The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 02, 2011

Filed:

May. 14, 2010
Applicants:

Andrew Blake, Stapleford, GB;

Antonio Criminisi, Lower Cambourne, GB;

Geoffrey Cross, Oxford, GB;

Vladimir Kolmogorov, Gough Way, GB;

Carsten Curt Eckard Rother, Cambridge, GB;

Inventors:

Andrew Blake, Stapleford, GB;

Antonio Criminisi, Lower Cambourne, GB;

Geoffrey Cross, Oxford, GB;

Vladimir Kolmogorov, Gough Way, GB;

Carsten Curt Eckard Rother, Cambridge, GB;

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/34 (2006.01);
U.S. Cl.
CPC ...
Abstract

Real-time segmentation of foreground from background layers in binocular video sequences may be provided by a segmentation process which may be based on one or more factors including likelihoods for stereo-matching, color, and optionally contrast, which may be fused to infer foreground and/or background layers accurately and efficiently. In one example, the stereo image may be segmented into foreground, background, and/or occluded regions using stereo disparities. The stereo-match likelihood may be fused with a contrast sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as dynamic programming or graph cut. In a second example, the stereo-match likelihood may be marginalized over foreground and background hypotheses, and fused with a contrast-sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as a binary graph cut.


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