{"id":12281,"date":"2014-06-15T09:52:12","date_gmt":"2014-06-15T06:52:12","guid":{"rendered":"http:\/\/hgpu.org\/?p=12281"},"modified":"2014-06-15T09:52:12","modified_gmt":"2014-06-15T06:52:12","slug":"a-parallel-algorithm-of-pca-sift-based-on-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12281","title":{"rendered":"A Parallel Algorithm of PCA-SIFT Based on CUDA"},"content":{"rendered":"<p>PCA-SIFT is an algorithm to extract invariant features from images, it has been widely applied to many application fields including image processing, computer vision and pattern recognition. However, the execution of PCA-SIFT is time-consuming. A parallel algorithm of PCA-SIFT based on Compute Unified Device Architecture (CUDA) is proposed in this paper, in which each step of PCA-SIFT is implemented in parallel as much as possible. The experimental results show that the speedup of parallel algorithm is 3-5 compared to the original PCA-SIFT while maintaining the same descriptors.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PCA-SIFT is an algorithm to extract invariant features from images, it has been widely applied to many application fields including image processing, computer vision and pattern recognition. However, the execution of PCA-SIFT is time-consuming. A parallel algorithm of PCA-SIFT based on Compute Unified Device Architecture (CUDA) is proposed in this paper, in which each step [&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":[11,73,89,33,3],"tags":[1782,1791,14,1786,20,570,1439,469,220],"class_list":["post-12281","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-image-processing","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gt-240","tag-opencv","tag-pattern-recognition","tag-sift"],"views":3325,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12281","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=12281"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12281\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12281"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12281"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12281"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}