{"id":147943,"date":"2005-06-01T00:00:00","date_gmt":"2005-06-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/estimating-disparity-and-occlusions-in-stereo-video-sequences\/"},"modified":"2018-10-16T20:27:29","modified_gmt":"2018-10-17T03:27:29","slug":"estimating-disparity-and-occlusions-in-stereo-video-sequences","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/estimating-disparity-and-occlusions-in-stereo-video-sequences\/","title":{"rendered":"Estimating Disparity and Occlusions in Stereo Video Sequences"},"content":{"rendered":"
\n

We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly models half-occlusions \u2014 scene points visible in one camera but not the other \u2014 and (ii) enforces the so-called “monotonicity constraint” on the boundary of half-occluded regions. The algorithm is also able to exploit temporal coherence more appropriately than many previous approaches to temporal stereo, by employing additional states in the Markov random field. These additional states permit rudimentary motion estimation to be performed as part of the belief propagation, thus improving the quality of temporal inference. Parameters of the algorithm are learned from the ground truth disparities of a real stereo sequence. Qualitative results are shown on real sequences, including comparisons with competing approaches, and the performance of the algorithm is assessed quantitatively using the ground truth data.<\/p>\n<\/div>\n

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We propose an algorithm for estimating disparity and occlusion in stereo video sequences. The algorithm defines a prior on sequences of disparity maps using a 3D Markov random field, and approximately computes the MAP estimate for the disparity sequence using loopy belief propagation. In contrast to previous work on temporal stereo, the algorithm (i) correctly […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2004 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"IEEE Conference on Computer Vision and Pattern Recognition 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