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:
Mar. 31, 2020

Filed:

Jun. 13, 2017
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Yannick Hold-Geoffroy, Quebec, CA;

Sunil S. Hadap, Dublin, CA (US);

Kalyan Krishna Sunkavalli, San Jose, CA (US);

Emiliano Gambaretto, San Francisco, CA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/00 (2006.01); H04N 5/232 (2006.01); G06K 9/46 (2006.01); G06T 15/50 (2011.01); G06N 3/08 (2006.01); H04N 5/235 (2006.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
H04N 5/23245 (2013.01); G06K 9/00624 (2013.01); G06K 9/00664 (2013.01); G06K 9/4628 (2013.01); G06K 9/4661 (2013.01); G06K 9/6274 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 5/009 (2013.01); G06T 15/506 (2013.01); H04N 5/2351 (2013.01); G06K 9/00671 (2013.01); G06N 3/0481 (2013.01); G06T 2207/20208 (2013.01); H04N 5/23222 (2013.01);
Abstract

The present disclosure is directed toward systems and methods for predicting lighting conditions. In particular, the systems and methods described herein analyze a single low-dynamic range digital image to estimate a set of high-dynamic range lighting conditions associated with the single low-dynamic range lighting digital image. Additionally, the systems and methods described herein train a convolutional neural network to extrapolate lighting conditions from a digital image. The systems and methods also augment low-dynamic range information from the single low-dynamic range digital image by using a sky model algorithm to predict high-dynamic range lighting conditions.


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