{"id":1134,"date":"2014-09-11T14:57:06","date_gmt":"2014-09-11T19:57:06","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1134"},"modified":"2026-02-18T11:28:01","modified_gmt":"2026-02-18T16:28:01","slug":"cobb2014boundary","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2014\/09\/11\/cobb2014boundary\/","title":{"rendered":"Boundary detection and Superpixel formation in synthetic aperture sonar imagery"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A boundary detection algorithm for synthetic aper- ture sonar (SAS) images that draws upon a popular superpixel formation algorithm is detailed and tested against a set of SAS images containing a variety of common seabed categories. Textural clues are gathered using a novel DP clustering algorithm that replaces the traditional K-means operator used in texton clustering. The superpixel formation algorithm uses texture and image pixel intensity cues as the input to a K-way normalized cuts algorithms. Superpixel boundaries computed by the normalized cuts algorithm are then merged using a Jensen-Shannon (JS) divergence metric. These larger regions boundaries resulting from superpixel merging contain homogeneous seabed charac- teristics and are useful for follow on image segmentation and environmental parameter estimation. To illustrate the algorithm\u2019s performance, boundary detection results are plotted in receiver- operator characteristic curve plot and selected oversegmented images are displayed at various JS divergence thresholds.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/cobb2014boundary.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, \u201cBoundary detection and Superpixel formation in synthetic aperture sonar imagery,\u201d in 3rd Int. Conf. SAS and SAR, 2014. <\/code><\/pre>\n<pre><code>@InProceedings{cobb2014boundary,\nauthor = {Cobb, James T. and Zare, Alina},\ntitle = {Boundary detection and Superpixel formation in synthetic aperture sonar imagery},\nbooktitle = {3rd Int. Conf. SAS and SAR},\nyear = {2014},\nmonth = {Sept.},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A boundary detection algorithm for synthetic aper- ture sonar (SAS) images that draws upon a popular superpixel formation algorithm is detailed and tested against a set of SAS images containing a variety of common seabed categories. Textural clues are gathered using a novel DP clustering algorithm that replaces the traditional K-means operator used in [&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":[659,683,717,729],"class_list":["post-1134","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-segmentation","tag-spatial","tag-superpixel","tag-synthetic-aperture-sonar"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1134","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=1134"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1134\/revisions"}],"predecessor-version":[{"id":14623,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1134\/revisions\/14623"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}