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detection method</title> <meta name="description" content="Search results for: edge detection method"> <meta name="keywords" content="edge detection method"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="edge detection method"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 21641</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: edge detection method</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21641</span> Subjective Evaluation of Mathematical Morphology Edge Detection on Computed Tomography (CT) Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emhimed%20Saffor">Emhimed Saffor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection "> edge detection </a> </p> <a href="https://publications.waset.org/abstracts/44926/subjective-evaluation-of-mathematical-morphology-edge-detection-on-computed-tomography-ct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44926.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21640</span> Multiscale Edge Detection Based on Nonsubsampled Contourlet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enqing%20Chen">Enqing Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbo%20Wang"> Jianbo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=NSCT" title=" NSCT"> NSCT</a>, <a href="https://publications.waset.org/abstracts/search?q=shift%20invariant" title=" shift invariant"> shift invariant</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20maxima" title=" modulus maxima"> modulus maxima</a> </p> <a href="https://publications.waset.org/abstracts/9528/multiscale-edge-detection-based-on-nonsubsampled-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9528.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21639</span> Concentric Circle Detection based on Edge Pre-Classification and Extended RANSAC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhongjie%20Yu">Zhongjie Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hancheng%20Yu"> Hancheng Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concentric%20circle%20detection" title="concentric circle detection">concentric circle detection</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient" title=" gradient"> gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=contour" title=" contour"> contour</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20pre-classification" title=" edge pre-classification"> edge pre-classification</a>, <a href="https://publications.waset.org/abstracts/search?q=RANSAC" title=" RANSAC"> RANSAC</a> </p> <a href="https://publications.waset.org/abstracts/144332/concentric-circle-detection-based-on-edge-pre-classification-and-extended-ransac" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21638</span> Comparative Analysis of Edge Detection Techniques for Extracting Characters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rana%20Gill">Rana Gill</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandandeep%20Kaur"> Chandandeep Kaur </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=text" title=" text"> text</a>, <a href="https://publications.waset.org/abstracts/search?q=extracting%20characters" title=" extracting characters"> extracting characters</a> </p> <a href="https://publications.waset.org/abstracts/9054/comparative-analysis-of-edge-detection-techniques-for-extracting-characters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9054.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21637</span> Implementation of Edge Detection Based on Autofluorescence Endoscopic Image of Field Programmable Gate Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao%20Cheng">Hao Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiwu%20Wang"> Zhiwu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guozheng%20Yan"> Guozheng Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Jiang"> Pingping Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shijia%20Qin"> Shijia Qin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuai%20Kuang"> Shuai Kuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AFI" title="AFI">AFI</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20threshold" title=" adaptive threshold"> adaptive threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20processing" title=" morphological processing"> morphological processing</a>, <a href="https://publications.waset.org/abstracts/search?q=OV6930" title=" OV6930"> OV6930</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a> </p> <a href="https://publications.waset.org/abstracts/102685/implementation-of-edge-detection-based-on-autofluorescence-endoscopic-image-of-field-programmable-gate-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102685.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21636</span> Detecting the Edge of Multiple Images in Parallel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prakash%20K.%20Aithal">Prakash K. Aithal</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20Dinesh%20Acharya"> U. Dinesh Acharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Gopakumar"> Rajesh Gopakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel .The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. The proposed method achieves pixel level parallelism as well as image level parallelism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multicore" title=" multicore"> multicore</a>, <a href="https://publications.waset.org/abstracts/search?q=gpu" title=" gpu"> gpu</a>, <a href="https://publications.waset.org/abstracts/search?q=opencl" title=" opencl"> opencl</a>, <a href="https://publications.waset.org/abstracts/search?q=mpi" title=" mpi"> mpi</a> </p> <a href="https://publications.waset.org/abstracts/30818/detecting-the-edge-of-multiple-images-in-parallel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30818.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21635</span> Edge Detection Using Multi-Agent System: Evaluation on Synthetic and Medical MR Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Nachour">A. Nachour</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Ouzizi"> L. Ouzizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Aoura"> Y. Aoura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20MRImages" title=" medical MRImages"> medical MRImages</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20systems" title=" multi-agent systems"> multi-agent systems</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20field%20convolution" title=" vector field convolution"> vector field convolution</a> </p> <a href="https://publications.waset.org/abstracts/50615/edge-detection-using-multi-agent-system-evaluation-on-synthetic-and-medical-mr-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21634</span> Lane Detection Using Labeling Based RANSAC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeongyu%20Choi">Yeongyu Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20H.%20Park"> Ju H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Youl%20Jung"> Ho-Youl Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Canny%20edge%20detection" title="Canny edge detection">Canny edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20algorithm" title=" k-means algorithm"> k-means algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=RANSAC" title=" RANSAC"> RANSAC</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20perspective%20mapping" title=" inverse perspective mapping"> inverse perspective mapping</a> </p> <a href="https://publications.waset.org/abstracts/92894/lane-detection-using-labeling-based-ransac-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21633</span> Optimized Road Lane Detection Through a Combined Canny Edge Detection, Hough Transform, and Scaleable Region Masking Toward Autonomous Driving</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samane%20Sharifi%20Monfared">Samane Sharifi Monfared</a>, <a href="https://publications.waset.org/abstracts/search?q=Lavdie%20Rada"> Lavdie Rada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, autonomous vehicles are developing rapidly toward facilitating human car driving. One of the main issues is road lane detection for a suitable guidance direction and car accident prevention. This paper aims to improve and optimize road line detection based on a combination of camera calibration, the Hough transform, and Canny edge detection. The video processing is implemented using the Open CV library with the novelty of having a scale able region masking. The aim of the study is to introduce automatic road lane detection techniques with the user’s minimum manual intervention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hough%20transform" title="hough transform">hough transform</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20edge%20detection" title=" canny edge detection"> canny edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation" title=" optimisation"> optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=scaleable%20masking" title=" scaleable masking"> scaleable masking</a>, <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title=" camera calibration"> camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=improving%20the%20quality%20of%20image" title=" improving the quality of image"> improving the quality of image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20processing" title=" video processing"> video processing</a> </p> <a href="https://publications.waset.org/abstracts/156139/optimized-road-lane-detection-through-a-combined-canny-edge-detection-hough-transform-and-scaleable-region-masking-toward-autonomous-driving" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21632</span> Edge Detection and Morphological Image for Estimating Gestational Age Based on Fetus Length Automatically</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retno%20Supriyanti">Retno Supriyanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Chuzaeri"> Ahmad Chuzaeri</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogi%20Ramadhani"> Yogi Ramadhani</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Haris%20Budi%20Widodo"> A. Haris Budi Widodo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of ultrasonography in the medical world has been very popular including the diagnosis of pregnancy. In determining pregnancy, ultrasonography has many roles, such as to check the position of the fetus, abnormal pregnancy, fetal age and others. Unfortunately, all these things still need to analyze the role of the obstetrician in the sense of image raised by ultrasonography. One of the most striking is the determination of gestational age. Usually, it is done by measuring the length of the fetus manually by obstetricians. In this study, we developed a computer-aided diagnosis for the determination of gestational age by measuring the length of the fetus automatically using edge detection method and image morphology. Results showed that the system is sufficiently accurate in determining the gestational age based image processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=gestational%20age" title=" gestational age"> gestational age</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20diameter%20of%20uterus" title=" and diameter of uterus"> and diameter of uterus</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20of%20fetus" title=" length of fetus"> length of fetus</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection%20method" title=" edge detection method"> edge detection method</a>, <a href="https://publications.waset.org/abstracts/search?q=morphology%20image" title=" morphology image"> morphology image</a> </p> <a href="https://publications.waset.org/abstracts/46484/edge-detection-and-morphological-image-for-estimating-gestational-age-based-on-fetus-length-automatically" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21631</span> A Gradient Orientation Based Efficient Linear Interpolation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Khan">S. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khan"> A. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20R.%20Soomrani"> Abdul R. Soomrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Raja%20F.%20Zafar"> Raja F. Zafar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Waqas"> A. Waqas</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Akbar"> G. Akbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20orientation" title=" gradient orientation"> gradient orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20upscaling" title=" image upscaling"> image upscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20interpolation" title=" linear interpolation"> linear interpolation</a>, <a href="https://publications.waset.org/abstracts/search?q=slope%20tracing" title=" slope tracing"> slope tracing</a> </p> <a href="https://publications.waset.org/abstracts/85765/a-gradient-orientation-based-efficient-linear-interpolation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85765.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21630</span> High Level Synthesis of Canny Edge Detection Algorithm on Zynq Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanaa%20M.%20Abdelgawad">Hanaa M. Abdelgawad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mona%20Safar"> Mona Safar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20M.%20Wahba"> Ayman M. Wahba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real-time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Therefore, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Canny edge detection is one of the common blocks in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20level%20synthesis" title="high level synthesis">high level synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20edge%20detection" title=" canny edge detection"> canny edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%20accelerators" title=" hardware accelerators"> hardware accelerators</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/21304/high-level-synthesis-of-canny-edge-detection-algorithm-on-zynq-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21304.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21629</span> Defect Detection for Nanofibrous Images with Deep Learning-Based Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaokai%20Liu">Gaokai Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomaterials" title=" nanomaterials"> nanomaterials</a> </p> <a href="https://publications.waset.org/abstracts/133093/defect-detection-for-nanofibrous-images-with-deep-learning-based-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133093.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21628</span> New Efficient Method for Coding Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walaa%20M.Abd-Elhafiez">Walaa M.Abd-Elhafiez</a>, <a href="https://publications.waset.org/abstracts/search?q=Wajeb%20Gharibi"> Wajeb Gharibi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=q-coder" title=" q-coder"> q-coder</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=edge-detection" title=" edge-detection"> edge-detection</a> </p> <a href="https://publications.waset.org/abstracts/2342/new-efficient-method-for-coding-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2342.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21627</span> Refined Edge Detection Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Elharrouss">Omar Elharrouss</a>, <a href="https://publications.waset.org/abstracts/search?q=Youssef%20Hmamouche"> Youssef Hmamouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Assia%20Kamal%20Idrissi"> Assia Kamal Idrissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Btissam%20El%20Khamlichi"> Btissam El Khamlichi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20El%20Fallah-Seghrouchni"> Amal El Fallah-Seghrouchni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=scale-representation" title=" scale-representation"> scale-representation</a>, <a href="https://publications.waset.org/abstracts/search?q=backbone" title=" backbone"> backbone</a> </p> <a href="https://publications.waset.org/abstracts/150865/refined-edge-detection-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21626</span> Edge Detection in Low Contrast Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Koushlendra%20Kumar%20Singh">Koushlendra Kumar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Manish%20Kumar%20Bajpai"> Manish Kumar Bajpai</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20K.%20Pandey"> Rajesh K. Pandey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20contrast%20image" title="low contrast image">low contrast image</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order%20differentiator" title="fractional order differentiator">fractional order differentiator</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplacian%20of%20Gaussian%20%28LoG%29%20method" title="Laplacian of Gaussian (LoG) method">Laplacian of Gaussian (LoG) method</a>, <a href="https://publications.waset.org/abstracts/search?q=chebyshev%20polynomial" title=" chebyshev polynomial"> chebyshev polynomial</a> </p> <a href="https://publications.waset.org/abstracts/21264/edge-detection-in-low-contrast-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">636</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21625</span> The Need for Multi-Edge Strategies and Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hugh%20Taylor">Hugh Taylor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Industry analysts project that edge computing will be generating tens of billions in revenue in coming years. It’s not clear, however, if this will actually happen, and who, if anyone, will make it happen. Edge computing is seen as a critical success factor in industries ranging from telecom, enterprise IT and co-location. However, will any of these industries actually step up to make edge computing into a viable technology business? This paper looks at why the edge seems to be in a chasm, on the edge of realization, so to speak, but failing to coalesce into a coherent technology category like the cloud—and how the segment’s divergent industry players can come together to build a viable business at the edge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20computing" title="edge computing">edge computing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-edge%20strategies" title=" multi-edge strategies"> multi-edge strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20data%20centers" title=" edge data centers"> edge data centers</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20cloud" title=" edge cloud"> edge cloud</a> </p> <a href="https://publications.waset.org/abstracts/154144/the-need-for-multi-edge-strategies-and-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21624</span> Review on Quaternion Gradient Operator with Marginal and Vector Approaches for Colour Edge Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Ben%20Youssef">Nadia Ben Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Bouzid"> Aicha Bouzid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient" title="gradient">gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=quaternion" title=" quaternion"> quaternion</a> </p> <a href="https://publications.waset.org/abstracts/141138/review-on-quaternion-gradient-operator-with-marginal-and-vector-approaches-for-colour-edge-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141138.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">234</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21623</span> mKDNAD: A Network Flow Anomaly Detection Method Based On Multi-teacher Knowledge Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yang">Yang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Liu"> Dan Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20flow%20anomaly%20detection%20%28NAD%29" title="network flow anomaly detection (NAD)">network flow anomaly detection (NAD)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-teacher%20knowledge%20distillation" title=" multi-teacher knowledge distillation"> multi-teacher knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/156811/mkdnad-a-network-flow-anomaly-detection-method-based-on-multi-teacher-knowledge-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156811.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21622</span> Distributed Framework for Pothole Detection and Monitoring Using Federated Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ezil%20Sam%20Leni">Ezil Sam Leni</a>, <a href="https://publications.waset.org/abstracts/search?q=Shalen%20S."> Shalen S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=federated%20Learning" title="federated Learning">federated Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pothole%20detection" title=" pothole detection"> pothole detection</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20framework" title=" distributed framework"> distributed framework</a>, <a href="https://publications.waset.org/abstracts/search?q=federated%20averaging" title=" federated averaging"> federated averaging</a> </p> <a href="https://publications.waset.org/abstracts/176254/distributed-framework-for-pothole-detection-and-monitoring-using-federated-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176254.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21621</span> Advancing in Cricket Analytics: Novel Approaches for Pitch and Ball Detection Employing OpenCV and YOLOV8</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratham%20Madnur">Pratham Madnur</a>, <a href="https://publications.waset.org/abstracts/search?q=Prathamkumar%20Shetty"> Prathamkumar Shetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneha%20Varur"> Sneha Varur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gouri%20Parashetti"> Gouri Parashetti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title="OpenCV">OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title=" YOLOv8"> YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=cricket" title=" cricket"> cricket</a>, <a href="https://publications.waset.org/abstracts/search?q=custom%20dataset" title=" custom dataset"> custom dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=sports" title=" sports"> sports</a> </p> <a href="https://publications.waset.org/abstracts/182020/advancing-in-cricket-analytics-novel-approaches-for-pitch-and-ball-detection-employing-opencv-and-yolov8" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21620</span> Improvement of Brain Tumors Detection Using Markers and Boundaries Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousif%20Mohamed%20Y.%20Abdallah">Yousif Mohamed Y. Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mommen%20A.%20Alkhir"> Mommen A. Alkhir</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20S.%20Algaddal"> Amel S. Algaddal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=improvement" title="improvement">improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a>, <a href="https://publications.waset.org/abstracts/search?q=matlab" title=" matlab"> matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=markers" title=" markers"> markers</a>, <a href="https://publications.waset.org/abstracts/search?q=boundaries" title=" boundaries"> boundaries</a> </p> <a href="https://publications.waset.org/abstracts/31036/improvement-of-brain-tumors-detection-using-markers-and-boundaries-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31036.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21619</span> Sea-Land Segmentation Method Based on the Transformer with Enhanced Edge Supervision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lianzhong%20Zhang">Lianzhong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Huang"> Chao Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/148759/sea-land-segmentation-method-based-on-the-transformer-with-enhanced-edge-supervision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">181</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21618</span> Investigating the Viability of Ultra-Low Parameter Count Networks for Real-Time Football Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tim%20Farrelly">Tim Farrelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision%20applications" title=" machine vision applications"> machine vision applications</a>, <a href="https://publications.waset.org/abstracts/search?q=sport" title=" sport"> sport</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20design" title=" network design"> network design</a> </p> <a href="https://publications.waset.org/abstracts/145298/investigating-the-viability-of-ultra-low-parameter-count-networks-for-real-time-football-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145298.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21617</span> Image Processing Approach for Detection of Three-Dimensional Tree-Rings from X-Ray Computed Tomography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Martinez-Garcia">Jorge Martinez-Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Ingrid%20Stelzner"> Ingrid Stelzner</a>, <a href="https://publications.waset.org/abstracts/search?q=Joerg%20Stelzner"> Joerg Stelzner</a>, <a href="https://publications.waset.org/abstracts/search?q=Damian%20Gwerder"> Damian Gwerder</a>, <a href="https://publications.waset.org/abstracts/search?q=Philipp%20Schuetz"> Philipp Schuetz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ring%20recognition" title="ring recognition">ring recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=X-ray%20computed%20tomography" title=" X-ray computed tomography"> X-ray computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrochronology" title=" dendrochronology"> dendrochronology</a> </p> <a href="https://publications.waset.org/abstracts/130684/image-processing-approach-for-detection-of-three-dimensional-tree-rings-from-x-ray-computed-tomography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130684.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21616</span> An investigation of Leading Edge and Trailing Edge Corrugation for Low Reynolds Number Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Hassan%20Raza%20Shah">Syed Hassan Raza Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mohammad%20Ali"> Mohammad Mohammad Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The flow over a smoothly profiled airfoil at a low Reynolds number is highly susceptible to separate even at a very low angle of attack. An investigation was made to study the effect of leading-edge and trailing-edge corrugation with the spanwise change in the ridges resulted due to the change in the chord length for an infinite wing. The wind tunnel results using NACA0018 wings revealed that leading and trailing edge corrugation did not have any benefit in terms of aerodynamic efficiency or delayed stall. The leading edge and trailing edge corrugation didn't change the lift curve slope, with the leading edge corrugation wing stalling first in the range of Reynolds number of 50,000 to 125,000. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=leading%20and%20trailing%20edge%20corrugations" title="leading and trailing edge corrugations">leading and trailing edge corrugations</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20reynolds%20number" title=" low reynolds number"> low reynolds number</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20tunnel%20testing" title=" wind tunnel testing"> wind tunnel testing</a>, <a href="https://publications.waset.org/abstracts/search?q=NACA0018" title=" NACA0018"> NACA0018</a> </p> <a href="https://publications.waset.org/abstracts/141121/an-investigation-of-leading-edge-and-trailing-edge-corrugation-for-low-reynolds-number-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141121.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21615</span> A Study of Effective Stereo Matching Method for Long-Wave Infrared Camera Module</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Koo%20Kim">Hyun-Koo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonghun%20Kim"> Yonghun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Hoon%20Kim"> Yong-Hoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Hee%20Lee"> Ju Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Myungho%20Song"> Myungho Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20driver%20assistance%20system" title="advanced driver assistance system">advanced driver assistance system</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title=" pedestrian detection"> pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20matching%20method" title=" stereo matching method"> stereo matching method</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20long-wave%20IR%20camera" title=" stereo long-wave IR camera"> stereo long-wave IR camera</a> </p> <a href="https://publications.waset.org/abstracts/58413/a-study-of-effective-stereo-matching-method-for-long-wave-infrared-camera-module" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58413.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21614</span> X-Corner Detection for Camera Calibration Using Saddle Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20S.%20Alturki">Abdulrahman S. Alturki</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20S.%20Loomis"> John S. Loomis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title="camera calibration">camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=corner%20detector" title=" corner detector"> corner detector</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detector" title=" edge detector"> edge detector</a>, <a href="https://publications.waset.org/abstracts/search?q=saddle%20points" title=" saddle points"> saddle points</a> </p> <a href="https://publications.waset.org/abstracts/40538/x-corner-detection-for-camera-calibration-using-saddle-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21613</span> Comparison of Central Light Reflex Width-to-Retinal Vessel Diameter Ratio between Glaucoma and Normal Eyes by Using Edge Detection Technique </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Siriarchawatana">P. Siriarchawatana</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Leungchavaphongse"> K. Leungchavaphongse</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Covavisaruch"> N. Covavisaruch</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Rojananuangnit"> K. Rojananuangnit</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Boondaeng"> P. Boondaeng</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Panyayingyong"> N. Panyayingyong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glaucoma is a disease that causes visual loss in adults. Glaucoma causes damage to the optic nerve and its overall pathophysiology is still not fully understood. Vasculopathy may be one of the possible causes of nerve damage. Photographic imaging of retinal vessels by fundus camera during eye examination may complement clinical management. This paper presents an innovation for measuring central light reflex width-to-retinal vessel diameter ratio (CRR) from digital retinal photographs. Using our edge detection technique, CRRs from glaucoma and normal eyes were compared to examine differences and associations. CRRs were evaluated on fundus photographs of participants from Mettapracharak (Wat Raikhing) Hospital in Nakhon Pathom, Thailand. Fifty-five photographs from normal eyes and twenty-one photographs from glaucoma eyes were included. Participants with hypertension were excluded. In each photograph, CRRs from four retinal vessels, including arteries and veins in the inferotemporal and superotemporal regions, were quantified using edge detection technique. From our finding, mean CRRs of all four retinal arteries and veins were significantly higher in persons with glaucoma than in those without glaucoma (0.34 <em>vs</em>. 0.32, <em>p</em> < 0.05 for inferotemporal vein, 0.33 <em>vs</em>. 0.30, <em>p</em> < 0.01 for inferotemporal artery, 0.34 <em>vs</em>. 0.31, <em>p </em>< 0.01 for superotemporal vein, and 0.33 <em>vs</em>. 0.30, <em>p</em> < 0.05 for superotemporal artery). From these results, an increase in CRRs of retinal vessels, as quantitatively measured from fundus photographs, could be associated with glaucoma. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=glaucoma" title="glaucoma">glaucoma</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20vessel" title=" retinal vessel"> retinal vessel</a>, <a href="https://publications.waset.org/abstracts/search?q=central%20light%20reflex" title=" central light reflex"> central light reflex</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=fundus%20photograph" title=" fundus photograph"> fundus photograph</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a> </p> <a href="https://publications.waset.org/abstracts/54545/comparison-of-central-light-reflex-width-to-retinal-vessel-diameter-ratio-between-glaucoma-and-normal-eyes-by-using-edge-detection-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21612</span> Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Urvee%20B.%20Trivedi">Urvee B. Trivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20D.%20Dalal"> U. D. Dalal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio%20%28CR%29" title="cognitive radio (CR)">cognitive radio (CR)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection%20%28PD%29" title=" probability of detection (PD)"> probability of detection (PD)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm%20%28PF%29" title=" probability of false alarm (PF)"> probability of false alarm (PF)</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20user%20%28PU%29" title=" primary user (PU)"> primary user (PU)</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20user%20%28SU%29" title=" secondary user (SU)"> secondary user (SU)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20Fourier%20transform%20%28FFT%29" title=" fast Fourier transform (FFT)"> fast Fourier transform (FFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20ratio%20%28SNR%29" title=" signal to noise ratio (SNR)"> signal to noise ratio (SNR)</a> </p> <a href="https://publications.waset.org/abstracts/45944/energy-detection-based-sensing-and-primary-user-traffic-classification-for-cognitive-radio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45944.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" 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