{"id":2957,"date":"2020-10-31T08:52:14","date_gmt":"2020-10-31T08:52:14","guid":{"rendered":"http:\/\/onlineclassesguru.com\/?p=2957"},"modified":"2020-10-31T08:52:14","modified_gmt":"2020-10-31T08:52:14","slug":"image-segmentation-using-network-flow","status":"publish","type":"post","link":"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/","title":{"rendered":"Image Segmentation using Network Flow"},"content":{"rendered":"<style type=\"text\/css\"><\/style><p>Image Segmentation using Network Flow<\/p>\n<p>In the image segmentation problem, the input is an image, and we would like to partition it into background and foreground. The MAXFLOW algorithm can be used to solve the segmentation problem in polynomial time by converting the image into a graph. The input is a bitmap on a grid where every grid node represents a pixel. We convert this grid into a directed graph G, by interpreting every edge of the grid as two directed edges.\u00a0 Specifically, the input for out problem is as follows:<\/p>\n<ul>\n<li>A bitmap of size N \u00d7 N, with an associated directed graph G = (V, E).<\/li>\n<li>For every pixel i, we have a value fi \u2265 0, which is an estimate of the likelihood of this pixel to be in the foreground (i.e., the larger fi is the more probable that it is in the foreground). The pixel intensity can be used to estimate fi, e.g. assuming that foreground pixels have a higher intensity than background pixels, brighter pixels have a higher probability of belonging to the foreground.<\/li>\n<li>For every pixel i, we have (similarly) an estimate bi of the likelihood of pixel i to be in the background<\/li>\n<\/ul>\n<p>.\u00a0 \u2022 For every two adjacent pixels i and j we have a separation penalty pij , which is the \u201cprice\u201d of separating i from j. This quantity is defined only for adjacent pixels in the bitmap. The separation penalty is higher when i and j have similar intensities.<\/p>\n<ul>\n<li>We introduce two new vertices s (foreground) and t (background) and an edge connecting each pixel with s and t as shown in the example below.<\/li>\n<li>The optimal segmentation can be found by finding the minimum s-t cut.<\/li>\n<\/ul>\n<p>Goal: implement a software solution that takes as input an image and outputs the segmentation of the image into foreground and background pixels.<\/p>\n<p><center><a href=\"http:\/\/onlineclassesguru.com\/orders\/ordernow\"><img decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTyj99p60XCLyLk1htB7-1neRt8-2QdnenNlQ&usqp=CAU\"target=\"_http:\/\/onlineclassesguru.com\/orders\/ordernow\"\/><\/center><p>","protected":false},"excerpt":{"rendered":"<p>Image Segmentation using Network Flow In the image segmentation problem, the input is an image, and we would like to partition it into background and foreground. The MAXFLOW algorithm can be used to solve the segmentation problem in polynomial time by converting the image into a graph. The input is a bitmap on a grid&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2957","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Image Segmentation using Network Flow - onlineclassesguru<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Image Segmentation using Network Flow - onlineclassesguru\" \/>\n<meta property=\"og:description\" content=\"Image Segmentation using Network Flow In the image segmentation problem, the input is an image, and we would like to partition it into background and foreground. The MAXFLOW algorithm can be used to solve the segmentation problem in polynomial time by converting the image into a graph. The input is a bitmap on a grid...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/\" \/>\n<meta property=\"og:site_name\" content=\"onlineclassesguru\" \/>\n<meta property=\"article:published_time\" content=\"2020-10-31T08:52:14+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin_admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"https:\/\/onlineclassesguru.com\/#website\",\"url\":\"https:\/\/onlineclassesguru.com\/\",\"name\":\"onlineclassesguru\",\"description\":\"Cheap Professional coursework and reaction papers help\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/onlineclassesguru.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/#webpage\",\"url\":\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/\",\"name\":\"Image Segmentation using Network Flow - onlineclassesguru\",\"isPartOf\":{\"@id\":\"https:\/\/onlineclassesguru.com\/#website\"},\"datePublished\":\"2020-10-31T08:52:14+00:00\",\"dateModified\":\"2020-10-31T08:52:14+00:00\",\"author\":{\"@id\":\"https:\/\/onlineclassesguru.com\/#\/schema\/person\/1831fa4d28e47b468621cf27932f5742\"},\"breadcrumb\":{\"@id\":\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/onlineclassesguru.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Image Segmentation using Network Flow\"}]},{\"@type\":\"Person\",\"@id\":\"https:\/\/onlineclassesguru.com\/#\/schema\/person\/1831fa4d28e47b468621cf27932f5742\",\"name\":\"admin_admin\",\"image\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/onlineclassesguru.com\/#personlogo\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/429c8d043f7a770af242b0031e8b9f2b?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/429c8d043f7a770af242b0031e8b9f2b?s=96&d=mm&r=g\",\"caption\":\"admin_admin\"},\"url\":\"https:\/\/onlineclassesguru.com\/index.php\/author\/admin_admin\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Image Segmentation using Network Flow - onlineclassesguru","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/onlineclassesguru.com\/index.php\/2020\/10\/31\/image-segmentation-using-network-flow\/","og_locale":"en_US","og_type":"article","og_title":"Image Segmentation using Network Flow - onlineclassesguru","og_description":"Image Segmentation using Network Flow In the image segmentation problem, the input is an image, and we would like to partition it into background and foreground. The MAXFLOW algorithm can be used to solve the segmentation problem in polynomial time by converting the image into a graph. 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