{"id":2466,"date":"2011-01-13T13:32:56","date_gmt":"2011-01-13T13:32:56","guid":{"rendered":"http:\/\/hgpu.org\/?p=2466"},"modified":"2011-01-13T13:32:56","modified_gmt":"2011-01-13T13:32:56","slug":"low-cost-high-speed-computer-vision-using-nvidias-cuda-architecture","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2466","title":{"rendered":"Low-cost, high-speed computer vision using NVIDIA&#8217;s CUDA architecture"},"content":{"rendered":"<p>In this paper, we introduce real time image processing techniques using modern programmable Graphic Processing Units (GPU). GPUs are SIMD (Single Instruction, Multiple Data) device that is inherently data-parallel. By utilizing NVIDIA&#8217;s new GPU programming framework, &#8220;Compute Unified Device Architecture&#8221; (CUDA) as a computational resource, we realize significant acceleration in image processing algorithm computations. We show that a range of computer vision algorithms map readily to CUDA with significant performance gains. Specifically, we demonstrate the efficiency of our approach by a parallelization and optimization of Canny&#8217;s edge detection algorithm, and applying it to a computation and data-intensive video motion tracking algorithm known as &#8220;Vector Coherence Mapping&#8221; (VCM). Our results show the promise of using such common low-cost processors for intensive computer vision tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we introduce real time image processing techniques using modern programmable Graphic Processing Units (GPU). GPUs are SIMD (Single Instruction, Multiple Data) device that is inherently data-parallel. By utilizing NVIDIA&#8217;s new GPU programming framework, &#8220;Compute Unified Device Architecture&#8221; (CUDA) as a computational resource, we realize significant acceleration in image processing algorithm computations. We [&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":[11,73,89,3],"tags":[1782,1791,14,20,672,357,469,402],"class_list":["post-2466","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-nvidia-geforce-8600-m-gt","tag-nvidia-geforce-8800-gts","tag-pattern-recognition","tag-video-tracking"],"views":2034,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2466","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=2466"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2466\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2466"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2466"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2466"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}