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:
Jan. 08, 2008

Filed:

Nov. 07, 2003
Applicants:

Gaile Gordon, Palo Alto, CA (US);

Trevor Darrell, Boston, MA (US);

Michael Harville, Palo Alto, CA (US);

John Woodfill, San Francisco, CA (US);

Inventors:

Gaile Gordon, Palo Alto, CA (US);

Trevor Darrell, Boston, MA (US);

Michael Harville, Palo Alto, CA (US);

John Woodfill, San Francisco, CA (US);

Assignee:

Vulcan Patents LLC, Seattle, CA (US);

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

Segmentation of background and foreground objects in an image is based upon the joint use of range and color data. Range-based data is largely independent of color image data, and hence not adversely affected by the limitations associated with color-based segmentation, such as shadows and similarly colored objects. Furthermore, color segmentation is complementary to range measurement in those cases where reliable range data cannot be obtained. These complementary sets of data are used to provide a multidimensional background estimation. The segmentation of a foreground object in a given frame of an image sequence is carried out by comparing the image frames with background statistics relating to range and normalized color, using the sets of statistics in a complementary manner. A background model is determined by estimating using a multidimensional histogram, recording pixel values, configuring the pixel values into a cluster, and selecting a largest cluster as representing the background model.


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