{"id":6427,"date":"2011-11-29T17:12:32","date_gmt":"2011-11-29T15:12:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=6427"},"modified":"2011-11-29T17:12:32","modified_gmt":"2011-11-29T15:12:32","slug":"gpgpu-volume-classification-using-simpleopencl","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6427","title":{"rendered":"GPGPU Volume Classification using SimpleOpenCL"},"content":{"rendered":"<p>In volume visualization, the definition of the regions of interest is inherently an iterative trialand-error process finding out the best parameters to classify and render the final image. In this work, we present a general framework for training multi-class classifiers using Error-Correcting Output Codes. Moreover, we propose a GPGPU parallelization system using SimpleOpenCL, an OpenSource library we created to make easier the use of OpenCL. Results show accurate classification results as well as good speed ups.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In volume visualization, the definition of the regions of interest is inherently an iterative trialand-error process finding out the best parameters to classify and render the final image. In this work, we present a general framework for training multi-class classifiers using Error-Correcting Output Codes. Moreover, we propose a GPGPU parallelization system using SimpleOpenCL, an OpenSource [&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":[11,90,3],"tags":[1782,20,953,1793,176,134],"class_list":["post-6427","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-nvidia","tag-nvidia-geforce-gtx-470","tag-opencl","tag-package","tag-visualization"],"views":1824,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6427","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=6427"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6427\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6427"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6427"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6427"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}