Many problems in early computer vision, like image segmentation, image reconstruction, 3D vision or object labeling can be modeled by Markov Random Fields (MRF). General algorithms to optimize a MRF like Simulated Annealing, Belief Propagation or Iterated Conditional Modes are either slow or produce low quality results [Rother 07]. On the other hand, in the last years a discrete subset of MRFs, which contain only so called quadratic pseudo-boolean energy functions, has been shown to be solvable very efficiently by graph cuts [Kolmogorov 04]. Graph cuts can be accelerated by the use of the graphics processing unit (GPU). GPU computing has become popular in a wide range of applications aside from graphics. There are already implementations in the context of image segmentation [Vineet 08], though we did not find any implementation for arbitrary graphs. Unfortunately, the Quadratic Pseudo-Boolean Optimization (QPBO) becomes NP-hard if the energy includes so called supermodular terms. Algorithms have been developed to obtain at least a partial solution in many cases. These algorithms are strictly sequential, though. No implementations we know use the GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"
Many problems in early computer vision, like image segmentation, image reconstruction, 3D vision or object labeling can be modeled by Markov Random Fields (MRF). General algorithms to optimize a MRF like Simulated Annealing, Belief Propagation or Iterated Conditional Modes are either slow or produce low quality results [Rother 07]. On the other hand, in the […]<\/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":[1787,1782,1791,14,512,20,1688,390],"class_list":["post-13464","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-image-reconstruction","tag-nvidia","tag-nvidia-geforce-gtx-450-ti","tag-thesis"],"views":2909,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13464","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=13464"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13464\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13464"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13464"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}