{"id":6389,"date":"2011-11-25T21:36:44","date_gmt":"2011-11-25T19:36:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=6389"},"modified":"2011-11-25T21:36:44","modified_gmt":"2011-11-25T19:36:44","slug":"fast-hough-transform-on-gpus-exploration-of-algorithm-trade-offs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6389","title":{"rendered":"Fast hough transform on GPUs: exploration of algorithm trade-offs"},"content":{"rendered":"<p>The Hough transform is a commonly used algorithm to detect lines and other features in images. It is robust to noise and occlusion, but has a large computational cost. This paper introduces two new implementations of the Hough transform for lines on a GPU. One focuses on minimizing processing time, while the other has an input-data independent processing time. Our results show that optimizing the GPU code for speed can achieve a speed-up over naive GPU code of about 10x. The implementation which focuses on processing speed is the faster one for most images, but the implementation which achieves a constant processing time is quicker for about 20% of the images.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Hough transform is a commonly used algorithm to detect lines and other features in images. It is robust to noise and occlusion, but has a large computational cost. This paper introduces two new implementations of the Hough transform for lines on a GPU. One focuses on minimizing processing time, while the other has an [&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":[36,11,73,89,3],"tags":[1787,1782,1791,14,20,953],"class_list":["post-6389","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-470"],"views":2821,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6389","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=6389"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6389\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}