at the inner surface of the penetration have been reported at several plants. Through wall cracks in the CRDM penetration or CRDM weld could lead to loss of PV coolant. The penetration presents a complex geometry for conventional NDE techniques. A thermal sleeve, through which pass the mechanical linkages for control-rod operation, is inserted into the penetration so that only a small annulus (nominally 3 mm) exists between the thermal sleeve and inside surface of the penetration. SwRI has developed and evaluated ultrasonic techniques for sizing defects in this area. Long, thin probes were designed to fit into the annulus to carry irrigated ultrasonic transducers into the region of interest. The probes were used to detect cracks in the penetration and to estimate remaining wall thickness.

  2. Automatic Defect Inspection of PCB Bare Board Based on Machine Vision%基于机器视觉PCB裸板缺陷自动检测研究

    Institute of Scientific and Technical Information of China (English)

    刘百芬; 李海文; 张姝颖; 林德欣

    2014-01-01

    AppIying to the method of reference comparison to automatic defect inspection of PCB bare board based on machine vision.Camera captures muItipIe standard PCB image and caIcuIate its average gray get standard circuit board image tn the same position,image registration compIeted by standard PCB image under test PCB image's corner detection and cor-ner registration,adopting to standard PCB image under test PCB image adopt gray-scaIe transformation,fiItering,binarization, XOR and other image processing respectiveIy to detect the position of the defect area.%运用参考比较法对机器视觉PCB裸板缺陷检测进行了研究。在相机摄像头下同一位置采集多幅标准PCB图像累加求平均值得到标准电路板图像,运用Harris角点算法进行标准电路板图像和待测电路板图像的配准,分别对标准电路板图像和待测电路板图像进行灰度变换、中值滤波、二值化、异或等图像处理检测出缺陷区域,然后通过形态学消除伪缺陷,实验证明,该检测方法有较高的准确率。

  3. Efficient Fruit Defect Detection and Glare removal Algorithm by anisotropic diffusion and 2D Gabor filter

    CERN Document Server

    Katyal, Vini

    2012-01-01

    This paper focuses on fruit defect detection and glare removal using morphological operations, Glare removal can be considered as an important preprocessing step as uneven lighting may introduce it in images, which hamper the results produced through segmentation by Gabor filters .The problem of glare in images is very pronounced sometimes due to the unusual reflectance from the camera sensor or stray light entering, this method counteracts this problem and makes the defect detection much more pronounced. Anisotropic diffusion is used for further smoothening of the images and removing the high energy regions in an image for better defect detection and makes the defects more retrievable. Our algorithm is robust and scalable the employability of a particular mask for glare removal has been checked and proved useful for counteracting.this problem, anisotropic diffusion further enhances the defects with its use further Optimal Gabor filter at various orientations is used for defect detection.

  4. A Comparative Evaluation of Automatic Rock Detection Methods

    Science.gov (United States)

    Thompson, D. R.; Castano, R.

    2006-12-01

    Enabling rock detection allows rovers to make the most of each command cycle by performing autonomous site characterization, and prioritization of the most important data for downlink. On Earth these algorithms assist data analysis by automating laborious image annotation tasks. We compare the performance of several detection algorithms on a representative set of Mars Exploration Rover data. Tested algorithms include strategies based on pixel intensity (Castano et al., 2004), filter cascades (Viola et al., 2002), shading models (Gulick et al., 2001) and stereo range data (Gor et al., 2001). The test dataset consists of 13 navigation images and 104 panoramic camera images under various terrain and lighting conditions. Together these images contain over 50,000 manually-labeled rocks. We assess detectors' performance on autonomous geology tasks: identifying targets for spectroscopy, estimating the fractional area of terrain covered by rocks, and identifying the contour outlines of rocks above 4cm in length. While detection performance varied considerably across different detection strategies, images, and tasks, some general trends are apparent. All rock detection algorithms underestimated fractional coverage area to varying degrees. Accurate identification of contour outlines was especially difficult; most detectors exhibit low recall and various biases in rock shape and size. However, all detectors performed significantly better than random on the target selection task, paralleling recent successes in autonomous spectrometer targeting. Comparative evaluation on field-typical datasets will remain important as rock detection technologies continue to mature. References: Castano et al, Intensity-based Rock Detection for Acquiring Onboard Rover Science, LPSC 2004. Gor et al, Autonomous rock detection for Mars terrain, AIAA Space 2001. Gulick et al, Autonomous image analysis during the 1999 Marsokhod rover field test, JGR 2001. Viola et al, Robust Real-Time Object

  5. Effect of Lock-in Frequency on Wall-Thinned Defects Detection Using IR Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kwae Hwan; Kim, Ju Hyun; Na, Man Gyun; Kim, Jin Weon; Jung, Hyun Cheol; Kim, Kyeong Suk [Chosun University, Gwangju (Korea, Republic of)

    2014-08-15

    Recently, various inspection techniques for improving the safety of nuclear power plants (NPPs) are being studied. Wall-thinned defect of the pipe are a major cause of reducing the NPP integrity. The purpose of this study was to detect the wall-thinned defects of Nuclear Power Plant (NPP) pipes using the lock-in infrared (IR) thermography method. When using the technique of lock-in IR thermography to detect wall-thinned defects of the pipe, it is very important to select the appropriate lock-in frequency. In this study, we applied a cooling and heating method for detecting wall-thinned defects of the pipe of NPPs.

  6. Fabric defect detection using the wavelet transform in an ARM processor

    Science.gov (United States)

    Fernández, J. A.; Orjuela, S. A.; Álvarez, J.; Philips, W.

    2012-01-01

    Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations.

  7. 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.

  8. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

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

  9. Necessity and effects of dynamic systems for railway wheel defect detection

    Directory of Open Access Journals (Sweden)

    S. Vesković

    2012-07-01

    Full Text Available State of railway vehicles highly influences transport safety due to vehicle derailments and in the same time worsens the quality of freight and passenger transportation. One of important elements that influence the state of railway vehicles is the wheel state. Wheel defects are common in railway transport. Therefore, timely defect detection is very important. This paper presents ways and effects of timely detection of wheel defects.

  10. Automatic colonic lesion detection and tracking in endoscopic videos

    Science.gov (United States)

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

    2011-03-01

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

  11. 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.

  12. Application defects detection in the small bore pipe using infrared thermography technique

    Energy Technology Data Exchange (ETDEWEB)

    Yun, Kyung Won; Kim, Dong Lyul; Jung, Hyun Chul; Hong, Dong Pyo; Kim, Kyeong Suk [Chosun Univ., Gwangju (Korea, Republic of)

    2013-02-15

    In the advanced research deducted infrared thermography (IRT) test using 4 inch pipe with artificial wall thinning defect to measure on the wall thinned nuclear pipe components. This study conducted for defect detection condition of nuclear small bore pipe research using deducted condition in the advanced research. Defect process is processed by change for defect length, circumferential direction angle, wall thinning depth. In the used equipment IR camera and two halogen lamps, whose full power capacity is 1 kW, halogen lamps and Target pipe experiment performed to the distance of the changed 1 m, 1.5 m, 2 m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length. Artificial defect of 4 inch pipe is high reliability in the 2 m, but small bore pipe is in the 1.5 m from defect clearly was detected.

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

    Science.gov (United States)

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

    1995-03-01

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

  14. Automatic Tree-Crown Detection in Challenging Scenarios

    Science.gov (United States)

    Bulatov, Dimitri; Wayand, Isabell; Schilling, Hendrik

    2016-06-01

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

  15. Automatic Fatigue Detection of Drivers through Yawning Analysis

    Science.gov (United States)

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

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

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

    Science.gov (United States)

    Lecron, Fabian; Benjelloun, Mohammed; Mahmoudi, Saïd

    2012-02-01

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

  17. Weak scratch detection and defect classification methods for a large-aperture optical element

    Science.gov (United States)

    Tao, Xian; Xu, De; Zhang, Zheng-Tao; Zhang, Feng; Liu, Xi-Long; Zhang, Da-Peng

    2017-03-01

    Surface defects on optics cause optic failure and heavy loss to the optical system. Therefore, surface defects on optics must be carefully inspected. This paper proposes a coarse-to-fine detection strategy of weak scratches in complicated dark-field images. First, all possible scratches are detected based on bionic vision. Then, each possible scratch is precisely positioned and connected to a complete scratch by the LSD and a priori knowledge. Finally, multiple scratches with various types can be detected in dark-field images. To classify defects and pollutants, a classification method based on GIST features is proposed. This paper uses many real dark-field images as experimental images. The results show that this method can detect multiple types of weak scratches in complex images and that the defects can be correctly distinguished with interference. This method satisfies the real-time and accurate detection requirements of surface defects.

  18. 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.

  19. Application angle of defects detection in the pipe using lock-in infrared thermography

    Energy Technology Data Exchange (ETDEWEB)

    Yun, Kyung Won; Go, Gyeong Uk; Kim, Jin Weon; Jung, Hyun Chul; Kim, Kyung Suk [Chosun University, Gwangju (Korea, Republic of)

    2013-08-15

    This perform research of angle rated defect detection conditions and nuclear power plant piping defect detection by lock-In infrared thermography technique. Defects were processed according to change for wall-thinning length, Circumference orientation angle and wall-thinning depth. In the used equipment IR camera and two halogen lamps, whose full power captaincy is 1 kW, halogen lamps and target pipe's distance fixed 2 m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length. Reliability of lock-In infrared thermography data is higher than Infrared thermography data. This through research, Shape of angle rated defect is identified industry place. It help various angles defect detection in the nuclear power plant in operation.

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  1. Automatic detection of REM sleep in subjects without atonia

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Nikolic, Miki

    2012-01-01

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

  2. 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 YouTu

  3. Automatic error detection in alignments for speech synthesis

    CSIR Research Space (South Africa)

    Barnard, E

    2006-11-01

    Full Text Available of the errors that existed in a manual segmentation were detected by this process, while flagging less than a quarter of all segments. Different phoneme classes are handled with differing amounts of success, with vowels being the most troublesome...

  4. Automatic Detection and Decoding of Photogrammetric Coded Targets

    OpenAIRE

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

    2016-01-01

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

  5. 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.

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

    Directory of Open Access Journals (Sweden)

    Dorothée Coppieters ’t Wallant

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Otakar Pinkas

    2015-10-01

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

  8. Detecting Defects in Aircraft Materials by Nuclear Technique (pas)

    Science.gov (United States)

    Badawi, Emad. A.

    Positron annihilation spectroscopy (PAS) is one of the nuclear techniques used in material science. The present measurements are used to study the behavior of defect concentration in one of the most important materials aluminum alloys which is the 7075 alloy. It has been shown that positrons can become trapped at imperfect locations in solids and their mean lifetime can be influenced by changes in the concentration of such defects. No changes have been observed in the mean lifetime values after the saturation of defect concentration. The mean lifetime and trapping rates are studied for samples deformed up to 58.3%. The concentration of defect range vary from 1015 to 1018cm-3 at the thickness reduction from 2.3 to 58.3%. The dislocation density varies from 108 to 1011cm/cm3.

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

    Science.gov (United States)

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

    2009-03-01

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

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

    OpenAIRE

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

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of YouTube users‟ behaviour and their characteristics through expert knowledge. Based on experts‟ knowledge, the system assigns a score to the users, which represents their level of “bulliness” based on th...

  11. Alcohol Detection and Automatic Drunken Drive Avoiding System

    Directory of Open Access Journals (Sweden)

    Prof. P. H. Kulkarni

    2014-04-01

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

  12. Automatic Deformation Detection for Aircraft Engine Disk Inspection

    Directory of Open Access Journals (Sweden)

    Dirk Padfield

    2007-08-01

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

  13. DETECTING DEFECTS IN AIRCRAFT MATERIALS BY NUCLEAR TECHNIQUE (PAS)

    OpenAIRE

    EMAD A. BADAWI

    2005-01-01

    Positron annihilation spectroscopy (PAS) is one of the nuclear techniques used in material science. The present measurements are used to study the behavior of defect concentration in one of the most important materials aluminum alloys which is the 7075 alloy. It has been shown that positrons can become trapped at imperfect locations in solids and their mean lifetime can be influenced by changes in the concentration of such defects. No changes have been observed in the mean lifetime values aft...

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    NARCIS (Netherlands)

    Hogeveen, H.

    2011-01-01

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

  16. 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 /x

  17. 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...

  18. 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

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

    Science.gov (United States)

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

    2006-03-01

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

  20. Automatic detection of service initiation signals used in bars

    Directory of Open Access Journals (Sweden)

    Sebastian eLoth

    2013-08-01

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

  1. 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

  2. Automatic Damage Detection for Sensitive Cultural Heritage Sites

    Science.gov (United States)

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

    2016-06-01

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

  3. Automatic oil spill detection on quad polarimetric UAVSAR imagery

    Science.gov (United States)

    Rahnemoonfar, Maryam; Dhakal, Shanti

    2016-05-01

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

  4. 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.

  5. Automatic localization method of small casting defect based on deep learning feature%基于深度学习特征匹配的铸件微小缺陷自动定位方法

    Institute of Scientific and Technical Information of China (English)

    余永维; 杜柳青; 曾翠兰; 张建恒

    2016-01-01

    Aiming at the requirement of the automatic localization of precise casting small defect in radiographic real-time imaging detection,a three-dimensional localization method for casting defects based on deep learning feature matching is proposed.Simulating the central-peripheral difference algorithm of selective attention mechanism,taking the visual saliency as the scale,the small defect and its region are detected from complex background of the ray images.The central point defined in the defect region is taken as the point to be matched.Then the deep convolution neural network is constructed to automatically extract the deep learning features of the small defect region.Through the similarity of the deep learning feature vector,the automatic matching of the same small defect point in the projection images at different viewing angles is achieved.Finally,based on the principle of translation parallax distance measurement, the 3D spatial coordinates of the defect matching points are calculated.Experiment results show that the method of deep learning feature matching can correctly search the same defect point in the projection images before and after translation.On the basis of this feature matching,the automatic and accurate localization of the small defect matching point is realized using the principle of parallax distance measurement.The depth localization error is less than 5.52%,which can meet the requirement of the small defect intelligent evaluation in precise casting.%针对射线实时成像检测中精密铸件微小缺陷自动定位的需要,提出一种基于深度学习特征匹配的铸件缺陷三维定位方法。模拟选择注意机制的中央-周边差算法,提出以视觉显著度为尺度,从射线图像复杂背景中检测出微小缺陷及其区域,以定义的区域中央点为待匹配点;然后,提出构造深度卷积神经网络自动提取微小缺陷区域的深度学习特征,通过深度学习特征矢量的相似度,

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

    Science.gov (United States)

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

    2015-09-01

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

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

    Science.gov (United States)

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

    2013-07-01

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

  8. Unattended vehicle detection for automatic traffic light control

    Science.gov (United States)

    Abdel Hady, Aya Salama; Moustafa, Mohamed

    2013-12-01

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

  9. Develop algorithms to improve detectability of defects in Sonic IR imaging NDE

    Science.gov (United States)

    Obeidat, Omar; Yu, Qiuye; Han, Xiaoyan

    2016-02-01

    Sonic Infrared (IR) technology is relative new in the NDE family. It is a fast, wide area imaging method. It combines ultrasound excitation and infrared imaging while the former to apply ultrasound energy thus induce friction heating in defects and the latter to capture the IR emission from the target. This technology can detect both surface and subsurface defects such as cracks and disbands/delaminations in various materials, metal/metal alloy or composites. However, certain defects may results in only very small IR signature be buried in noise or heating patterns. In such cases, to effectively extract the defect signals becomes critical in identifying the defects. In this paper, we will present algorithms which are developed to improve the detectability of defects in Sonic IR.

  10. Novel detection and process improvement for organic coating-film defects

    Science.gov (United States)

    Harumoto, Masahiko; Tanaka, Yuji; Hisai, Akihiro; Asai, Masaya; Ota, Hideo; Endo, Fumiaki; Takahashi, Kazuo

    2016-03-01

    Spin coating has been used as a photoresist application method for many years,[1,2] and it has continued to include applications such as the tri-layer with stacked photoresist, Si containing anti-reflected coating (Si-ARC), and Spin on Carbon (SOC). Last year we reported EUV defectivity improvement, but the causes of some defect types were not found.[3,4] In this study, the defects unique to the coated organic film were detected using an LS9300 by Hitachi High-Technologies, and some of these defects were able to be mitigated by optimizing the SOKUDO-DUO track system. Utilizing these systems in tandem, we have revealed a mechanism of EUV pattern defect reduction linked to novel detected film coating defects. During the conference, we will discuss expansion of this concept to other film coatings.

  11. 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.

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

    DEFF Research Database (Denmark)

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

    1995-01-01

    curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...

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

    Directory of Open Access Journals (Sweden)

    N. Khalili Moghadam

    2015-12-01

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

  14. Automatic detection of microaneurysms using microstructure and wavelet methods

    Indian Academy of Sciences (India)

    M Tamilarasi; K Duraiswamy

    2015-06-01

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

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

    Directory of Open Access Journals (Sweden)

    E. Campmany

    2009-03-01

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

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

    Science.gov (United States)

    Lamarque, P.

    2004-04-01

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

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    Vlad Turcu

    2012-09-01

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

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection. We implemented a state-of-the-art algorithm...... to investigate the performance of paroxysm detection. Using only a single scalp electroencephalogram channel from 20 patients with a total of 125 paroxysms >2 seconds, 97.2% of paroxysms could be detected with no false detections. This result leads us to recommend further investigations of tiny, one......-channel electroencephalogram systems in an ambulatory setting....

  20. Pipe wall damage detection by electromagnetic acoustic transducer generated guided waves in absence of defect signals.

    Science.gov (United States)

    Vasiljevic, Milos; Kundu, Tribikram; Grill, Wolfgang; Twerdowski, Evgeny

    2008-05-01

    Most investigators emphasize the importance of detecting the reflected signal from the defect to determine if the pipe wall has any damage and to predict the damage location. However, often the small signal from the defect is hidden behind the other arriving wave modes and signal noise. To overcome the difficulties associated with the identification of the small defect signal in the time history plots, in this paper the time history is analyzed well after the arrival of the first defect signal, and after different wave modes have propagated multiple times through the pipe. It is shown that the defective pipe can be clearly identified by analyzing these late arriving diffuse ultrasonic signals. Multiple reflections and scattering of the propagating wave modes by the defect and pipe ends do not hamper the defect detection capability; on the contrary, it apparently stabilizes the signal and makes it easier to distinguish the defective pipe from the defect-free pipe. This paper also highlights difficulties associated with the interpretation of the recorded time histories due to mode conversion by the defect. The design of electro-magnetic acoustic transducers used to generate and receive the guided waves in the pipe is briefly described in the paper.

  1. Evaluation of Respiratory Motion Effect on Defect Detection in Myocardial Perfusion SPECT: A Simulation Study.

    Science.gov (United States)

    Yang, Yu-Wen; Chen, Jyh-Cheng; He, Xin; Wang, Shyh-Jen; Tsui, Benjamin M W

    2009-06-01

    The objective of this study is to investigate the effects of respiratory motion (RM) on defect detection in Tc-99m sestamibi myocardial perfusion SPECT (MPS) using a phantom population that includes patient variability. Three RM patterns are included, namely breath-hold, slightly enhanced normal breathing, and deep breathing. For each RM pattern, six 4-D NCAT phantoms were generated, each with anatomical variations. Anterior, lateral and inferior myocardial defects with different sizes and contrasts were inserted. Noise-free SPECT projections were simulated using an analytical projector. Poisson noise was then added to generate noisy realizations. The projection data were reconstructed using the OS-EM algorithm with 1 and 4 subsets/iteration and at 1, 2, 3, 5, 7, and 10 iterations. Short-axis images centered at the centroid of the myocardial defect were extracted, and the channelized Hotelling observer (CHO) was applied for the detection of the defect. The CHO results show that the value of the area under the receiver operating characteristics (ROC) curve (AUC) is affected by the RM amplitude. For all the defect sizes and contrasts studied, the highest or optimal AUC values indicate maximum detectability decrease with the increase of the RM amplitude. With no respiration, the ranking of the optimal AUC value in decreasing order is anterior then lateral, and finally inferior defects. The AUC value of the lateral defect drops more severely as the RM amplitude increases compared to other defect locations. Furthermore, as the RM amplitude increases, the AUC values of the smaller defects drop more quickly than the larger ones. We demonstrated that RM affects defect detectability of MPS imaging. The results indicate that developments of optimal data acquisition methods and RM correction methods are needed to improve the defect detectability in MPS.

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

    Directory of Open Access Journals (Sweden)

    Gábor eStefanics

    2011-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Suhai Sándor

    2008-09-01

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

  4. Real-time curvature defect detection on outer surfaces using best-fit polynomial interpolation.

    Science.gov (United States)

    Golkar, Ehsan; Prabuwono, Anton Satria; Patel, Ahmed

    2012-11-02

    This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously.

  5. Defect Detection and Localization of Nonlinear System Based on Particle Filter with an Adaptive Parametric Model

    Directory of Open Access Journals (Sweden)

    Jingjing Wu

    2015-01-01

    Full Text Available A robust particle filter (PF and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.

  6. Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

    Institute of Scientific and Technical Information of China (English)

    WANG Yong; GUO Hui

    2014-01-01

    Support vector machines(SVM) received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

  7. Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

    Directory of Open Access Journals (Sweden)

    Zhu Dandan

    2015-09-01

    Full Text Available In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

  8. Application of an IR Thermographic Device for the Detection of a Simulated Defect in a Pipe

    Directory of Open Access Journals (Sweden)

    Young Han Kim

    2006-10-01

    Full Text Available An infrared (IR temperature sensor module developed for the detection ofdefects in a metal plate is modified for defect detection in a pipe. A module giving closesensor arrangement and maintaining a constant distance between sensor and measuredobject is developed and utilized in the present modification of the IR thermographic device.The defect detection performance is experimentally investigated, and the measuredtemperature is compared with the computed temperature distribution and with a previousexperimental result. The outcome of this experiment indicates that detection of a simulateddefect is readily obtainable, and the measured temperature distribution is better for defectdetection than with the previously utilized device. The comparison of standard deviations ofdifferent sensors clearly indicates an improvement in the location of defects in this study.Also, the measured temperature distribution is comparable to the one calculated using a heatconduction equation. The device developed for defect detection here is suitable forimplementation in chemical processes, where most vessels and piping systems arecylindrical in shape.

  9. Analytical analysis of adaptive defect detection in amplitude and phase structures using photorefractive four-wave mixing

    Science.gov (United States)

    Nehmetallah, George; Donoghue, John; Banerjee, Partha; Khoury, Jed; Yamamoto, Michiharu; Peyghambarian, Nasser

    2016-04-01

    In this work, brief theoretical modeling, analysis, and novel numerical verification of a photorefractive polymer based four wave mixing (FWM) setup for defect detection has been developed. The numerical simulation helps to validate our earlier experimental results to perform defect detection in periodic amplitude and phase objects using FWM. Specifically, we develop the theory behind the detection of isolated defects, and random defects in amplitude, and phase periodic patterns. In accordance with the developed theory, the results show that this technique successfully detects the slightest defects through band-pass intensity filtering and requires minimal additional post image processing contrast enhancement. This optical defect detection technique can be applied to the detection of production line defects, e.g., scratch enhancement, defect cluster enhancement, and periodic pattern dislocation enhancement. This technique is very useful in quality control systems, production line defect inspection, and computer vision.

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

    Institute of Scientific and Technical Information of China (English)

    SUI Haigang; LI Deren; GONG Jianya

    2003-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  12. 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...

  13. 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.

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

    Science.gov (United States)

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-06-25

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

  15. Efficient defect pixel cluster detection and correction for Bayer CFA image sequences

    Science.gov (United States)

    Tajbakhsh, Touraj

    2011-01-01

    Image sensor arrays may have defect pixels, either originating from manufacturing or being developed over the lifetime of the image sensor array. Continuous defect pixel detection and correction performing during camera runtime is desirable. On-the-fly detection and correction is challenging since edges and high-frequency image content might get identified as defect pixel regions and intact pixels become corrupted during defect pixel replacement. We propose a table-based detection and correction method which by and by fills the non-volatile table during normal camera operation. In this work we model defect pixels and pixel clusters to be stuck to fixed values or at least fixed to a narrow value range whereas the local neighborhood of these pixels indicate a normal behavior. The idea is to temporally observe the value ranges of small group of pixels (e.g. 4x4 pixel blocks) and to decide about their defective condition depending on their variability with respect to their neighbor pixels. Our method is computationally efficient, requires no frame buffer, requires modest memory, and therefore is appropriate to operate in line-buffer based image signal processing (ISP) systems. Our results indicate high reliability in terms of detection rates and robustness against high-frequency image content. As part of the defect pixel replacement system we also propose a simple and efficient defect pixel correction method based on the mean of medians operating on the Bayer CFA image domain.

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

    Directory of Open Access Journals (Sweden)

    Juan Ródenas

    2015-09-01

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

  17. Diagnostic Accuracy of Inverted and Unprocessed Digitized Periapical Radiographs for Detection of Peri-Implant Defects

    Directory of Open Access Journals (Sweden)

    Seyed Jalal Pourhashemi

    2016-04-01

    Full Text Available Objectives: This study aimed to compare the diagnostic accuracy of inverted and unprocessed digitized periapical radiographs for detection of peri-implant defects.Materials and Methods: A total of 30 osteotomy sites were prepared in three groups of control, study group 1 with 0.425 mm defects and study group 2 with 0.725 mm defects using the SIC and Astra Tech drill systems with 4.25mm and 4.85mm diameters. Small and large defects were randomly created in the coronal 8mm of 20 implant sites; implants (3.4mm diameter, 14.5mm length were then placed. Thirty periapical (PA radiographs were obtained using Digora imaging system (Soredex Corporation, Helsinki, Finland, size 2 photostimulable storage phosphor (PSP plate sensors (40.0mm×30.0mm and Scanora software. Unprocessed images were inverted using Scanora software by applying image inversion and a total of 60 images were obtained and randomly evaluated by four oral and maxillofacial radiologists. Data were analyzed using the t-test.Results: Significant differences were observed in absolute and complete sensitivity and specificity of the two imaging modalities for detection of small and large defects (P<0.05. Unprocessed digital images had a higher mean in terms of absolute sensitivity for detection of small defects, complete sensitivity for detection of large peri-implant defects and definite rule out of defects compared with inverted images.Conclusion: Unprocessed digital images have a higher diagnostic value for detection of small and large peri-implant defects and also for definite rule out of defects compared with inverted images.

  18. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    Directory of Open Access Journals (Sweden)

    Saiqa Aleem

    2015-06-01

    Full Text Available 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 from different perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction. Results showed most of the machine learning methods performed well on software bug datasets.

  19. 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.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Jianwen Zhao

    2014-04-01

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

  2. Model based defect detection for free stator of ultrasonic motor

    DEFF Research Database (Denmark)

    Amini, Rouzbeh; Mojallali, Hamed; Izadi-Zamanabadi, Roozbeh;

    2007-01-01

    In this paper, measurements of admittance magnitude and phase are used to identify the complex values of equivalent circuit model for free stator of an ultrasonic motor. The model is used to evaluate the changes in the admittance and relative changes in the values of equivalent circuit elements....... This method identifies the damages and categorizes them. The validity of the method is verified by using free stator measurements of defect free stators of a recently developed multilayer piezoelectric motor....

  3. Model Based Defect Detection in Multi-Dimensional Vector Spaces

    Science.gov (United States)

    Honda, Toshifumi; Obara, Kenji; Harada, Minoru; Igarashi, Hajime

    A highly sensitive inspection algorithm is proposed that extracts defects in multidimensional vector spaces from multiple images. The proposed algorithm projects subtraction vectors calculated from test and reference images to control the noise by reducing the dimensionality of vector spaces. The linear projection vectors are optimized using a physical defect model, and the noise distribution is calculated from the images. Because the noise distribution varies with the intensity or texture of the pixels, the target image is divided into small regions and the noise distribution of the subtraction images are calculated for each divided region. The bidirectional local perturbation pattern matching (BD-LPPM) which is an enhanced version of the LPPM, is proposed to increase the sensitivity when calculating the subtraction vectors, especially when the reference image contains more high-frequency components than the test image. The proposed algorithm is evaluated using defect samples for three different scanning electron microscopy images. The results reveal that the proposed algorithm increases the signal-to-noise ratio by a factor of 1.32 relative to that obtained using the Mahalanobis distance algorithm.

  4. Efficient Fruit Defect Detection and Glare removal Algorithm by anisotropic diffusion and 2D Gabor filter

    OpenAIRE

    Katyal, Vini; Srivastava, Deepesh

    2012-01-01

    This paper focuses on fruit defect detection and glare removal using morphological operations, Glare removal can be considered as an important preprocessing step as uneven lighting may introduce it in images, which hamper the results produced through segmentation by Gabor filters .The problem of glare in images is very pronounced sometimes due to the unusual reflectance from the camera sensor or stray light entering, this method counteracts this problem and makes the defect detection much mor...

  5. 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.

  6. Evaluation of Defects Detection of Loop System in the Pipe using Lock-in Infrared Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Kim, S. C.; Jung, H. C.; Choi, T. H.; Jung, H. I.; Kim, K. S. [Chosun University, Gwangju (Korea, Republic of)

    2014-08-15

    The pipes of nuclear power plant could be thinned by the corrosion and fatigue and the defect could lead to a big accident. For this reason, the effective non-destructive testing method is necessary. This perform research of angle rated defect detection conditions and nuclear power plant piping defect detection by Lock-In Infrared Thermography technique. Defects were processed according to change for wall-thinning length, Circumference orientation angle and wall-thinning depth. In the used equipment IR camera and two halogen lamps, whose full power capacitany is 1 kW, halogen lamps and target pipe's distance fixed 2m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length.

  7. Quantitative detection of defects based on Markov-PCA-BP algorithm using pulsed infrared thermography technology

    Science.gov (United States)

    Tang, Qingju; Dai, Jingmin; Liu, Junyan; Liu, Chunsheng; Liu, Yuanlin; Ren, Chunping

    2016-07-01

    Quantitative detection of debonding defects' diameter and depth in TBCs has been carried out using pulsed infrared thermography technology. By combining principal component analysis with neural network theory, the Markov-PCA-BP algorithm was proposed. The principle and realization process of the proposed algorithm was described. In the prediction model, the principal components which can reflect most characteristics of the thermal wave signal were set as the input, and the defect depth and diameter was set as the output. The experimental data from pulsed infrared thermography tests of TBCs with flat bottom hole defects was selected as the training and testing sample. Markov-PCA-BP predictive system was arrived, based on which both the defect depth and diameter were identified accurately, which proved the effectiveness of the proposed method for quantitative detection of debonding defects in TBCs.

  8. Real time fabric defect detection system on an embedded DSP platform

    Science.gov (United States)

    Raheja, Jagdish Lal; Ajay, Bandla; Chaudhary, Ankit

    2013-11-01

    In industrial fabric productions, automated real time systems are needed to find out the minor defects. It will save the cost by not transporting defected products and also would help in making compmay image of quality fabrics by sending out only undefected products. A real time fabric defect detection system (FDDS), implementd on an embedded DSP platform is presented here. Textural features of fabric image are extracted based on gray level co-occurrence matrix (GLCM). A sliding window technique is used for defect detection where window moves over the whole image computing a textural energy from the GLCM of the fabric image. The energy values are compared to a reference and the deviations beyond a threshold are reported as defects and also visually represented by a window. The implementation is carried out on a TI TMS320DM642 platform and programmed using code composer studio software. The real time output of this implementation was shown on a monitor.

  9. Detectability of Pore Defect in Wind Turbine Blade Composites Using Image Correlation Technique

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Il; Huh, Yong Hak; Lee, Gun Chang [Korea Research institute of Standard and Science, Daejeon (Korea, Republic of)

    2013-10-15

    Defects that occur during the manufacturing process or operation of a wind turbine blade have a great influence on its life and safety. Typically, defects such as delamination, pore, wrinkle and matrix crack are found in a blade. In this study, the detectability of the pores, a type of defect that frequently occur during manufacturing, was examined from the full field strain distribution determined with the image correlation technique. Pore defects were artificially introduced in four-ply laminated GFRP composites with 0 .deg/{+-}45 .deg fiber direction. The artificial pores were introduced in consideration of their size and location. Three different-sized pores with diameter of 1, 2 and 3 mm were located on the top and bottom surface and embedded. By applying static loads of 0-200 MPa, the strain distributions over the specimen with the pore defects were determined using image correlation technique. It was found the pores with diameter exceeding 2 mm can be detected in diameter.

  10. Defect Detection of Velvet Bathrobe Fabrics and Grading with Demerit Point Systems

    Directory of Open Access Journals (Sweden)

    Deniz Mutlu Ala

    2015-12-01

    Full Text Available Fabric defects that may occur at different stages of woven terry fabric production requires quality control and classification of fabrics as first or second grade before sending to customer. In this study, before shipping of two different terry fabric orders, defects were detected by inspection of fabric rolls on a lighted control board by experienced experts. Number of the defects and dimensions of the defects seen during the inspection were noted on quality control charts. Detected defects were defined and scored according to different demerit point systems. In this way, the fabric rolls were classified according to the demerit point systems before being shipped to garment enterprises. Disputes can be avoided with classification made by a demerit point system on which manufacturer and the customer have agreed.

  11. A method of gear defect intelligent detection based on transmission noise

    Science.gov (United States)

    Chen, Hong-fang; Zhao, Yun; Lin, Jia-chun; Guo, Mian

    2015-02-01

    A new approach was proposed by combing Ensemble Empirical Mode Decomposition (EEMD) algorithm and Back Propagation (BP) neural network for detection of gear through transmission noise analysis. Then feature values of the feature signals are calculated. The feature values which have a great difference for different defect types are chosen to build an eigenvector. BP neural network is used to train and learn on the eigenvector for recognition of gear defects intelligently. In this study, a comparative experiment has been performed among normal gears, cracked gears and eccentric gears with fifteen sets of different gears. Experimental results indicate that the proposed method can detect gear defect features carried by the transmission noise effectively.

  12. Cognitive high speed defect detection and classification in MWIR images of laser welding

    Science.gov (United States)

    Lapido, Yago L.; Rodriguez-Araújo, Jorge; García-Díaz, Antón; Castro, Gemma; Vidal, Félix; Romero, Pablo; Vergara, Germán.

    2015-07-01

    We present a novel approach for real-time defect detection and classification in laser welding processes based on the use of uncooled PbSe image sensors working in the MWIR range. The spatial evolution of the melt pool was recorded and analyzed during several welding procedures. A machine learning approach was developed to classify welding defects. Principal components analysis (PCA) is used for dimensionality reduction of the melt pool data. This enhances classification results and enables on-line classification rates close to 1 kHz with non-optimized code prototyped in Python. These results point to the feasibility of real-time defect detection.

  13. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis.

    Science.gov (United States)

    Sánchez, Clara I; Hornero, Roberto; López, María I; Aboy, Mateo; Poza, Jesús; Abásolo, Daniel

    2008-04-01

    We present an automatic image processing algorithm to detect hard exudates. Automatic detection of hard exudates from retinal images is an important problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Fisher's linear discriminant analysis and makes use of colour information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 58 retinal images with variable colour, brightness, and quality. Our proposed algorithm obtained a sensitivity of 88% with a mean number of 4.83+/-4.64 false positives per image using the lesion-based performance evaluation criterion, and achieved an image-based classification accuracy of 100% (sensitivity of 100% and specificity of 100%).

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Yonghong Luo

    2013-11-01

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

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    Bo Huang

    2015-10-01

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

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

    CERN Document Server

    Kodge, B G

    2011-01-01

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

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

    Zhang, Xuan; Zheng, Guoyan

    2008-01-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Science.gov (United States)

    Zhang, Mo; Jin, Weiqi; Li, Li

    2016-10-01

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

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

    Science.gov (United States)

    Matsunawa, Tetsuaki; Nojima, Shigeki; Kotani, Toshiya

    2016-03-01

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

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

    OpenAIRE

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

    2016-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

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

  6. Error Detection And Correction Systems For Optical Disk: Issues Of Media Defect Distribution, Defect Growth, Error Management, And Disk Longevity

    Science.gov (United States)

    Nugent, William R.

    1987-01-01

    We examine the principal systems of Error Detection and Correction (EDAC) which have been recently proposed as U.S. standards for optical disks and discuss the the two principal methodologies employed: Reed-Solomon Codes and Product Codes, and describe the variations in their operating characteristics and their overhead in disk space. We then present current knowledge of the nature of defect distributions on optical media including bit error rates, the incidence and extents of clustered errors and burst errors, and the controversial aspects of correlation between these forms of error. We show that if such forms are correlated then stronger EDAC systems are needed than if they are not. We discuss the nature of defect growth over time and its likely causes, and present the differing views on the growth of burst errors including nucleation and incubation effects which are not detectable in new media. We exhibit a mathematical model of a currently proposed end-of-life defect distribution for write once media and discuss its implications in EDAC selection. We show that standardization of an EDAC system unifies the data recording process and is permissive to data interchange, but that enhancements in EDAC computation during reading can achieve higher than normal EDAC performance, though sometimes at the expense of decoding time. Finally we examine vendor estimates of disk longevity and possible means of life extension where archival recording is desired.

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

    Science.gov (United States)

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

    2014-03-01

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

  8. 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.

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Marghany Maged

    2016-10-01

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

  11. Adaptive defect and pattern detection in amplitude and phase structures via photorefractive four-wave mixing.

    Science.gov (United States)

    Nehmetallah, George; Banerjee, Partha; Khoury, Jed

    2015-11-10

    This work comprises the theoretical and numerical validations of experimental work on pattern and defect detection of periodic amplitude and phase structures using four-wave mixing in photorefractive materials. The four-wave mixing optical processor uses intensity filtering in the Fourier domain. Specifically, the nonlinear transfer function describing four-wave mixing is modeled, and the theory for detection of amplitude and phase defects and dislocations are developed. Furthermore, numerical simulations are performed for these cases. The results show that this technique successfully detects the slightest defects clearly even with no prior enhancement. This technique should prove to be useful in quality control systems, production-line defect inspection, and e-beam lithography.

  12. Application of thermal wave imaging and phase shifting method for defect detection in Stainless steel

    Science.gov (United States)

    Shrestha, Ranjit; Park, Jeonghak; Kim, Wontae

    2016-05-01

    This paper presents an experimental arrangement for detection of artificial subsurface defects in a stainless steel sample by means of thermal wave imaging with lock-in thermography and consequently, the impact of excitation frequency on defect detectability. The experimental analysis was performed at several excitation frequencies to observe the sample beginning from 0.18 Hz all the way down to 0.01 Hz. The phase contrast between the defective and sound regions illustrates the qualitative and quantitative investigation of defects. The two, three, four and five-step phase shifting methods are investigated to obtain the information on defects. A contrast to noise ratio analysis was applied to each phase shifting method allowing the choice of the most appropriate one. Phase contrast with four-step phase shifting at an optimum frequency of 0.01 Hz provides excellent results. The inquiry with the effect of defect size and depth on phase contrast shows that phase contrast decreases with increase in defect depth and increases with the increase in defect size.

  13. New method of detection and classification of yield-impacting EUV mask defects

    Science.gov (United States)

    Graur, Ioana; Vengertsev, Dmitry; Raghunathan, Ananthan; Stobert, Ian; Rankin, Jed

    2015-10-01

    Extreme ultraviolet lithography (EUV) advances printability of small size features for both memory and logic semiconductor devices. It promises to bring relief to the semiconductor manufacturing industry, removing the need for multiple masks in rendering a single design layer on wafer. However, EUV also brings new challenges, one of which is of mask defectivity. For this purpose, much of the focus in recent years has been in finding ways to adequately detect, characterize, and reduce defects on both EUV blanks and patterned masks. In this paper we will present an efficient way to classify and disposition EUV mask defects through a new algorithm developed to classify defects located on EUV photomasks. By processing scanning electronmicroscopy images (SEM) of small regions of a photomask, we extract highdimensional local features Histograms of Oriented Gradients (HOG). Local features represent image contents compactly for detection or classification, without requiring image segmentation. Using these HOGs, a supervised classification method is applied which allows differentiating between nondefective and defective images. In the new approach we have developed a superior method of detection and classification of defects, using mask and supporting mask printed data from several metallization masks. We will demonstrate that use of the HOG method allows realtime identification of defects on EUV masks regardless of geometry or construct. The defects identified by this classifier are further divided into subclasses for mask defect disposition: foreign material, foreign material from previous step, and topological defects. The goal of disposition is to categorize on the images into subcategories and provide recommendation of prescriptive actions to avoid impact on the wafer yield.

  14. Further development of image processing algorithms to improve detectability of defects in Sonic IR NDE

    Science.gov (United States)

    Obeidat, Omar; Yu, Qiuye; Han, Xiaoyan

    2017-02-01

    Sonic Infrared imaging (SIR) technology is a relatively new NDE technique that has received significant acceptance in the NDE community. SIR NDE is a super-fast, wide range NDE method. The technology uses short pulses of ultrasonic excitation together with infrared imaging to detect defects in the structures under inspection. Defects become visible to the IR camera when the temperature in the crack vicinity increases due to various heating mechanisms in the specimen. Defect detection is highly affected by noise levels as well as mode patterns in the image. Mode patterns result from the superposition of sonic waves interfering within the specimen during the application of sound pulse. Mode patterns can be a serious concern, especially in composite structures. Mode patterns can either mimic real defects in the specimen, or alternatively, hide defects if they overlap. In last year's QNDE, we have presented algorithms to improve defects detectability in severe noise. In this paper, we will present our development of algorithms on defect extraction targeting specifically to mode patterns in SIR images.

  15. On-power detection of wall-thinned defects using lock-in infrared thermography

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Kwae Hwan; Kim, Ju Hyun [Department of Nuclear Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759 (Korea, Republic of); Na, Man Gyun, E-mail: magyna@chosun.ac.kr [Department of Nuclear Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759 (Korea, Republic of); Kim, Jin Weon [Department of Nuclear Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759 (Korea, Republic of); Kim, Kyeong Suk [Department of Mechanical Design Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759 (Korea, Republic of)

    2014-12-15

    Highlights: • Lock-in IR thermography was proposed to detect wall-thinned defects during normal NPP operation. • A mock-up loop was constructed that contained artificially generated defects. • The fluid inside the loop was maintained at a temperature similar to NPP operating conditions. • The boundary of the wall-thinned defective part was clear from acquired images. - Abstract: Recently, nuclear power plants (NPPs) have been using ultrasonic testing (UT) to inspect the pipes of secondary piping systems. However, UT is not suitable for measuring wall-thinned defects in small-diameter pipes and requires a long period of time to acquire analysis results. In addition, it is less reliable when inspecting small-diameter piping, and it is almost impossible to inspect defects during NPP normal operation. Therefore, UT is not reliable for detecting wall-thinned defects in the small-diameter pipes of NPPs during normal operation. In this study, we developed a lock-in infrared (IR) thermography technique to detect wall-thinned defects in the small diameter pipes of a NPP's secondary systems during normal operation. For experiments, a mock-up loop was constructed that contained artificially generated defects. The fluid inside the loop was maintained at a temperature similar to the operating conditions of a NPP. Based on the results of experiments where lock-in IR thermography was applied, it is expected to be possible to detect wall-thinned defects in piping during normal operation, shorten the maintenance time of NPPs, and improve the work efficiency of the inspector.

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

    Science.gov (United States)

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

    2016-03-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Razavi, A.

    1998-12-31

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

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

    Science.gov (United States)

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

    2013-12-01

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

  19. 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....

  20. 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.)

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

    Science.gov (United States)

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

    2013-04-01

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

  2. Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

    Directory of Open Access Journals (Sweden)

    Bin Chen

    2014-01-01

    Full Text Available Wheel-bearings easily acquire defects due to their high-speed operating conditions and constant metal-metal contact, so defect detection is of great importance for railroad safety. The conventional spectral kurtosis (SK technique provides an optimal bandwidth for envelope demodulation. However, this technique may cause false detections when processing real vibration signals for wheel-bearings, because of sparse interference impulses. In this paper, a novel defect detection method with entropy, time-spectral kurtosis (TSK and support vector machine (SVM is proposed. In this method, the possible outliers in the short time Fourier transform (STFT amplitude series are first estimated and preprocessed with information entropy. Then the method extends the SK technique to the time-domain, and extracts defective frequencies from reconstructed vibration signals by TSK filtering. Finally, the multi-class SVM was applied to classify bearing defects. The effectiveness of the proposed method is illustrated using real wheel-bearing vibration signals. Experimental results show that the proposed method provides a better performance in defect frequency detection and classification than the conventional SK-based envelope demodulation.

  3. Test system for defect detection in cementitious material with artificial neural network

    Directory of Open Access Journals (Sweden)

    Saowanee Saechai

    2013-04-01

    Full Text Available This paper introduces a newly developed test system for defect detection, classification of number of defects andidentification of defect materials in cement-based products. With the system, the pattern of ultrasonic waves for each case ofspecimen can be obtained from direct and indirect measurements. The machine learning algorithm called artificial neuralnetwork classifier with back-propagation model is employed for classification and verification of the wave patterns obtainedfrom different specimens. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover,the system is applied to identify the number and materials of defects inside the mortar. The methodology is explained and theclassification results are discussed. The effectiveness of the developed test system is evaluated. Comparison of the classification results between different input features with different number of training sets is demonstrated. The results show that thistechnique based on pattern recognition has a potential for practical inspection of concrete structures.

  4. Detection of critical congenital heart defects: Review of contributions from prenatal and newborn screening.

    Science.gov (United States)

    Olney, Richard S; Ailes, Elizabeth C; Sontag, Marci K

    2015-04-01

    In 2011, statewide newborn screening programs for critical congenital heart defects began in the United States, and subsequently screening has been implemented widely. In this review, we focus on data reports and collection efforts related to both prenatal diagnosis and newborn screening. Defect-specific, maternal, and geographic factors are associated with variations in prenatal detection, so newborn screening provides a population-wide safety net for early diagnosis. A new web-based repository is collecting information on newborn screening program policies, quality indicators related to screening programs, and specific case-level data on infants with these defects. Birth defects surveillance programs also collect data about critical congenital heart defects, particularly related to diagnostic timing, mortality, and services. Individuals from state programs, federal agencies, and national organizations will be interested in these data to further refine algorithms for screening in normal newborn nurseries, neonatal intensive care settings, and other special populations; and ultimately to evaluate the impact of screening on outcomes.

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

    Directory of Open Access Journals (Sweden)

    Khalil Mohamad

    2007-01-01

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

  6. 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.

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

    Science.gov (United States)

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

    2014-03-01

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

  8. Automatic 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.

  9. Reactive Impinging-Flow Technique for Polymer-Electrolyte-Fuel-Cell Electrode-Defect Detection

    Energy Technology Data Exchange (ETDEWEB)

    Zenyuk, Iryna V.; Englund, Nicholas; Bender, Guido; Weber, Adam Z.; Ulsh, Michael

    2016-11-15

    Reactive impinging flow (RIF) is a novel quality-control method for defect detection (i.e., reduction in Pt catalyst loading) in gas-diffusion electrodes (GDEs) on weblines. The technique uses infrared thermography to detect temperature of a nonflammable (<4% H2) reactive mixture of H2/O2 in N2 impinging and reacting on a Pt catalytic surface. In this paper, different GDE size defects (with catalyst-loading reductions of 25, 50, and 100%) are detected at various webline speeds (3.048 and 9.144 m min-1) and gas flowrates (32.5 or 50 standard L min-1). Furthermore, a model is developed and validated for the technique, and it is subsequently used to optimize operating conditions and explore the applicability of the technique to a range of defects. The model suggests that increased detection can be achieved by recting more of the impinging H2, which can be accomplished by placing blocking substrates on the top, bottom, or both of the GDE; placing a substrate on both results in a factor of four increase in the temperature differential, which is needed for smaller defect detection. Overall, the RIF technique is shown to be a promising route for in-line, high-speed, large-area detection of GDE defects on moving weblines.

  10. Defect Detection in Pipes using a Mobile Laser-Optics Technology and Digital Geometry

    Directory of Open Access Journals (Sweden)

    Tezerjani Abbasali Dehghan

    2015-01-01

    Full Text Available This paper presents a novel method for defect detection in pipes using a mobile laser-optics technology and conventional digital-geometry-based image processing techniques. The laser-optics consists of a laser that projects a line onto the pipe’s surface, and an omnidirectional camera. It can be mounted on a pipe crawling robot for conducting continuous inspection. The projected laser line will be seen as a half-oval in the image. When the laser line passes over defected points, the image moments on the pixel information would change. We propose a B-spline curve fitting on the digitally-convoluted image and a curvature estimation algorithm to detect the defects from the image. Defect sizes of 2 mm or larger can be detected using this method in pipes of up to 24 inch in diameter. The proposed sensor can detect 180-degree (i.e., upper half surface of the pipe. By turning the sensor 180 degrees, one will be able to detect the other half (i.e., lower half of the pipe’s surface. While, 360-degree laser rings are available commercially, but they did not provide the intensity needed for our experimentation. We also propose a fast boundary extraction algorithm for real time detection of defects, where a trace of consecutive images are used to track the image features. Tests were carried out on PVC and steel pipes.

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

    Science.gov (United States)

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

    2013-08-01

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

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

    Science.gov (United States)

    Lin, Hui; Long, JianFeng

    2011-10-01

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

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

    Science.gov (United States)

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

    2014-04-01

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

  14. 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.

  15. Comprehensive Detection and Analysis of Defects in Foundation Pile of Bridge

    Institute of Scientific and Technical Information of China (English)

    WANG Qi-ren; HE Ji-shan; YANG Tian-chun

    2003-01-01

    In the process of piling ,there are many various defects in foundation pile of bridge such as mud-bearing,sediment-bearing, isolation, honeycomb, broken piles, and so on, showing physical and mechanical features of low-density and low-intensity. In fact, by using the comprehensive detection of acoustic transmission method, the reflected wave method as well as drill coring sample method, and the rational utilization of engineering geological condition in field, the characteristics, size and location of common defects of foundation pile of bridge can be accurately detected and judged and the integrity of piles and the quality of concrete can be impersonally estimated.comprehensive detecting and analyzing methods on this kind of piles are introduced briefly. The physical characters of defects and basic features of detecting curves and their corresponding relation are emphasized, and causes are analyzed in in detail in this paper.

  16. On-loom, real-time, noncontact detection of fabric defects by ultrasonic imaging.

    Energy Technology Data Exchange (ETDEWEB)

    Chien, H. T.

    1998-09-08

    A noncontact, on-loom ultrasonic inspection technique was developed for real-time 100% defect inspection of fabrics. A prototype was built and tested successfully on loom. The system is compact, rugged, low cost, requires minimal maintenance, is not sensitive to fabric color and vibration, and can easily be adapted to current loom configurations. Moreover, it can detect defects in both the pick and warp directions. The system is capable of determining the size, location, and orientation of each defect. To further improve the system, air-coupled transducers with higher efficiency and sensitivity need to be developed. Advanced detection algorithms also need to be developed for better classification and categorization of defects in real-time.

  17. In-situ Monitoring and Defect Detection for Laser Metal Deposition by Using Infrared Thermography

    Science.gov (United States)

    Hassler, Ulf; Gruber, Daniel; Hentschel, Oliver; Sukowski, Frank; Grulich, Tobias; Seifert, Lars

    Aim of the presented approach is the early detection of defects (mainly material inhomogeneities like voids, delaminations, kissing bonds) occuring during the additive Laser Metal Deposition (LMD) process. Basis of the approach is the evaluation of the surface temperature gradient within the welding spot using a high speed thermographic sensor. Our contribution covers the following aspects: Estimation of the expected defect contrast by means of a simulation study Second point Experimental setup and performed experiments Achieved results on different welding parameters and mock-up defects together with the associated image processing chain First experiments showed that a set of process parameters can be monitored through the temperature signature of the welding spot. Also, the available defects have been detected down to a diameter of 0.5 mm. The presented work has been carried out within the research project 'ForNextGen' funded by the Bavarian Research Foundation and is part of the work package 6 (Non destructive testing).

  18. Defects detection in thin components using two-dimensional ultrasonic arrays

    Science.gov (United States)

    Velichko, A.; Wilcox, P. D.; Drinkwater, B. W.

    2013-01-01

    The use of 2D ultrasonic arrays provides great flexibility, as one array probe allows a given defect to be illuminated from a wide range of angles. However there are a number of challenges in the application of 2D arrays to detection and characterization of 3D defects. In the current paper the problem of finding the optimal array configuration for defects detection in thin sections is investigated. The efficient FE scattering model is used to simulate an ultrasonic array response for different 3D defects. The data provided by this model is then used to analyze the influence of different parameters on the array performance (signal to noise ratio, sensitivity, resolution). Finally, experimental results are shown that illustrate the imaging performance of optimal 2D array configuration.

  19. 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.

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

    Science.gov (United States)

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

    2017-08-01

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

  1. 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.

  2. Theoretical assessment of different ultrasonic configurations for defects detection in composite components

    DEFF Research Database (Denmark)

    Kappatos, Vassilios; Asfis, Georgios; Salonitis, Konstantinos

    2017-01-01

    physical models representative of laminated Carbon Fiber Reinforced Polymer (CFRP) composites, consisting of a variety of artificial delamination defect modes (different sizes and depth), were numerically tested. Different ultrasonic configurations on both the positioning and the firing of the probe...... and prevent a catastrophic failure by substituting or repairing them. The objective of this work is the theoretical assessment of different ultrasonic configurations that could maximize delamination defect detection in composite components. Modeling study was performed using simulation software, where...

  3. Defect detection in LiNbO3 crystals using cross Nicol optical system with heating function

    Science.gov (United States)

    Hoshino, Yasushi; Shimizu, Hajime; Arishima, Koichi; Kozawaguchi, Haruki

    2017-07-01

    LiNbO3 crystals are important for applications such as optical devices and surface acoustic wave filters. However, defects in such crystals can negatively affect device characteristics. Consequently, control of defects is important during device manufacturing. In this study, a new defect detection method using a cross Nicol optical system with a heating function and defect image enhancement is proposed. Experiments confirmed that defects detected by this method correspond to those imaged by X-ray topography. It was found that additional defects are formed by charge induced during heating.

  4. Utilization of Lamp Lock-in for Wall-Thinned Defects Detection Using IR Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Kwaehwan; Kim, Juhyun; Na, Mangyun; Kim, Jinweon; Jung, Hyunchul; Kim, Kyeongsuk [Chosun Univ., Gwangju (Korea, Republic of)

    2013-05-15

    In this study, lock-in techniques and output adjustment were applied to the heating device using the IR thermography in order to detect the wall-thinned defects in the pipe specimen. As a result of experiment, the location and size of the wall-thinned defects are detected well in case of using the lock-in technique than heating the pipe specimen continuously. By using the lock-in technique with Lab View, an inspector can control the heating device through the computer. Recently, the number of long-term aged nuclear power plants (NPPs) has increased. As a result, the problem of the NPP secondary system equipment also increased. For these reasons, the interest about NDT for checking the integrity of the major equipment is growing. The infrared (IR) thermography can detect the defects by analyzing the surface radiant energy from the object through real-time temperature-changing images. To detect the wall-thinned defects of the pipe inside precisely and quickly, it is most important to control the heating device. The IR thermography is expected to show a variety of applications in the field of NDT because it is much safer and faster than other techniques. Through this study, we have developed the inspection technique that can detect the defects by using the lock-in technique in the IR thermography for inspection of pipes in the NPPs during the overhaul.

  5. 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.

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

    Science.gov (United States)

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

    2017-07-01

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

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

    Science.gov (United States)

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

    2011-09-30

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

  8. 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.

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

    Science.gov (United States)

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

    2012-03-01

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

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Syaiful Anam

    2014-10-01

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

  12. An Automatic Prolongation Detection Approach in Continuous Speech With Robustness Against Speaking Rate Variations

    Science.gov (United States)

    Esmaili, Iman; Dabanloo, Nader Jafarnia; Vali, Mansour

    2017-01-01

    In recent years, many methods have been introduced for supporting the diagnosis of stuttering for automatic detection of prolongation in the speech of people who stutter. However, less attention has been paid to treatment processes in which clients learn to speak more slowly. The aim of this study was to develop a method to help speech-language pathologists (SLPs) during diagnosis and treatment sessions. To this end, speech signals were initially parameterized to perceptual linear predictive (PLP) features. To detect the prolonged segments, the similarities between successive frames of speech signals were calculated based on correlation similarity measures. The segments were labeled as prolongation when the duration of highly similar successive frames exceeded a threshold specified by the speaking rate. The proposed method was evaluated by UCLASS and self-recorded Persian speech databases. The results were also compared with three high-performance studies in automatic prolongation detection. The best accuracies of prolongation detection were 99 and 97.1% for UCLASS and Persian databases, respectively. The proposed method also indicated promising robustness against artificial variation of speaking rate from 70 to 130% of normal speaking rate. PMID:28487827

  13. 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.

  14. Fast online detection of body defect of glass containers

    Science.gov (United States)

    Wen, Zhangbin; Ge, Jia; Xia, Lijia; Luo, Yunhan; Chen, Zhe

    2011-11-01

    Online inspection of glass containers is important to guarantee the high quality production, safe use and effective recovery, so high precision and low cost online detection system has important practical value. The system introduced in this paper consists of LED linear array as the light source, a linear CCD as the detector, and a dual core processor with ARM and DSP as well. The self rotating stage for glass bottles and the digital image process technology enable this system to acquire the complete data and improve the traditional detection method. As a result, a detection speed over 100 bottles per minute, and a precision over 99.99% were achieved with the relatively simple structure and low cost.

  15. A numerical study on detecting defects in a plane-stressed body by system identification

    Energy Technology Data Exchange (ETDEWEB)

    Shin, S. [Dong-A Univ., Pusan (Korea, Republic of) Dept. of Civil Eng.; Moo Koh, H. [Department of Civil Engineering, Seoul National University, Seoul (Korea, Republic of)

    1999-06-01

    A parametric system identification algorithm is applied for detecting holes or cracks in an elastic plane-stressed body using measured static response at the boundaries. A linearly constrained nonlinear optimization problem is solved for optimal constitutive parameters by minimizing the error between the measured and computed displacements. Each finite element in the model is parameterized by decomposing its stiffness matrix into constitutive parameters and kernel matrices. Because locations and sizes of actual holes or cracks in a body are not the a priori knowledge, the finite element model for detecting such defects is simply set up for the defect-free state with the assumption of a linear elastic behavior. Defects in a plane-stressed body are predicted by the reduction in the constitutive parameters of each element from their baseline values without modifying the geometry and topology of the defined finite element model. The proposed defect-detection algorithm allows sparse measured data with respect to the number of degrees of freedom of the model and also provides statistical defect indices when considering noise in measurements. An adaptive parameter grouping scheme is applied to localize defects when limited measurements are provided. The proposed method is investigated through numerically simulated examples. (orig.) 9 refs.

  16. 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 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...

  17. 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.

  18. Spatial Spectroscopy Approach for Detection of Internal Defect of Component without Zero-Position Sensors

    Directory of Open Access Journals (Sweden)

    Qizhou Wu

    2016-01-01

    Full Text Available Conventional approach to detect the internal defect of a component needs sensors to mark the “zero” positions, which is time-consuming and lowers down the detecting efficiency. In this study, we proposed a novelty approach that uses spatial spectroscopy to detect internal defect of objects without zero-position sensors. Specifically, the spatial variation wave of distance between the detecting source and object surface is analyzed, from which a periodical cycle is determined with the correlative approaches. Additionally, a wavelet method is adopted to reduce the noise of the periodic distance signal. This approach is validated by the ultrasound detection of a component with round cross section and elliptical shape in axis. The experimental results demonstrate that this approach greatly saves the time spent on the judgment of a complete cycle and improves the detecting efficiency of internal defect in the component. The approach can be expanded to other physical methods for noninvasive detection of internal defect, such as optical spectroscopy or X-ray scanning, and it can be used for hybrid medium, such as biological tissues.

  19. Defect Detection in Composite Coatings by Computational Simulation Aided Thermography

    Science.gov (United States)

    Almeida, R. M.; Souza, M. P. V.; Rebello, J. M. A.

    2010-02-01

    Thermography is based on the measurement of superficial temperature distribution of an object inspected subjected to tension, normally thermal heat. This measurement is performed with a thermographic camera that detects the infrared radiation emitted by every object. In this work thermograph was simulated by COMSOL software for optimize experimental parameters in composite material coatings inspection.

  20. Automatic ultrasonic breast lesions detection using support vector machine based algorithm

    Science.gov (United States)

    Yeh, Chih-Kuang; Miao, Shan-Jung; Fan, Wei-Che; Chen, Yung-Sheng

    2007-03-01

    It is difficult to automatically detect tumors and extract lesion boundaries in ultrasound images due to the variance in shape, the interference from speckle noise, and the low contrast between objects and background. The enhancement of ultrasonic image becomes a significant task before performing lesion classification, which was usually done with manual delineation of the tumor boundaries in the previous works. In this study, a linear support vector machine (SVM) based algorithm is proposed for ultrasound breast image training and classification. Then a disk expansion algorithm is applied for automatically detecting lesions boundary. A set of sub-images including smooth and irregular boundaries in tumor objects and those in speckle-noised background are trained by the SVM algorithm to produce an optimal classification function. Based on this classification model, each pixel within an ultrasound image is classified into either object or background oriented pixel. This enhanced binary image can highlight the object and suppress the speckle noise; and it can be regarded as degraded paint character (DPC) image containing closure noise, which is well known in perceptual organization of psychology. An effective scheme of removing closure noise using iterative disk expansion method has been successfully demonstrated in our previous works. The boundary detection of ultrasonic breast lesions can be further equivalent to the removal of speckle noise. By applying the disk expansion method to the binary image, we can obtain a significant radius-based image where the radius for each pixel represents the corresponding disk covering the specific object information. Finally, a signal transmission process is used for searching the complete breast lesion region and thus the desired lesion boundary can be effectively and automatically determined. Our algorithm can be performed iteratively until all desired objects are detected. Simulations and clinical images were introduced to

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2008-12-01

    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 research herd of about 200 cows milked by 2 automatic milking systems during the 2006-2007 milking season included EC, ISCC, monthly laboratory-determined SCC, and observed cases of CM that were treated with antibiotics. Milk samples for ISCC and laboratory-determined SCC were taken sequentially at the end of a cow milking. Both samples were derived from a composite cow milking obtained from the bottom of the milk receiver. Different time windows were defined in which true-positive, false-negative, and false-positive alerts were determined. Quarters suspected of having CM were visually checked and, if CM was confirmed, sampled for bacteriological culturing and treated with an antibiotic treatment. These treated quarters were considered as gold-standard positives for comparing CM detection models. Alert thresholds were adjusted to achieve a sensitivity of 80% in 3 detection models: using ISCC alone, EC alone, or a combination of these. The success rate (also known as the positive predictive value) and the false alert rate (number of false-positive alerts per 1,000 cow milkings) were used to evaluate detection performance. Normalized ISCC estimates were highly correlated with normalized laboratory-determined SCC measurements (r = 0.82) for SCC measurements >200 x 10(3) cells/mL. Using EC alone as a detection tool resulted in a range of 6.9 to 11.0% for success rate, and a range of 4.7 to 7.8 for the false alert rate. Values for the ISCC model were better than the model using EC alone with 12.7 to 15.6% for the success rate and 2.9 to 3.7 for the false alert rate. Combining sensor information to detect CM, by using a fuzzy logic algorithm, produced a 2- to 3-fold increase

  3. 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.

  4. 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

  5. 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.

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

    Directory of Open Access Journals (Sweden)

    Duck Hee Lee

    2013-03-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Wang Yimin; An Jinwen

    2005-01-01

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

  8. 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.

  9. 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.

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

    Science.gov (United States)

    Sidor, Kamil; Szlachta, Anna

    2017-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Sidor Kamil

    2017-04-01

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

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

    Directory of Open Access Journals (Sweden)

    José Rouco

    2016-03-01

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

  13. [Automatic detection of exudates in retinal images based on threshold moving average models].

    Science.gov (United States)

    Wisaeng, K; Hiransakolwong, N; Pothiruk, E

    2015-01-01

    Since exudate diagnostic procedures require the attention of an expert ophthalmologist as well as regular monitoring of the disease, the workload of expert ophthalmologists will eventually exceed the current screening capabilities. Retinal imaging technology is a current practice screening capability providing a great potential solution. In this paper, a fast and robust automatic detection of exudates based on moving average histogram models of the fuzzy image was applied, and then the better histogram was derived. After segmentation of the exudate candidates, the true exudates were pruned based on Sobel edge detector and automatic Otsu's thresholding algorithm that resulted in the accurate location of the exudates in digital retinal images. To compare the performance of exudate detection methods we have constructed a large database of digital retinal images. The method was trained on a set of 200 retinal images, and tested on a completely independent set of 1220 retinal images. Results show that the exudate detection method performs overall best sensitivity, specificity, and accuracy of 90.42%, 94.60%, and 93.69%, respectively.

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

    Institute of Scientific and Technical Information of China (English)

    Shoutao Li; Xiaomao Li; Yandong Tang

    2007-01-01

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

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

    Science.gov (United States)

    Krauß, Thomas; Fischer, Peter

    2016-08-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

  17. Importance of defect detectability in Positron Emission Tomography imaging of abdominal lesions

    Directory of Open Access Journals (Sweden)

    Shozo Yamashita

    2015-07-01

    Full Text Available Objective(s: This study was designed to assess defect detectability in positron emission tomography (PET imaging of abdominal lesions. Methods: 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. Results: 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. Conclusion: 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.

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

    Science.gov (United States)

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

    2015-01-01

    Objective(s): This study was designed to assess defect detectability in positron emission tomography (PET) imaging of abdominal lesions. Methods: 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). Results: 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. Conclusion: 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. PMID:27408887

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

    Science.gov (United States)

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

    2016-01-01

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

  20. Reactive impinging-flow technique for polymer-electrolyte-fuel-cell electrode-defect detection

    Science.gov (United States)

    Zenyuk, Iryna V.; Englund, Nicholas; Bender, Guido; Weber, Adam Z.; Ulsh, Michael

    2016-11-01

    Reactive impinging flow (RIF) is a novel quality-control method for defect detection (i.e., reduction in Pt catalyst loading) in gas-diffusion electrodes (GDEs) on weblines. The technique uses infrared thermography to detect temperature of a nonflammable (catalyst-loading reductions of 25, 50, and 100%) are detected at various webline speeds (3.048 and 9.144 m min-1) and gas flowrates (32.5 or 50 standard L min-1). Furthermore, a model is developed and validated for the technique, and it is subsequently used to optimize operating conditions and explore the applicability of the technique to a range of defects. The model suggests that increased detection can be achieved by recting more of the impinging H2, which can be accomplished by placing blocking substrates on the top, bottom, or both of the GDE; placing a substrate on both results in a factor of four increase in the temperature differential, which is needed for smaller defect detection. Overall, the RIF technique is shown to be a promising route for in-line, high-speed, large-area detection of GDE defects on moving weblines.

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

    Directory of Open Access Journals (Sweden)

    A. L. Salih

    2016-06-01

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

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

    Science.gov (United States)

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

    2016-06-01

    The analysis of the impact crater size-frequency distribution (CSFD) is a well-established approach to the determination of the age of planetary surfaces. Classically, estimation of the CSFD is achieved by manual crater counting and size determination in spacecraft images, which, however, becomes very time-consuming for large surface areas and/or high image resolution. With increasing availability of high-resolution (nearly) global image mosaics of planetary surfaces, a variety of automated methods for the detection of craters based on image data and/or topographic data have been developed. In this contribution a template-based crater detection algorithm is used which analyses image data acquired under known illumination conditions. Its results are used to establish the CSFD for the examined area, which is then used to estimate the absolute model age of the surface. The detection threshold of the automatic crater detection algorithm is calibrated based on a region with available manually determined CSFD such that the age inferred from the manual crater counts corresponds to the age inferred from the automatic crater detection results. With this detection threshold, the automatic crater detection algorithm can be applied to a much larger surface region around the calibration area. The proposed age estimation method is demonstrated for a Kaguya Terrain Camera image mosaic of 7.4 m per pixel resolution of the floor region of the lunar crater Tsiolkovsky, which consists of dark and flat mare basalt and has an area of nearly 10,000 km2. The region used for calibration, for which manual crater counts are available, has an area of 100 km2. In order to obtain a spatially resolved age map, CSFDs and surface ages are computed for overlapping quadratic regions of about 4.4 x 4.4 km2 size offset by a step width of 74 m. Our constructed surface age map of the floor of Tsiolkovsky shows age values of typically 3.2-3.3 Ga, while for small regions lower (down to 2.9 Ga) and higher

  3. On-Power Detection of Wall-Thinned Defects Using IR Thermography in NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ju Hyun; Kim, Jae Hwan; Lee, Sim Won; Na, Man Gyun; Kim, Jin Weon; Jung, Hyun Chul; Kim, Kyeong Suk [Chosun University, Gwangju (Korea, Republic of)

    2012-05-15

    Recently, the number of nuclear power plants (NPPs) that are aging by long-term operations has increased. Accordingly, the number of operational interruptions has increased due to malfunctions of the NPPs secondary systems. These cases occur in the NPPs secondary systems with various structures by the fatigue, wall-thinned defects, corrosions and so on. Of these problems, the wall thinned defects occur in the pipes by the diffusion of the corrosion with the flow of the fluids, and the defects frequently take place in the carbon steel pipes of the lower Cr contents. Periodic inspections are required for systematic management of the wall-thinned defects. In particular, they are also needed during the normal operations of the NPPs. There are many NDT techniques to detect the wall-thinned defects such as the UT, PT, ECT, MT, etc. These NDT techniques include the infrared thermography. This technique may solve the existing constraints of NDT by detecting the defects through observations of the temperature differentials on the object surface

  4. Defects detection in typical positions of bend pipes using low-frequency ultrasonic guided wave

    Institute of Scientific and Technical Information of China (English)

    罗更生; 谭建平; 汪亮; 许焰

    2015-01-01

    In order to analyze the possibility of detecting defects in bend pipe using low-frequency ultrasonic guided wave, the propagation of T(0,1) mode and L(0,2) mode through straight-curved-straight pipe sections was studied. FE (finite element) models of bend pipe without defects and those with defects were introduced to analyze energy distribution, mode transition and defect detection of ultrasonic guided wave. FE simulation results were validated by experiments of four different bend pipes with circumferential defects in different positions. It is shown that most energy of T(0,1) mode or L(0,2) mode focuses on extrados of bend but little passes through intrados of bend, and T(0,1) mode or L(0,2) mode is converted to other possible non-axisymmetric modes when propagating through the bend and the defect after bend respectively. Furthermore, L(0,2) mode is more sensitive to circumferential notch than T(0,1) mode. The results of this work are beneficial for practical testing of pipes.

  5. Model-based defect detection on structured surfaces having optically unresolved features.

    Science.gov (United States)

    O'Connor, Daniel; Henning, Andrew J; Sherlock, Ben; Leach, Richard K; Coupland, Jeremy; Giusca, Claudiu L

    2015-10-20

    In this paper, we demonstrate, both numerically and experimentally, a method for the detection of defects on structured surfaces having optically unresolved features. The method makes use of synthetic reference data generated by an observational model that is able to simulate the response of the selected optical inspection system to the ideal structure, thereby providing an ideal measure of deviation from nominal geometry. The method addresses the high dynamic range challenge faced in highly parallel manufacturing by enabling the use of low resolution, wide field of view optical systems for defect detection on surfaces containing small features over large regions.

  6. Detecting defect in cast iron using high-T{sub C} SQUID

    Energy Technology Data Exchange (ETDEWEB)

    He, D.F.; Yoshizawa, M.; Oyama, Y.; Nakamura, M

    2004-10-01

    For eddy-current NDE, due to the big permeability of ferromagnetic material, low testing frequency is needed to detect defects in it. SQUID has advantages in low frequency eddy current NDE. But the large magnetic field produced by ferromagnetic material often exceeds the dynamic range of general SQUID system. We developed a mobile high-T{sub C} SQUID system, with which, the dc and low-frequency magnetic field could be compensated well. Using our mobile SQUID system, the magnetic field produced by the cast iron could be compensated well and the defect in it could be successfully detected.

  7. Detection of outer raceway bearing defects in small induction motors using stator current analysis

    Indian Academy of Sciences (India)

    İzzet Y Önel; K Burak Dalci; İbrahim Senol

    2005-12-01

    We investigate the application of induction motor stator current spectral analysis (MCSA) for detection of rolling element bearing damage from the outer raceway. In this work, MCSA and vibration analysis are applied to induction motor to detect outer raceway defects in faulty bearings. Data acquisition, recording,and fast fourier transform (FFT) algorithms are done by using the LabVIEW programming language. Experimental results verify the relationship between vibration analysis and MCSA, and identify the presence of outer raceway bearing defects in induction machines. This work also indicates that detecting fault frequencies by motor currents is more difficult than detecting them by vibration analysis. The use of intensive resolution FFT is recommended in MCSA for detecting faults easily. Reinstalling a faulty bearing can alter the characteristic frequencies and it is difficult to compare results from different bearings or even from the same bearing in different installations.

  8. Detection of Simulated Periodontal Bone Defects Using Digital Images. An in vitro Study

    Directory of Open Access Journals (Sweden)

    Rafael Scaf de Molon

    2014-08-01

    Materials and Methods: The samples comprised 24 hemi-mandibles from pigs, which were allocated into 3 groups; G1 (before acid application, G2 (after acid application and G3 (without bone defect and acid treatment. Periodontal bone defects were created with round burs between the second and third pre-molar. The radiographs were taken using the Visualix eHD sensor. The central ray was perpendicular to the sensor and to the hemi-mandible at a 40 cm focal-spot to sensor distance (settings 70 kVp, 10 mA and 15 impulses. After the defects were created in groups G1 and G2, they were treated with 100% perchloric acid for 48 hours. Images were zoomed to the level of 125% and interpreted by three examiners. Sensitivity and specificity were computed for the detection of periodontal bone defects with acid application and created using only round burs. The examiner's radiographic interpretation produced a diagnosis based on a five-point confidence scale. If the interpretation received the scores 1 or 2, it was concluded that no bone defect was present, whereas the scores 3, 4, or 5 were considered to reflect evidence of a bone defect. Results: There was no difference between groups G1 (Sen -95%CI=0.9167; Spec -95%CI=0.9167 and G2 (Sen -95%CI=0.8333; Spec -95%CI=0.9167. Conclusions: There is no difference in the detection of periodontal bone defects created using round burs and defects created using round burs followed by acid treatment. [Arch Clin Exp Surg 2014; 3(4.000: 220-225

  9. 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.

  10. 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

  11. Automatic detection of avalanches in seismic data using Hidden Markov Models

    Science.gov (United States)

    Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

    2017-04-01

    Seismic monitoring systems are well suited for the remote detection of mass movements, such as landslides, rockfalls and debris flows. For snow avalanches, this has been known since the 1970s and seismic monitoring could potentially provide valuable information for avalanche forecasting. We thus explored continuous seismic data from a string of vertical component geophones in an avalanche starting zone above Davos, Switzerland. The overall goal is to automatically detect avalanches with a Hidden Markov Model (HMM), a statistical pattern recognition tool widely used for speech recognition. A HMM uses a classifier to determine the likelihood that input objects belong to a finite number of classes. These classes are obtained by learning a multidimensional Gaussian mixture model representation of the overall observable feature space. This model is then used to derive the HMM parameters for avalanche waveforms using a single training sample to build the final classifier. We classified data from the winter seasons of 2010 and compared the results to several hundred avalanches manually identified in the seismic data. First results of a classification of a single day have shown, that the model is good in terms of probability of detection while having a relatively low false alarm rate. We further implemented a voting based classification approach to neglect events detected only by one sensor to further improve the model performance. For instance, on 22 March 2010, a day with particular high avalanche activity, 17 avalanches were positively identified by at least three sensors with no false alarms. These results show, that the automatic detection of avalanches in seismic data is feasible, bringing us one step closer to implementing seismic monitoring system in operational forecasting.

  12. 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.

  13. Recognition and defect detection of dot-matrix text via variation-model based learning

    Science.gov (United States)

    Ohyama, Wataru; Suzuki, Koushi; Wakabayashi, Tetsushi

    2017-03-01

    An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68 %.

  14. 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.

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

    Science.gov (United States)

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

    2013-11-01

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

  16. Automatic detection and classification of sleep stages by multichannel EEG signal modeling.

    Science.gov (United States)

    Zhovna, Inna; Shallom, Ilan D

    2008-01-01

    In this paper a novel method for automatic detection and classification of sleep stages using a multichannel electroencephalography (EEG) is presented. Understanding the sleep mechanism is vital for diagnosis and treatment of sleep disorders. The EEG is one of the most important tools of studying and diagnosing sleep disorders. EEG signals waveforms activity interpretation is performed by visual analysis (a very difficult procedure). This research aim is to ease the difficulties involved in the existing manual process of EEG interpretation by proposing an automatic sleep stage detection and classification system. The suggested method based on Multichannel Auto Regressive (MAR) model. The multichannel analysis approach incorporates the cross correlation information existing between different EEG signals. In the training phase, we used the vector quantization (VQ) algorithm, Linde-Buzo-Gray (LBG) and sleep stage definition, by estimation of probability mass functions (pmf) per every sleep stage using Generalized Log Likelihood Ratio (GLLR) distortion. The classification phase was performed using Kullback-Leibler (KL) divergence. The results of this research are promising with classification accuracy rate of 93.2%. The results encourage continuation of this research in the sleep field and in other biomedical signals applications.

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

    Science.gov (United States)

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

    2012-09-01

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

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

    Science.gov (United States)

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

    2014-07-01

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

  19. 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.

  20. Automatic detection of trustworthiness of the face: a visual mismatch negativity study.

    Science.gov (United States)

    Kovács-Bálint, Z; Stefanics, G; Trunk, A; Hernádi, I

    2014-03-01

    Recognizing intentions of strangers from facial cues is crucial in everyday social interactions. Recent studies demonstrated enhanced event-related potential (ERP) responses to untrustworthy compared to trustworthy faces. The aim of the present study was to investigate the electrophysiological correlates of automatic processing of trustworthiness cues in a visual oddball paradigm in two consecutive experimental blocks. In one block, frequent trustworthy (p = 0.9) and rare untrustworthy face stimuli (p = 0.1) were briefly presented on a computer screen with each stimulus consisting of four peripherally positioned faces. In the other block stimuli were presented with reversed probabilities enabling the comparison of ERPs evoked by physically identical deviant and standard stimuli. To avoid attentional effects participants engaged in a central detection task. Analyses of deviant minus standard difference waveforms revealed that deviant untrustworthy but not trustworthy faces elicited the visual mismatch negativity (vMMN) component. The present results indicate that adaptation occurred to repeated unattended trustworthy (but not untrustworthy) faces, i.e., an automatic expectation was elicited towards trustworthiness signals, which was violated by deviant untrustworthy faces. As an evolutionary adaptive mechanism, the observed fast detection of trustworthiness-related social facial cues may serve as the basis of conscious recognition of reliable partners.

  1. 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.

  2. Detection of subsurface defects in metal materials using infrared thermography; Image processing and finite element modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ranjit, Shrestha; Kim, Won Tae [Dept. of Mechanical Engineering, Kongju National University, Cheonan (Korea, Republic of)

    2014-04-15

    Infrared thermography is an emerging approach to non-contact, non-intrusive, and non-destructive inspection of various solid materials such as metals, composites, and semiconductors for industrial and research interests. In this study, data processing was applied to infrared thermography measurements to detect defects in metals that were widely used in industrial fields. When analyzing experimental data from infrared thermographic testing, raw images were often not appropriate. Thus, various data analysis methods were used at the pre-processing and processing levels in data processing programs for quantitative analysis of defect detection and characterization; these increased the infrared non-destructive testing capabilities since subtle defects signature became apparent. A 3D finite element simulation was performed to verify and analyze the data obtained from both the experiment and the image processing techniques.

  3. 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.

  4. The Detection of Defects in Optical Fibers Using a Hybrid Opto-electronic Correlator

    Institute of Scientific and Technical Information of China (English)

    LIU Yange; LIU Wei; ZHANG Yimo; ZHOU Ge

    2000-01-01

    A hybrid opto-electronic correlator for detecting defects in optical fibers is proposed. After the light from a He-Ne laser being expanded and filtered it is not collimated but directly passes a Fourier transform lens and illuminates a reference fiber and a test fiber at the same input plane. The Fourier transform spectrum of the two fibers is therefore obtained at the rear focal plane of the lens, where it is sampled via a CCD array connected with a computer through a frame grabber. The computer performs filter, inverse Fourier transform and setting threshold operation on classification. The system is an equivalent of joint transform correlator with a Fourier lens of long focal length. The experiment results for optical fibers having incoordinate defects are presented. The results indicate that the system can be used for fiber defect detection, and has the advantages of high identification, compact configuration, easy adjustment and flexible manipulation.

  5. 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.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Vaishnavi Ravindran

    2016-11-01

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

  8. Automatic near-real-time detection of CMEs in Mauna Loa K-Cor coronagraph images

    Science.gov (United States)

    Thompson, William T.; St. Cyr, Orville Chris; Burkepile, Joan; Posner, Arik

    2017-08-01

    A simple algorithm has been developed to detect the onset of coronal mass ejections (CMEs), together with an estimate of their speed, in near-real-time using images of the linearly polarized white-light solar corona taken by the K-Cor telescope at the Mauna Loa Solar Observatory (MLSO). The algorithm used is a variation on the Solar Eruptive Event Detection System (SEEDS) developed at George Mason University. The algorithm was tested against K-Cor data taken between 29 April 2014 and 20 February 2017, on days which the MLSO website marked as containing CMEs. This resulted in testing of 139 days worth of data containing 171 CMEs. The detection rate varied from close to 80% in 2014-2015 when solar activity was high, down to as low as 20-30% in 2017 when activity was low. The difference in effectiveness with solar cycle is attributed to the difference in relative prevalance of strong CMEs between active and quiet periods. There were also twelve false detections during this time period, leading to an average false detection rate of 8.6% on any given day. However, half of the false detections were clustered into two short periods of a few days each when special conditions prevailed to increase the false detection rate. The K-Cor data were also compared with major Solar Energetic Particle (SEP) storms during this time period. There were three SEP events detected either at Earth or at one of the two STEREO spacecraft where K-Cor was observing during the relevant time period. The K-Cor CME detection algorithm successfully generated alerts for two of these events, with lead times of 1-3 hours before the SEP onset at 1 AU. The third event was not detected by the automatic algorithm because of the unusually broad width of the CME in position angle.

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

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

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

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    Bohui Zhu

    2013-01-01

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

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Laixiang Wen

    2013-01-01

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

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

    Science.gov (United States)

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

    2014-11-01

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

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

    DEFF Research Database (Denmark)

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Richard Washington

    2008-11-01

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

  18. 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.

  19. A fast three-dimensional reconstruction method applied for the fabric defect detection

    Science.gov (United States)

    Song, Limei; Zhang, Chunbo; Xiong, Hui; Wei, Yiying; Chen, Huawei

    2010-11-01

    The fabric quality defect detection is very useful for improving the qualities of the products. It is also very important to increase the reputation and the economic benefits of a company. However, there are some shortcomings in the traditional manual detection methods, such as the low detection efficiency, the fatigue problem of the operator, and the detection inaccuracy, etc. The existing 2D image processing methods are difficult to solve the interference which is caused by non-defect case, just like the cloth folds, the flying thick silk floss, the noise from the background light and ambient light, etc. In order to solve those problem, the BCCSL (Binocular Camera Color Structure Light) method and SFMS (Shape from Multi Shading) method is proposed in this paper. The three-dimensional color coordinates of the fabric can be quickly and highly-precision obtained, thus to judge the defects shape and location. The BCCSL method and SFMS method can quickly obtain the three-dimensional coordinates' information of the fabric defects. The BCCSL method collects the 3D skeleton's information of a fabric image through the binocular video capture device and the color structured light projection device in real-time. And the details 3D coordinates of fabric outside strip structural are obtained through the proposed method SFMS. The interference information, such as the cloth fold, the flying thick silk floss, and the noise from the background light and ambient light can be excluded by using the three-dimensional defect identification. What is more, according to the characteristics of 3D structure of the defect, the fabric can be identified and classified. Further more, the possible problems from the production line can be summarized.

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

    OpenAIRE

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

    2012-01-01

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

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

    OpenAIRE

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

    2016-01-01

    We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum ...

  2. Hyperspectral Waveband Selection for Internal Defect Detection of Pickling Cucumbers and Whole Pickles

    Science.gov (United States)

    Hyperspectral imaging under transmittance mode has shown potential for detecting internal defect, however, the technique still cannot meet the on-line speed requirement because it needs to acquire and analyze a large amount of image data. This study was carried out to select important wavebands that...

  3. Hyperspectral imaging-based classification and wavebands selection for internal defect detection of pickling cucumbers

    Science.gov (United States)

    Hyperspectral imaging is useful for detecting internal defect of pickling cucumbers. The technique, however, is not yet suitable for high-speed online implementation due to the challenges for analyzing large-scale hyperspectral images. This research was aimed to select the optimal wavebands from the...

  4. On-Line Hyperspectral Transmittance Imaging for Internal Defect Detection of Pickling Cucumbers

    Science.gov (United States)

    Hyperspectral imaging technique under transmittance mode was investigated for detection of internal defect in pickling cucumbers such as carpel suture separation or hollow cucumbers caused by mechanical stress. A prototype of on-line hyperspectral transmittance imaging system was developed for real...

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

    Directory of Open Access Journals (Sweden)

    Andrius Gudiškis

    2015-07-01

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

  6. Method and apparatus for detecting flaws and defects in heat seals

    Science.gov (United States)

    Rai, Kula R. (Inventor); Lew, Thomas M. (Inventor); Sinclair, Robert B. (Inventor)

    1993-01-01

    Flaws and defects in heat seals formed between sheets of translucent film are identified by optically examining consecutive lateral sections of the seal along the seal length. Each lateral seal section is illuminated and an optical sensor array detects the intensity of light transmitted through the seal section for the purpose of detecting and locating edges in the heat seal. A line profile for each consecutive seal section is derived having an amplitude proportional to the change in light intensity across the seal section. Instances in the derived line profile where the amplitude is greater than a threshold level indicate the detection of a seal edge. The detected edges in each derived line profile are then compared to a preset profile edge standard to identify the existence of a flaw or defect.

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

    Science.gov (United States)

    Erdogan, Gizem; Ross, Arun

    2013-05-01

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

  8. Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme

    Directory of Open Access Journals (Sweden)

    Zhao Peng

    2016-01-01

    Full Text Available Wood grading and wood price are mainly connected with the wood defect and wood species. In this paper, a wood defect quantitative detection scheme and a wood species qualitative identification scheme are proposed simultaneously based on 3D laser scanning point cloud. First, an Artec 3D scanner is used to scan the wood surface to get the 3D point cloud. Each 3D point contains its X, Y, and Z coordinate and its RGB color information. After preprocessing, the Z coordinate value of current point is compared with the set threshold to judge whether it is a defect point (i.e., cavity, worm tunnel, and crack. Second, a deep preferred search algorithm is used to segment the retained defect points marked with different colors. The integration algorithm is used to calculate the surface area and volume of every defect. Finally, wood species identification is performed with the wood surface’s color information. The color moments of scanned points are used for classification, but the defect points are not used. Experiments indicate that our scheme can accurately measure the surface areas and volumes of cavity, worm tunnel, and crack on wood surface with measurement error less than 5% and it can also reach a wood species recognition accuracy of 95%.

  9. 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.

  10. On power detection of pipe wall-thinned defects using IR thermography in NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ju Hyun; Yoo, Kwae Hwan; Na, Man Gyun; Kim, Jin Weon; Kim, Kyeong Suk [Chosun University, Gwangju (Korea, Republic of)

    2014-04-15

    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.

  11. Defect detection of pipes using Lyapunov dimension of Duffing oscillator based on ultrasonic guided waves

    Science.gov (United States)

    Wu, Jing; Wang, Yu; Zhang, Weiwei; Nie, Zhenhua; Lin, Rong; Ma, Hongwei

    2017-01-01

    This study proposes a novel small defect detection approach for steel pipes using the Lyapunov dimension (D) of the Duffing chaotic system based on ultrasonic guided waves. In this paper, inspection model is constructed by inputting the measured guided wave signal into the Duffing equation as the external turbulent driving force term and then Dis calculated. The properties of the Duffing system's noise immunity are first demonstrated theoretically based on the Lyapunov exponents. By comparing Dof the Duffing inspection system between the conditions of the inputted pure noise and the guided wave signal, the amplitude of the periodic force (F), the important parameter of the Duffing inspection system, could be determined. The values of other parameters of the Duffing inspection system are subsequently determined according to the numerical investigation. Furthermore, a time-moving window function is constructed to scan along the measured signal to locate the defect. And the small defect echo signal polluted by the noise is illustrated to prove the availability of the proposed method. Both numerical and experimental results show that the proposed approach can be used to improve the sensitivity of small defect detection and locate the small defect in pipes.

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhongliang, Pan, E-mail: panz@scnu.edu.cn [Laboratory of Quantum Information Technology, School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou 510006 (China); Ling, Chen [Laboratory of Quantum Information Technology, School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou 510006 (China); Guangju, Chen [Department of Automation, University of Electronics Science and Technology of China, Chengdu 610054 (China)

    2013-07-15

    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.

  13. AUTOMATIC DETECTION OF SECONDARY CRATERS AND MAPPING OF PLANETARY SURFACE AGE BASED ON LUNAR ORBITAL IMAGES

    Directory of Open Access Journals (Sweden)

    A. L. Salih

    2017-07-01

    Full Text Available Ages of planetary surfaces are typically obtained by manually determining the impact crater size-frequency distribution (CSFD in spacecraft imagery, which is a very intricate and time-consuming procedure. In this work, an image-based crater detection algorithm that relies on a generative template matching technique is applied to establish the CSFD of the floor of the lunar farside crater Tsiolkovsky. The automatic detection threshold value is calibrated based on a 100 km² test area for which the CSFD has been determined by manual crater counting in a previous study. This allows for the construction of an age map of the complete crater floor. It is well known that the CSFD may be affected by secondary craters. Hence, our detection results are refined by applying a secondary candidate detection (SCD algorithm relying on Voronoi tessellation of the spatial crater distribution, which searches for clusters of craters. The detected clusters are assumed to result from the presence of secondary craters, which are then removed from the CSFD. We found it favourable to apply the SCD algorithm separately to each diameter bin of the CSFD histogram. In comparison with the original age map, the refined age map obtained after removal of secondary candidates has a more homogeneous appearance and does not exhibit regions of spuriously high age resulting from contamination by secondary craters.

  14. 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.

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

    Science.gov (United States)

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

    2010-04-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-05-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

  18. Automatic Detection of Secondary Craters and Mapping of Planetary Surface Age Based on Lunar Orbital Images

    Science.gov (United States)

    Salih, A. L.; Lompart, A.; Grumpe, A.; Wöhler, C.; Hiesinger, H.

    2017-07-01

    Ages of planetary surfaces are typically obtained by manually determining the impact crater size-frequency distribution (CSFD) in spacecraft imagery, which is a very intricate and time-consuming procedure. In this work, an image-based crater detection algorithm that relies on a generative template matching technique is applied to establish the CSFD of the floor of the lunar farside crater Tsiolkovsky. The automatic detection threshold value is calibrated based on a 100 km² test area for which the CSFD has been determined by manual crater counting in a previous study. This allows for the construction of an age map of the complete crater floor. It is well known that the CSFD may be affected by secondary craters. Hence, our detection results are refined by applying a secondary candidate detection (SCD) algorithm relying on Voronoi tessellation of the spatial crater distribution, which searches for clusters of craters. The detected clusters are assumed to result from the presence of secondary craters, which are then removed from the CSFD. We found it favourable to apply the SCD algorithm separately to each diameter bin of the CSFD histogram. In comparison with the original age map, the refined age map obtained after removal of secondary candidates has a more homogeneous appearance and does not exhibit regions of spuriously high age resulting from contamination by secondary craters.

  19. 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

  20. Scanning laser polarimetry and optical coherence tomography for detection of retinal nerve fiber layer defects.

    Science.gov (United States)

    Oh, Jong-Hyun; Kim, Yong Yeon

    2009-09-01

    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. 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. 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. 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.

  1. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction

    NARCIS (Netherlands)

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

    2010-01-01

    The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM,

  2. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.

    Science.gov (United States)

    Yousefi, Siamak; Goldbaum, Michael H; Balasubramanian, Madhusudhanan; Medeiros, Felipe A; Zangwill, Linda M; Liebmann, Jeffrey M; Girkin, Christopher A; Weinreb, Robert N; Bowd, Christopher

    2014-07-01

    A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

  3. Analysis of polarimetric terahertz imaging for non-destructive detection of subsurface defects in wind turbine blades

    Science.gov (United States)

    Martin, Robert Warren

    During the manufacture of wind turbine blades, internal defects can form which negatively affect their structural integrity and can lead to premature failure. These defects are often not detected before the final installation of the blades onto wind turbines in the field. The purpose of this research was to investigate the advantages of using fully-polarimetric inverse synthetic aperture radar (ISAR) terahertz imaging techniques for scanning the interior structure of the wind turbine blades in order to detect and identify any defects in the blade's internal structure before the blade leaves the manufacturer. Additionally, the research has investigated the use of the Euler parameter polarimetric transformation in improving defect detection, and increasing understanding of the scattering properties of such defects. Use of an image compositing algorithm and of the Euler parameters was found to enhance defect detection.

  4. 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.

  5. 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.

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

    Directory of Open Access Journals (Sweden)

    Greet Baldewijns

    2016-02-01

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

  7. 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)

  8. 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

    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...

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Jie Su

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-11-01

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

  12. 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.

  13. Automatic segmentation and centroid detection of skin sensors for lung interventions

    Science.gov (United States)

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

    2012-02-01

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

  14. On automatic bioacoustic detection of pests: the cases of Rhynchophorus ferrugineus and Sitophilus oryzae.

    Science.gov (United States)

    Potamitis, Ilyas; Ganchev, Todor; Kontodimas, Dimitris

    2009-08-01

    The present work reports research efforts toward development and evaluation of a unified framework for automatic bioacoustic recognition of specific insect pests. Our approach is based on capturing and automatically recognizing the acoustic emission resulting from typical behaviors, e.g., locomotion and feeding, of the target pests. After acquisition the signals are amplified, filtered, parameterized, and classified by advanced machine learning methods on a portable computer. Specifically, we investigate an advanced signal parameterization scheme that relies on variable size signal segmentation. The feature vector computed for each segment of the signal is composed of the dominant harmonic, which carry information about the periodicity of the signal, and the cepstral coefficients, which carry information about the relative distribution of energy among the different spectral sub-bands. This parameterization offers a reliable representation of both the acoustic emissions of the pests of interest and the interferences from the environment. We illustrate the practical significance of our methodology on two specific cases: 1) a devastating pest for palm plantations, namely, Rhynchophorus ferrugineus Olivier and 2) a pest that attacks warehouse stored rice (Oryza sativa L.), the rice weevil, Sitophilus oryzae (L.) (both Coleoptera: Curculionidae, Dryophorinae). These pests are known in many countries around the world and contribute for significant economical loss. The proposed approach led to detection results in real field trials, reaching 99.1% on real-field recordings of R. ferrugineus and 100% for S. oryzae.

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

    Science.gov (United States)

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

    2014-11-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

  17. 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 ...

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

    Science.gov (United States)

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

    2006-01-01

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

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

    CERN Document Server

    Boujelben, Atef; Mnif, Jameleddine; Abid, Mohamed

    2011-01-01

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

  20. Automatic detection of zebra crossings from mobile LiDAR data

    Science.gov (United States)

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

    2015-07-01

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