{"id":8340,"date":"2012-10-09T17:15:24","date_gmt":"2012-10-09T14:15:24","guid":{"rendered":"http:\/\/hgpu.org\/?p=8340"},"modified":"2012-10-09T17:20:10","modified_gmt":"2012-10-09T14:20:10","slug":"accelerating-mean-shift-segmentation-algorithm-on-hybrid-cpugpu-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8340","title":{"rendered":"Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU\/GPU Platforms"},"content":{"rendered":"<p>Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clustering-based segmentation, and is favored in many cases because it is non-parametric. However, mean shift is very computationally intensive compared with other simple methods such as k-means. In this work, we present a hybrid design of mean shift algorithm on a computer platform consisting of both CPUs and GPUs. By taking advantages of the massive parallelism and the advanced memory hierarchy on Nvidia&#8217;s Fermi GPU, the hybrid design achieves a 20x speedup compared with the pure CPU implementation when dealing with images bigger than 1024&#215;1024 pixels.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clustering-based segmentation, and is favored in many cases because it is non-parametric. However, mean shift is very computationally intensive compared with other simple methods such as k-means. In this work, we present a hybrid design [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,33,3],"tags":[1787,468,14,1786,20,1226],"class_list":["post-8340","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-clustering","tag-cuda","tag-image-processing","tag-nvidia","tag-tesla-c2075"],"views":2518,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8340","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8340"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8340\/revisions"}],"predecessor-version":[{"id":8341,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8340\/revisions\/8341"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8340"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8340"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8340"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}