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:
Oct. 25, 2016

Filed:

Feb. 17, 2016
Applicant:

Adobe Systems Incorporated, San Jose, CA (US);

Inventors:

Huixuan Tang, Toronto, CA;

Scott Cohen, Sunnyvale, CA (US);

Stephen Schiller, Oakland, CA (US);

Brian Price, San Jose, CA (US);

Assignee:

Adobe Systems Incorporated, San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 5/222 (2006.01); H04N 5/232 (2006.01); H04N 13/00 (2006.01); G06T 7/00 (2006.01); G06T 7/20 (2006.01); H04N 5/235 (2006.01); H04N 13/02 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); H04N 17/00 (2006.01); H04N 1/387 (2006.01);
U.S. Cl.
CPC ...
H04N 13/0022 (2013.01); G06K 9/4661 (2013.01); G06K 9/6215 (2013.01); G06T 7/0038 (2013.01); G06T 7/0042 (2013.01); G06T 7/0051 (2013.01); G06T 7/0069 (2013.01); G06T 7/206 (2013.01); H04N 5/2226 (2013.01); H04N 5/2329 (2013.01); H04N 5/2351 (2013.01); H04N 5/2355 (2013.01); H04N 5/23212 (2013.01); H04N 5/23222 (2013.01); H04N 13/0271 (2013.01); H04N 17/002 (2013.01); G06K 2009/4666 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/10148 (2013.01); G06T 2207/20048 (2013.01); G06T 2207/20144 (2013.01); H04N 1/387 (2013.01); H04N 2013/0081 (2013.01); H04N 2213/003 (2013.01);
Abstract

Depth maps are generated from two or more of images captured with a conventional digital camera from the same viewpoint using different configuration settings, which may be arbitrarily selected for each image. The configuration settings may include aperture and focus settings and/or other configuration settings capable of introducing blur into an image. The depth of a selected image patch is evaluated over a set of discrete depth hypotheses using a depth likelihood function modeled to analyze corresponding images patches convolved with blur kernels using a flat prior in the frequency domain. In this way, the depth likelihood function may be evaluated without first reconstructing an all-in-focus image. Blur kernels used in the depth likelihood function and are identified from a mapping of depths and configuration settings to the blur kernels. This mapping is determined from calibration data for the digital camera used to capture the two or more images.


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