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:
Feb. 02, 2016

Filed:

Aug. 18, 2011
Applicant:

LI Hong, San Diego, CA (US);

Inventor:

Li Hong, San Diego, CA (US);

Assignee:

NIKON CORPORATION, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 5/232 (2006.01); H04N 5/217 (2011.01); G06K 9/66 (2006.01); G06K 9/62 (2006.01); G06K 9/46 (2006.01); G06K 9/52 (2006.01); G06K 9/40 (2006.01); G06K 9/03 (2006.01); G06T 7/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/66 (2013.01); G06K 9/036 (2013.01); G06K 9/40 (2013.01); G06K 9/46 (2013.01); G06K 9/522 (2013.01); G06K 9/527 (2013.01); G06K 9/6267 (2013.01); G06T 7/0002 (2013.01); H04N 5/217 (2013.01); H04N 5/23229 (2013.01); H04N 5/23254 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30168 (2013.01);
Abstract

A method for predicting whether a test image () is sharp or blurred includes the steps of: training a sharpness classifier () to discriminate between sharp and blurred images, the sharpness classifier () being trained based on a set of training sharpness features () computed from a plurality of training images (), the set of training sharpness features () for each training image () being computed by (i) resizing each training image () by a first resizing factor; (ii) identifying texture regions () in the resized training image; and (iii) computing the set of sharpness features in the training image () from the identified texture regions; and applying the trained sharpness classifier () to the test image () to determine if the test image () is sharp or blurred based on a set of test sharpness features () computed from the test image (), the set of test sharpness features () for each test image () being computed by (i) resizing the test image () by a second resizing factor that is different than the first resizing factor; (ii) identifying texture regions () in the resized test image; and (iii) computing the set of sharpness features in the test image () from the identified texture regions.


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