{"id":10528,"date":"2013-09-14T23:27:09","date_gmt":"2013-09-14T20:27:09","guid":{"rendered":"http:\/\/hgpu.org\/?p=10528"},"modified":"2013-09-14T23:27:09","modified_gmt":"2013-09-14T20:27:09","slug":"a-gpu-based-affine-and-scale-invariant-feature-transform-algorithm","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10528","title":{"rendered":"A GPU-based Affine and Scale Invariant Feature Transform Algorithm"},"content":{"rendered":"<p>Affine invariance is one of the main performances of a good feature extraction algorithm. SIFT is a kind of scale-invariant feature extraction algorithm, but it is not affine invariant. In order to improve SIFT algorithm&#8217;s affine invariance. Affine and Scale Invariant Feature Transform (ASIFT) algorithm takes affine Model into SIFT. However, serial ASIFT algorithm&#8217;s computing rate is low and it is not suitable for interactive applications. By analyzing the algorithm&#8217;s principle of ASIFT, this paper proposed multicore parallel ASIFT algorithm and achieved ASIFT algorithm&#8217;s parallelization by CUDA architecture. Finally, the experiment shown that multicore parallel ASIFT algorithm greatly improves computing speed in the same precision as mononuclear serial ASIFT algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Affine invariance is one of the main performances of a good feature extraction algorithm. SIFT is a kind of scale-invariant feature extraction algorithm, but it is not affine invariant. In order to improve SIFT algorithm&#8217;s affine invariance. Affine and Scale Invariant Feature Transform (ASIFT) algorithm takes affine Model into SIFT. However, serial ASIFT algorithm&#8217;s computing [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,73,89,3],"tags":[1787,1782,1791,14,205,20,1089],"class_list":["post-10528","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-interactive-application","tag-nvidia","tag-nvidia-geforce-gtx-560-ti"],"views":3386,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10528","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=10528"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10528\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10528"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10528"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10528"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}