{"id":2989,"date":"2011-02-26T21:50:29","date_gmt":"2011-02-26T21:50:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=2989"},"modified":"2011-02-26T21:50:29","modified_gmt":"2011-02-26T21:50:29","slug":"gpu-based-foreground-background-segmentation-using-an-extended-colinearity-criterion","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2989","title":{"rendered":"GPU-Based Foreground-Background Segmentation Using an Extended Colinearity Criterion"},"content":{"rendered":"<p>We present a GPU-based foreground-background segmentation that processes image sequences in less than 4ms per frame. Change detection wrt. the background is based on a color similarity test in a small pixel neighbourhood, and is integrated into a Bayesian estimation framework. An iterative MRF-based model is applied, exploiting parallelism on modern graphics hardware. Resulting segmentation exhibits compactness and smoothness in foreground areas as well as for inter-frame temporal contiguity. Further refinements extend the colinearity criterion with compensation for dark foreground and background areas and thus improving overall performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a GPU-based foreground-background segmentation that processes image sequences in less than 4ms per frame. Change detection wrt. the background is based on a color similarity test in a small pixel neighbourhood, and is integrated into a Bayesian estimation framework. An iterative MRF-based model is applied, exploiting parallelism on modern graphics hardware. Resulting segmentation [&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":[73,33,3],"tags":[444,1791,1786,20,301,297],"class_list":["post-2989","post","type-post","status-publish","format-standard","hentry","category-computer-vision","category-image-processing","category-paper","tag-cg","tag-computer-vision","tag-image-processing","tag-nvidia","tag-nvidia-geforce-6800-gt","tag-real-time-graphics"],"views":2049,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2989","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=2989"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2989\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2989"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2989"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2989"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}