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.
Patent No.:
Date of Patent:
Mar. 19, 2013
Filed:
Jan. 31, 2011
Richard Newcombe, Nuneaton, GB;
Shahram Izadi, Cambridge, GB;
Otmar Hilliges, Cambridge, GB;
David Kim, Cambridge, GB;
David Molyneaux, Oldham, GB;
Jamie Daniel Joseph Shotton, Cambridge, GB;
Pushmeet Kohli, Cambridge, GB;
Andrew Fitzgibbon, Cambridge, GB;
Stephen Edward Hodges, Cambridge, GB;
David Alexander Butler, Cambridge, GB;
Richard Newcombe, Nuneaton, GB;
Shahram Izadi, Cambridge, GB;
Otmar Hilliges, Cambridge, GB;
David Kim, Cambridge, GB;
David Molyneaux, Oldham, GB;
Jamie Daniel Joseph Shotton, Cambridge, GB;
Pushmeet Kohli, Cambridge, GB;
Andrew Fitzgibbon, Cambridge, GB;
Stephen Edward Hodges, Cambridge, GB;
David Alexander Butler, Cambridge, GB;
Microsoft Corporation, Redmond, WA (US);
Abstract
Moving object segmentation using depth images is described. In an example, a moving object is segmented from the background of a depth image of a scene received from a mobile depth camera. A previous depth image of the scene is retrieved, and compared to the current depth image using an iterative closest point algorithm. The iterative closest point algorithm includes a determination of a set of points that correspond between the current depth image and the previous depth image. During the determination of the set of points, one or more outlying points are detected that do not correspond between the two depth images, and the image elements at these outlying points are labeled as belonging to the moving object. In examples, the iterative closest point algorithm is executed as part of an algorithm for tracking the mobile depth camera, and hence the segmentation does not add substantial additional computational complexity.