{"id":1042,"date":"2013-06-11T11:27:42","date_gmt":"2013-06-11T16:27:42","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1042"},"modified":"2026-02-18T11:28:04","modified_gmt":"2026-02-18T16:28:04","slug":"cobb2013multi","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2013\/06\/11\/cobb2013multi\/","title":{"rendered":"Multi-Image texton selection for sonar image seabed co-segmentation"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>In this paper we describe an unsupervised approach to seabed co-segmentation over the multiple sonar images collected in sonar surveys. We adapt a traditional single image segmentation texton-based approach to the sonar survey task by modifying the texture extraction filter bank to better model possible sonar image textures. Two different algorithms for building a universal texton library are presented that produce common pixel labels across multiple images. Following pixel labeling with the universal texton library, images are quantized into superpixels and co-segmented using a DP clustering algorithm. The segmentation results for both texton library selection criteria are contrasted and compared for a labeled set of SAS images with various discernable textures.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"http:\/\/doi.org\/10.1117\/12.2016427\"><img decoding=\"async\" border=\"2\" alt=\"\u201cSPIE\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/SPIE-logo-cmyk-e1482256584489.jpg\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/cobb2013multi.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>J. T. Cobb and A. Zare, \u201cMulti-Image texton selection for sonar image seabed co-segmentation,\u201d in Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013. <\/code><\/pre>\n<pre><code>@InProceedings{cobb2013multi,\nTitle = {Multi-Image texton selection for sonar image seabed co-segmentation},\nAuthor = {Cobb, James T. and Zare, Alina},\nBooktitle = {Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII},\nYear = {2013},\nMonth = {June},\nNumber = {87090H},\nVolume = {8709},\nDoi = {10.1117\/12.2016427},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: In this paper we describe an unsupervised approach to seabed co-segmentation over the multiple sonar images collected in sonar surveys. We adapt a traditional single image segmentation texton-based approach to the sonar survey task by modifying the texture extraction filter bank to better model possible sonar image textures. Two different algorithms for building a [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[659,717,729],"class_list":["post-1042","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-segmentation","tag-superpixel","tag-synthetic-aperture-sonar"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1042"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1042\/revisions"}],"predecessor-version":[{"id":14651,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1042\/revisions\/14651"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1042"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1042"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}