{"id":3874,"date":"2011-05-11T14:07:50","date_gmt":"2011-05-11T14:07:50","guid":{"rendered":"http:\/\/hgpu.org\/?p=3874"},"modified":"2011-05-11T14:07:50","modified_gmt":"2011-05-11T14:07:50","slug":"fast-gpu-implementation-of-large-scale-dictionary-and-sparse-representation-based-vision-problems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3874","title":{"rendered":"Fast GPU implementation of large scale dictionary and sparse representation based vision problems"},"content":{"rendered":"<p>Recently, Computer Vision problems like Face Recognition and Super-Resolution solved using sparse representation based methods with large dictionaries have shown state-of-the-art results. However such methods are computationally prohibitive for typical CPUs, especially for a large dictionary size. We present fast implementation of these methods by exploiting the massively parallel processing capabilities of a GPU within a CUDA framework, owing to its easy off-the-shelf availability and programmer friendliness. We provide details of system level design, memory management and implementation strategies. Further, we integrate the solution to the preferred scientific computational platform &#8211; MATLAB.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recently, Computer Vision problems like Face Recognition and Super-Resolution solved using sparse representation based methods with large dictionaries have shown state-of-the-art results. However such methods are computationally prohibitive for typical CPUs, especially for a large dictionary size. We present fast implementation of these methods by exploiting the massively parallel processing capabilities of a GPU within [&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":[1782,1791,14,901,20,554],"class_list":["post-3874","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-image-recognition","tag-nvidia","tag-nvidia-geforce-9800-gt"],"views":1854,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3874","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=3874"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3874\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3874"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3874"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3874"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}