{"id":161784,"date":"2011-10-01T00:00:00","date_gmt":"2011-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/efficient-regression-of-general-activity-human-poses-from-depth-images\/"},"modified":"2018-10-16T19:58:44","modified_gmt":"2018-10-17T02:58:44","slug":"efficient-regression-of-general-activity-human-poses-from-depth-images","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/efficient-regression-of-general-activity-human-poses-from-depth-images\/","title":{"rendered":"Efficient Regression of General-Activity Human Poses from Depth Images"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints.<br \/>\nExperimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our 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Forests","post_name":"decision-forests","post_type":"msr-project","post_date":"2012-07-25 01:35:22","post_modified":"2017-06-06 12:09:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/decision-forests\/","post_excerpt":"Decision Forests for Computer Vision and Medical Image Analysis A. Criminisi and J. Shotton Springer 2013, XIX, 368 p. 143 illus., 136 in color. ISBN 978-1-4471-4929-3 \u00a0","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171004"}]}},{"ID":170869,"post_title":"Touchless Interaction in Medical Imaging","post_name":"touchless-interaction-in-medical-imaging","post_type":"msr-project","post_date":"2011-11-16 03:23:17","post_modified":"2022-09-07 10:57:24","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/touchless-interaction-in-medical-imaging\/","post_excerpt":"This project explores the use of new touchless technology in medical practice. With advances in medical imaging over the years, surgical procedures have become increasingly reliant on a range of digital imaging systems for navigation, reference, diagnosis and documentation. The need to interact with images in these surgical settings offers particular challenges arising from the need to maintain boundaries between sterile and non-sterile aspects of the surgical environment and practices. Traditional input devices such as&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170869"}]}},{"ID":170652,"post_title":"Human Pose Estimation for Kinect","post_name":"human-pose-estimation-for-kinect","post_type":"msr-project","post_date":"2011-01-25 09:18:30","post_modified":"2022-09-07 10:53:34","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/human-pose-estimation-for-kinect\/","post_excerpt":"Kinect for Xbox 360 and Windows makes you the controller by fusing 3D imaging hardware with markerless human-motion capture software. Our group investigates such software. Mixing computer vision, graphics, and machine learning techniques, we look at how to build algorithms that can learn to recognize human poses quickly and reliably. Images Traditional RGB image Image from new depth sensing camera Body parts inferred by our recognition algorithm 3D body part position proposals Related Press Binary&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170652"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/161784","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/161784\/revisions"}],"predecessor-version":[{"id":516488,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/161784\/revisions\/516488"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=161784"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=161784"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=161784"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=161784"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=161784"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=161784"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=161784"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=161784"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=161784"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=161784"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=161784"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=161784"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=161784"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}