Segmentation of Noisy Digital Images with Stochastic Gradient Kernel

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Segmentation of Noisy Digital Images with Stochastic Gradient Kernel</title> <meta name="description" content="Segmentation of Noisy Digital Images with Stochastic Gradient Kernel"> <meta name="keywords" content="Image segmentation, edge Detection, noisy images, spatialfilters, stochastic gradient kernel."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Segmentation of Noisy Digital Images with Stochastic Gradient Kernel"> <meta name="citation_author" content="Abhishek Neogi"> <meta name="citation_author" content="Jayesh Verma"> <meta name="citation_author" content="Pinaki Pratim Acharjya"> <meta name="citation_publication_date" content="2015/06/01"> <meta name="citation_journal_title" content="International Journal of Computer and Information Engineering"> <meta name="citation_volume" content="8"> <meta name="citation_issue" content="11"> <meta name="citation_firstpage" 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href="https://publications.waset.org/search?q=Jayesh%20Verma"> Jayesh Verma</a>, <a href="https://publications.waset.org/search?q=Pinaki%20Pratim%20Acharjya"> Pinaki Pratim Acharjya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image segmentation and edge detection is a fundamental section in image processing. In case of noisy images Edge Detection is very less effective if we use conventional Spatial Filters like Sobel, Prewitt, LOG, Laplacian etc. To overcome this problem we have proposed the use of Stochastic Gradient Mask instead of Spatial Filters for generating gradient images. The present study has shown that the resultant images obtained by applying Stochastic Gradient Masks appear to be much clearer and sharper as per Edge detection is considered. <iframe src="https://publications.waset.org/10001671.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Image%20segmentation" title="Image segmentation">Image segmentation</a>, <a href="https://publications.waset.org/search?q=edge%20Detection" title=" edge Detection"> edge Detection</a>, <a href="https://publications.waset.org/search?q=noisy%20images" title=" noisy images"> noisy images</a>, <a href="https://publications.waset.org/search?q=spatialfilters" title=" spatialfilters"> spatialfilters</a>, <a href="https://publications.waset.org/search?q=stochastic%20gradient%20kernel." title=" stochastic gradient kernel."> stochastic gradient kernel.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1107125" target="_blank">doi.org/10.5281/zenodo.1107125</a> </p> <a href="https://publications.waset.org/10001671/segmentation-of-noisy-digital-images-with-stochastic-gradient-kernel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001671/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001671/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001671/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001671/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001671/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001671/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001671/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001671/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001671/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001671/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001671.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">1520</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] L. Vincent, "Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms," IEEE Transactions on Image Processing, vol. 2, pp. 176-201, 1993. <br>[2] P. Soille, “Morphological image analysis: principles and applications”, in Springer-Verlag New York, Inc. Secaucus, NJ, USA, 1999. <br>[3] J. Serra, “Image analysis and mathematical morphology”: Academic Press, Inc. Orlando, FL, USA, 1983. <br>[4] J. Serra and L. Vincent, "An overview of morphological filtering", Circuits, Systems, and Signal Processing, vol. 11, pp. 47-108, 1992. <br>[5] W. J. Niessen, K. L. Vincken, J. A. Weickert,and M. A. Viergever, "Nonlinear multiscale representations for image segmentation," Computer Vision and Image Understanding, vol. 66, pp. 233-245, 1997. <br>[6] A.K.Jain, “Fundamentals of digital image processing”, Second Edition, Prentice Hall, 2002. <br>[7] Canny, J., “A Computational Approach to Edge Detection”, IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986. <br>[8] R. Deriche, Using Canny's criteria to derive a recursively implemented optimal edge detector, Int. J. Computer Vision, Vol. 1, pp. 167–187, April 1987. <br>[9] C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 2nd Edition, Addison Wesley Pub. 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Segmentation of Noisy Digital Images with Stochastic Gradient Kernel