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processing</title> <meta name="description" content="Search results for: image processing"> <meta name="keywords" content="image processing"> <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 d-lg-none 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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="image processing"> <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> 5836</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: image processing</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5836</span> A Review on Artificial Neural Networks in Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Afsharipoor">B. Afsharipoor</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Nazemi"> E. Nazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</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=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=MANN" title=" MANN"> MANN</a> </p> <a href="https://publications.waset.org/abstracts/36843/a-review-on-artificial-neural-networks-in-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36843.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">407</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">5835</span> A Deep Learning Based Approach for Dynamically Selecting Pre-processing Technique for Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Revoti%20Prasad%20Bora">Revoti Prasad Bora</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikita%20Katyal"> Nikita Katyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabh%20Yadav"> Saurabh Yadav</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep-learning" title="deep-learning">deep-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-processing" title=" pre-processing"> pre-processing</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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a> </p> <a href="https://publications.waset.org/abstracts/148397/a-deep-learning-based-approach-for-dynamically-selecting-pre-processing-technique-for-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148397.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">163</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">5834</span> Improvement Image Summarization using Image Processing and Particle swarm optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Torabifard">Hooman Torabifard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20summarization" title="image summarization">image summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20threshold" title=" image threshold"> image threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/138289/improvement-image-summarization-using-image-processing-and-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138289.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">133</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">5833</span> Rough Neural Networks in Adapting Cellular Automata Rule for Reducing Image Noise</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan">Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20neural%20networks" title=" rough neural networks"> rough neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title=" cellular automata"> cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/1516/rough-neural-networks-in-adapting-cellular-automata-rule-for-reducing-image-noise" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1516.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">439</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">5832</span> High Speed Image Rotation Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hee-Choul%20Kwon">Hee-Choul Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyungjin%20Cho"> Hyungjin Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Heeyong%20Kwon"> Heeyong Kwon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20speed%20rotation%20operation" title="high speed rotation operation">high speed rotation operation</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=image%20rotation" title=" image rotation"> image rotation</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation%20matrix" title=" transformation matrix"> transformation matrix</a> </p> <a href="https://publications.waset.org/abstracts/25258/high-speed-image-rotation-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25258.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">506</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">5831</span> Image Rotation Using an Augmented 2-Step Shear Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hee-Choul%20Kwon">Hee-Choul Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Heeyong%20Kwon"> Heeyong Kwon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-speed%20rotation%20operation" title="high-speed rotation operation">high-speed rotation operation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20rotation" title=" image rotation"> image rotation</a>, <a href="https://publications.waset.org/abstracts/search?q=transform%20matrix" title=" transform matrix"> transform matrix</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=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/64167/image-rotation-using-an-augmented-2-step-shear-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64167.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">277</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">5830</span> Post-Processing Method for Performance Improvement of Aerial Image Parcel Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Donghee%20Noh">Donghee Noh</a>, <a href="https://publications.waset.org/abstracts/search?q=Seonhyeong%20Kim"> Seonhyeong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Junhwan%20Choi"> Junhwan Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Heegon%20Kim"> Heegon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sooho%20Jung"> Sooho Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Keunho%20Park"> Keunho Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20image" title="aerial image">aerial image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20process" title=" image process"> image process</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20field%20smart%20farm" title=" open field smart farm"> open field smart farm</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/170203/post-processing-method-for-performance-improvement-of-aerial-image-parcel-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170203.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">5829</span> Robust Image Design Based Steganographic System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadiq%20J.%20Abou-Loukh">Sadiq J. Abou-Loukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20M.%20Habbi"> Hanan M. Habbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=encryption" title="encryption">encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%0D%0Apredictive%20coding" title=" differential predictive coding"> differential predictive coding</a>, <a href="https://publications.waset.org/abstracts/search?q=four%20triangles%20operation" title=" four triangles operation "> four triangles operation </a> </p> <a href="https://publications.waset.org/abstracts/16654/robust-image-design-based-steganographic-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16654.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">493</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">5828</span> Performance of Hybrid Image Fusion: Implementation of Dual-Tree Complex Wavelet Transform Technique </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manoj%20Gupta">Manoj Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirmendra%20Singh%20Bhadauria"> Nirmendra Singh Bhadauria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DT-CWT" title=" DT-CWT"> DT-CWT</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20image%20fusion" title=" average image fusion"> average image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20image%20fusion" title=" hybrid image fusion"> hybrid image fusion</a> </p> <a href="https://publications.waset.org/abstracts/19207/performance-of-hybrid-image-fusion-implementation-of-dual-tree-complex-wavelet-transform-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19207.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">606</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">5827</span> Automatic Algorithm for Processing and Analysis of Images from the Comet Assay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeimy%20L.%20Quintana">Yeimy L. Quintana</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20G.%20Zuluaga"> Juan G. Zuluaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20S.%20Arango"> Sandra S. Arango</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The comet assay is a method based on electrophoresis that is used to measure DNA damage in cells and has shown important results in the identification of substances with a potential risk to the human population as innumerable physical, chemical and biological agents. With this technique is possible to obtain images like a comet, in which the tail of these refers to damaged fragments of the DNA. One of the main problems is that the image has unequal luminosity caused by the fluorescence microscope and requires different processing to condition it as well as to know how many optimal comets there are per sample and finally to perform the measurements and determine the percentage of DNA damage. In this paper, we propose the design and implementation of software using Image Processing Toolbox-MATLAB that allows the automation of image processing. The software chooses the optimum comets and measuring the necessary parameters to detect the damage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20vision" title="artificial vision">artificial vision</a>, <a href="https://publications.waset.org/abstracts/search?q=comet%20assay" title=" comet assay"> comet assay</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20damage" title=" DNA damage"> DNA damage</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/69288/automatic-algorithm-for-processing-and-analysis-of-images-from-the-comet-assay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69288.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">310</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">5826</span> External Retinal Prosthesis Image Processing System Used One-Cue Saliency Map Based on DSP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yili%20Chen">Yili Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jixiang%20Fu"> Jixiang Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhihua%20Liu"> Zhihua Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhicheng%20Zhang"> Zhicheng Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongmao%20Li"> Rongmao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Nan%20Fu"> Nan Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaoqin%20Xie"> Yaoqin Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retinal prothesis is designed to help the blind to get some sight.It is made up of internal part and external part.In external part ,there is made up of camera, image processing, and RF transmitter.In internal part, there is RF receiver, implant chip,micro-electrode.The image got from the camera should be processed by suitable stragies to corresponds to stimulus the electrode.Nowadays, the number of the micro-electrode is hundreds and we don鈥檛 know the mechanism how the elctrode stimulus the optic nerve, an easy way to the hypothesis is that the pixel in the image is correspondence to the electrode.So it is a question how to get the important information of the image captured from the picture.There are many strategies to experimented to get the most important information as soon as possible, due to the real time system.ROI is a useful algorithem to extract the region of the interest.Our paper will explain the details of the orinciples and functions of the ROI.And based on this, we simplified the ROI algrithem,and used it in outside image prcessing DSP system of the retinal prothesis.Results show that our image processing stratiges is suitable for real-time retinal prothesis and can cut redundant information and help useful information to express in the low-size image. <p class="card-text"><strong>Keywords:</strong> <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=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=saliency%20map" title=" saliency map"> saliency map</a>, <a href="https://publications.waset.org/abstracts/search?q=low-size%20image" title=" low-size image"> low-size image</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20information%20express" title=" useful information express"> useful information express</a>, <a href="https://publications.waset.org/abstracts/search?q=cut%20redundant%20information%20in%20image" title=" cut redundant information in image"> cut redundant information in image</a> </p> <a href="https://publications.waset.org/abstracts/37364/external-retinal-prosthesis-image-processing-system-used-one-cue-saliency-map-based-on-dsp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37364.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">282</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">5825</span> Pre-Processing of Ultrasonography Image Quality Improvement in Cases of Cervical Cancer Using Image Enhancement </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=Teguh%20Budiono"> Teguh Budiono</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogi%20Ramadhani"> Yogi Ramadhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Haris%20B.%20Widodo"> Haris B. Widodo</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwita%20Mulyawati"> Arwita Mulyawati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cervical cancer is the leading cause of mortality in cancer-related diseases. In this diagnosis doctors usually perform several tests to determine the presence of cervical cancer in a patient. However, these checks require support equipment to get the results in more detail. One is by using ultrasonography. However, for the developing countries most of the existing ultrasonography has a low resolution. The goal of this research is to obtain abnormalities on low-resolution ultrasound images especially for cervical cancer case. In this paper, we emphasize our work to use Image Enhancement for pre-processing image quality improvement. The result shows that pre-processing stage is promising to support further analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cervical%20cancer" title="cervical cancer">cervical cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mortality" title=" mortality"> mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=low-resolution" title=" low-resolution"> low-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement." title=" image enhancement. "> image enhancement. </a> </p> <a href="https://publications.waset.org/abstracts/26523/pre-processing-of-ultrasonography-image-quality-improvement-in-cases-of-cervical-cancer-using-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26523.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">5824</span> A Survey on Types of Noises and De-Noising Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amandeep%20Kaur">Amandeep Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital Image processing is a fundamental tool to perform various operations on the digital images for pattern recognition, noise removal and feature extraction. In this paper noise removal technique has been described for various types of noises. This paper comprises discussion about various noises available in the image due to different environmental, accidental factors. In this paper, various de-noising approaches have been discussed that utilize different wavelets and filters for de-noising. By analyzing various papers on image de-noising we extract that wavelet based de-noise approaches are much effective as compared to others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=de-noising%20techniques" title="de-noising techniques">de-noising techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=edges" title=" edges"> edges</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/54155/a-survey-on-types-of-noises-and-de-noising-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54155.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">336</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">5823</span> The Use of Image Processing Responses Tools Applied to Analysing Bouguer Gravity Anomaly Map (Tangier-Tetuan's Area-Morocco)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Bakkali">Saad Bakkali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image processing is a powerful tool for the enhancement of edges in images used in the interpretation of geophysical potential field data. Arial and terrestrial gravimetric surveys were carried out in the region of Tangier-Tetuan. From the observed and measured data of gravity Bouguer gravity anomalies map was prepared. This paper reports the results and interpretations of the transformed maps of Bouguer gravity anomaly of the Tangier-Tetuan area using image processing. Filtering analysis based on classical image process was applied. Operator image process like logarithmic and gamma correction are used. This paper also present the results obtained from this image processing analysis of the enhancement edges of the Bouguer gravity anomaly map of the Tangier-Tetuan zone. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bouguer" title="bouguer">bouguer</a>, <a href="https://publications.waset.org/abstracts/search?q=tangier" title=" tangier"> tangier</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20correction" title=" gamma correction"> gamma correction</a>, <a href="https://publications.waset.org/abstracts/search?q=logarithmic%20enhancement%20edges" title=" logarithmic enhancement edges"> logarithmic enhancement edges</a> </p> <a href="https://publications.waset.org/abstracts/36524/the-use-of-image-processing-responses-tools-applied-to-analysing-bouguer-gravity-anomaly-map-tangier-tetuans-area-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36524.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">422</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">5822</span> Quantum Entangled States and Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20%20Singh">Sanjay Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sushil%20Kumar"> Sushil Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rashmi%20Jain"> Rashmi Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Greenberger-Horne-Zeilinger" title="Greenberger-Horne-Zeilinger">Greenberger-Horne-Zeilinger</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20storage%20and%20retrieval" title=" image storage and retrieval"> image storage and retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20entanglement" title=" quantum entanglement"> quantum entanglement</a>, <a href="https://publications.waset.org/abstracts/search?q=W%20states" title=" W states"> W states</a> </p> <a href="https://publications.waset.org/abstracts/67732/quantum-entangled-states-and-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67732.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">306</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">5821</span> A Modified Shannon Entropy Measure for Improved Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20U.%20Khan">Mohammad A. U. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20A.%20Kittaneh"> Omar A. Kittaneh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Akbar"> M. Akbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tariq%20M.%20Khan"> Tariq M. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Husam%20A.%20Bayoud"> Husam A. Bayoud </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Shannon Entropy measure has been widely used for measuring uncertainty. However, in partial settings, the histogram is used to estimate the underlying distribution. The histogram is dependent on the number of bins used. In this paper, a modification is proposed that makes the Shannon entropy based on histogram consistent. For providing the benefits, two application are picked in medical image processing applications. The simulations are carried out to show the superiority of this modified measure for image segmentation problem. The improvement may be contributed to robustness shown to uneven background in images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shannon%20entropy" title="Shannon entropy">Shannon entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20processing" title=" medical image processing"> medical image processing</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=modification" title=" modification"> modification</a> </p> <a href="https://publications.waset.org/abstracts/19414/a-modified-shannon-entropy-measure-for-improved-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19414.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">497</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">5820</span> Ice Load Measurements on Known Structures Using Image Processing Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azam%20Fazelpour">Azam Fazelpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20R.%20Dehghani"> Saeed R. Dehghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Vlastimil%20Masek"> Vlastimil Masek</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20S.%20Muzychka"> Yuri S. Muzychka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates. <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=ice%20detection" title=" ice detection"> ice detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ice%20load%20measurements" title=" ice load measurements"> ice load measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/74768/ice-load-measurements-on-known-structures-using-image-processing-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74768.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">368</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">5819</span> Optimizing Machine Learning Through Python Based Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinidhi.%20A">Srinidhi. A</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Twinkle%20Hareendran"> Twinkle Hareendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Vriksha%20Prakash"> Vriksha Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models. <p class="card-text"><strong>Keywords:</strong> <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=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a> </p> <a href="https://publications.waset.org/abstracts/193107/optimizing-machine-learning-through-python-based-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193107.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">15</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">5818</span> Design and Implementation of Image Super-Resolution for Myocardial Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy">M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian"> M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Super-resolution is the technique of intelligently upscaling images, avoiding artifacts or blurring, and deals with the recovery of a high-resolution image from one or more low-resolution images. Single-image super-resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super-resolution has been demonstrated to improve image quality in scaled down images in the image domain, its effects on the Fourier-based technique remains unknown. Super-resolution substantially improved the spatial resolution of the patient LGE images by sharpening the edges of the heart and the scar. This paper aims at investigating the effects of single image super-resolution on Fourier-based and image based methods of scale-up. In this paper, first, generate a training phase of the low-resolution image and high-resolution image to obtain dictionary. In the test phase, first, generate a patch and then difference of high-resolution image and interpolation image from the low-resolution image. Next simulation of the image is obtained by applying convolution method to the dictionary creation image and patch extracted the image. Finally, super-resolution image is obtained by combining the fused image and difference of high-resolution and interpolated image. Super-resolution reduces image errors and improves the image quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20dictionary%20creation" title="image dictionary creation">image dictionary creation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20super-resolution" title=" image super-resolution"> image super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=LGE%20images" title=" LGE images"> LGE images</a>, <a href="https://publications.waset.org/abstracts/search?q=patch%20extraction" title=" patch extraction"> patch extraction</a> </p> <a href="https://publications.waset.org/abstracts/59494/design-and-implementation-of-image-super-resolution-for-myocardial-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59494.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">375</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">5817</span> Detecting and Disabling Digital Cameras Using D3CIP Algorithm Based on Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Vignesh">S. Vignesh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Rangasamy"> K. S. Rangasamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper deals with the device capable of detecting and disabling digital cameras. The system locates the camera and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro-reflective and sends light back directly to its original source at the same angle. The device shines infrared LED light, which is invisible to the human eye, at a distance of about 20 feet. It then collects video of these reflections with a camcorder. Then the video of the reflections is transferred to a computer connected to the device, where it is sent through image processing algorithms that pick out infrared light bouncing back. Once the camera is detected, the device would project an invisible infrared laser into the camera's lens, thereby overexposing the photo and rendering it useless. Low levels of infrared laser neutralize digital cameras but are neither a health danger to humans nor a physical damage to cameras. We also discuss the simplified design of the above device that can used in theatres to prevent piracy. The domains being covered here are optics and image processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CCD" title="CCD">CCD</a>, <a href="https://publications.waset.org/abstracts/search?q=optics" title=" optics"> optics</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=D3CIP" title=" D3CIP"> D3CIP</a> </p> <a href="https://publications.waset.org/abstracts/1736/detecting-and-disabling-digital-cameras-using-d3cip-algorithm-based-on-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1736.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">357</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">5816</span> Assessment of Image Databases Used for Human Skin Detection Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saleh%20Alshehri">Saleh Alshehri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20databases" title="image databases">image databases</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=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/87836/assessment-of-image-databases-used-for-human-skin-detection-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87836.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">271</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">5815</span> Design and Development of 5-DOF Color Sorting Manipulator for Industrial Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atef%20A.%20Ata">Atef A. Ata</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohair%20F.%20Rezeka"> Sohair F. Rezeka</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20El-Shenawy"> Ahmed El-Shenawy</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Diab"> Mohammed Diab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image processing in today鈥檚 world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system applications which consists of two integrated stations of processing and handling with a new image processing feature. Existing color sorting techniques use a set of inductive, capacitive, and optical sensors to differentiate object color. This research presents a mechatronics color sorting system solution with the application of image processing. A 5-DOF robot arm is designed and developed with pick and place operation to be main part of the color sorting system. Image processing procedure senses the circular objects in an image captured in real time by a webcam attached at the end-effector then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator that has pick-and-place mechanism. Performance analysis proves that this color based object sorting system works very accurate under ideal condition in term of adequate illumination, circular objects shape and color. The circular objects tested for sorting are red, green and blue. For non-ideal condition, such as unspecified color the accuracy reduces to 80%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotics%20manipulator" title="robotics manipulator">robotics manipulator</a>, <a href="https://publications.waset.org/abstracts/search?q=5-DOF%20manipulator" title=" 5-DOF manipulator"> 5-DOF manipulator</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=color%20sorting" title=" color sorting"> color sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=pick-and-place" title=" pick-and-place"> pick-and-place</a> </p> <a href="https://publications.waset.org/abstracts/1473/design-and-development-of-5-dof-color-sorting-manipulator-for-industrial-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1473.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">374</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">5814</span> Automatic Music Score Recognition System Using Digital Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Hsiang%20Chang">Yuan-Hsiang Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhong-Xian%20Peng"> Zhong-Xian Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Der%20Jeng"> Li-Der Jeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an <em>Automatic music score recognition system using digital image processing</em>, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connected%20component%20labeling" title="connected component labeling">connected component labeling</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=morphological%20processing" title=" morphological processing"> morphological processing</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20musical%20recognition" title=" optical musical recognition"> optical musical recognition</a> </p> <a href="https://publications.waset.org/abstracts/13588/automatic-music-score-recognition-system-using-digital-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13588.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">419</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">5813</span> The Resistance Reader Program Based on Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janpen%20Srijan">Janpen Srijan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nahathai%20%20Tanmang"> Nahathai Tanmang</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanit%20Purathanang"> Thanit Purathanang</a>, <a href="https://publications.waset.org/abstracts/search?q=Anun%20Dowchern"> Anun Dowchern</a>, <a href="https://publications.waset.org/abstracts/search?q=Saksit%20Summart"> Saksit Summart</a>, <a href="https://publications.waset.org/abstracts/search?q=Seangduan%20Kampimpa"> Seangduan Kampimpa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the resistance reader program based on image processing by using MATLAB. The proposed program is divided into six parts; the first part is the web camera; the second part is a watt selection before shooting the resistor; the third part is a part of finding the position of the color on the mid-point of resistor; the fourth part is a part of identifying color code of the resistor; the fifth part is a part of taking the number of values for each color for resistance calculation and the last part is a part of displaying result of resistance value. The experimental result of the resistance reader program based on image processing was able to display the resistance value of resistor. The accuracy of proposed program is 85 percent for 1 watt resistor. It has 15 percent of reading error because a problem with the color code of some resistor was too bright. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resistance%20reader%20program" title="resistance reader program">resistance reader program</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=resistor" title=" resistor"> resistor</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/39785/the-resistance-reader-program-based-on-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39785.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">389</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">5812</span> Image Segmentation Techniques: Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lindani%20Mbatha">Lindani Mbatha</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvendi%20Rimer"> Suvendi Rimer</a>, <a href="https://publications.waset.org/abstracts/search?q=Mpho%20Gololo"> Mpho Gololo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering-based" title="clustering-based">clustering-based</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution-network" title=" convolution-network"> convolution-network</a>, <a href="https://publications.waset.org/abstracts/search?q=edge-based" title=" edge-based"> edge-based</a>, <a href="https://publications.waset.org/abstracts/search?q=region-growing" title=" region-growing"> region-growing</a> </p> <a href="https://publications.waset.org/abstracts/166513/image-segmentation-techniques-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166513.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">96</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">5811</span> Crop Classification using Unmanned Aerial Vehicle Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iqra%20Yaseen">Iqra Yaseen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it. <p class="card-text"><strong>Keywords:</strong> <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=UAV" title=" UAV"> UAV</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLO" title=" YOLO"> YOLO</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</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=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/157744/crop-classification-using-unmanned-aerial-vehicle-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157744.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">107</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">5810</span> Design of a Graphical User Interface for Data Preprocessing and Image Segmentation Process in 2D MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enver%20Kucukkulahli">Enver Kucukkulahli</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakize%20Erdogmus"> Pakize Erdogmus</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat"> Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software. <p class="card-text"><strong>Keywords:</strong> <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=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=GUI" title=" GUI"> GUI</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20MRI" title=" 2D MRI "> 2D MRI </a> </p> <a href="https://publications.waset.org/abstracts/68097/design-of-a-graphical-user-interface-for-data-preprocessing-and-image-segmentation-process-in-2d-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68097.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">377</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">5809</span> Agile Real-Time Field Programmable Gate Array-Based Image Processing System for Drone Imagery in Digital Agriculture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabiha%20Shahid%20Antora">Sabiha Shahid Antora</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20Ki%20Chang"> Young Ki Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Along with various farm management technologies, imagery is an important tool that facilitates crop assessment, monitoring, and management. As a consequence, drone imaging technology is playing a vital role to capture the state of the entire field for yield mapping, crop scouting, weed detection, and so on. Although it is essential to inspect the cultivable lands in real-time for making rapid decisions regarding field variable inputs to combat stresses and diseases, drone imagery is still evolving in this area of interest. Cost margin and post-processing complexions of the image stream are the main challenges of imaging technology. Therefore, this proposed project involves the cost-effective field programmable gate array (FPGA) based image processing device that would process the image stream in real-time as well as providing the processed output to support on-the-spot decisions in the crop field. As a result, the real-time FPGA-based image processing system would reduce operating costs while minimizing a few intermediate steps to deliver scalable field decisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time" title="real-time">real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20imagery" title=" drone imagery"> drone imagery</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=crop%20monitoring" title=" crop monitoring"> crop monitoring</a> </p> <a href="https://publications.waset.org/abstracts/132611/agile-real-time-field-programmable-gate-array-based-image-processing-system-for-drone-imagery-in-digital-agriculture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132611.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">113</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">5808</span> Steel Bridge Coating Inspection Using Image Processing with Neural Network Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Elbeheri">Ahmed Elbeheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Zayed"> Tarek Zayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it鈥檚 the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=steel%20bridge" title="steel bridge">steel bridge</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20inspection" title=" bridge inspection"> bridge inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20corrosion" title=" steel corrosion"> steel corrosion</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/83264/steel-bridge-coating-inspection-using-image-processing-with-neural-network-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83264.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">306</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">5807</span> Robust and Real-Time Traffic Counting System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossam%20M.%20Moftah">Hossam M. Moftah</a>, <a href="https://publications.waset.org/abstracts/search?q=Aboul%20Ella%20Hassanien"> Aboul Ella Hassanien</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20counting" title="traffic counting">traffic counting</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20management" title=" traffic management"> traffic management</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=object%20detection" title=" object detection"> object detection</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/43835/robust-and-real-time-traffic-counting-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43835.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> <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" href="https://publications.waset.org/abstracts/search?q=image%20processing&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20processing&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20processing&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20processing&page=5">5</a></li> <li class="page-item"><a class="page-link" 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