{"id":7980,"date":"2012-07-28T20:51:17","date_gmt":"2012-07-28T17:51:17","guid":{"rendered":"http:\/\/hgpu.org\/?p=7980"},"modified":"2012-07-28T20:51:17","modified_gmt":"2012-07-28T17:51:17","slug":"speeding-up-lip-canny-with-cuda-programming","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7980","title":{"rendered":"Speeding up LIP-Canny with CUDA programming"},"content":{"rendered":"<p>The LIP-Canny algorithm outperforms traditional Canny edge detection in terms of edge detection under varying illumination. This method is based on a robust mathematical model (LIP paradigm), which is closer to the human vision system. However, this model requires more computations and more complex operations than the traditional paradigm. Non-parallel implementations of LIP-Canny do not fit Real-Time requirements because of the large amount of operations required. NVIDIA CUDA is a platform which enables the parallelization of this algorithm, obtaining very high performance. In this work, a comparison results between the non-parallel implementation (written in C\/C++) and the NVIDIA CUDA one.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The LIP-Canny algorithm outperforms traditional Canny edge detection in terms of edge detection under varying illumination. This method is based on a robust mathematical model (LIP paradigm), which is closer to the human vision system. However, this model requires more computations and more complex operations than the traditional paradigm. Non-parallel implementations of LIP-Canny do not [&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":[36,11,89,3],"tags":[1787,1782,14,20,1148],"class_list":["post-7980","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-9200-m-gs"],"views":2535,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7980","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=7980"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7980\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7980"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7980"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}