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. 16, 2021

Filed:

May. 13, 2019
Applicant:

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

Inventors:

Brian Price, San Jose, CA (US);

Ning Xu, Mountain View, CA (US);

Naoto Inoue, Kanagawa, JP;

Jimei Yang, Mountain View, CA (US);

Daicho Ito, San Jose, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/20 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06T 7/40 (2017.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06T 11/203 (2013.01); G06K 9/4609 (2013.01); G06K 9/6256 (2013.01); G06T 5/002 (2013.01); G06T 7/40 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30184 (2013.01); G06T 2207/30196 (2013.01);
Abstract

Computing systems and computer-implemented methods can be used for automatically generating a digital line drawing of the contents of a photograph. In various examples, these techniques include use of a neural network, referred to as a generator network, that is trained on a dataset of photographs and human-generated line drawings of the photographs. The training data set teaches the neural network to trace the edges and features of objects in the photographs, as well as which edges or features can be ignored. The output of the generator network is a two-tone digital image, where the background of the image is one tone, and the contents in the input photographs are represented by lines drawn in the second tone. In some examples, a second neural network, referred to as a restorer network, can further process the output of the generator network, and remove visual artifacts and clean up the lines.


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