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.
Patent No.:
Date of Patent:
Jan. 04, 2022
Filed:
Apr. 14, 2020
Applicant:
Getac Technology Corporation, Hsinchu County, TW;
Inventor:
Kun-Yu Tsai, Taipei, TW;
Assignee:
GETAC TECHNOLOGY CORPORATION, Hsinchu County, TW;
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/46 (2006.01); G06T 7/586 (2017.01); G06T 7/00 (2017.01); G06K 9/20 (2006.01); G06K 9/32 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); G01N 21/3581 (2014.01); G01N 21/88 (2006.01); G06T 7/40 (2017.01); G06N 3/04 (2006.01); G06T 7/11 (2017.01); G01J 3/28 (2006.01); G01N 21/956 (2006.01); G06T 7/45 (2017.01); G06F 17/16 (2006.01); G06N 3/063 (2006.01); G01N 21/01 (2006.01); G01N 21/952 (2006.01);
U.S. Cl.
CPC ...
G06K 9/4661 (2013.01); G01J 3/2823 (2013.01); G01N 21/01 (2013.01); G01N 21/3581 (2013.01); G01N 21/8851 (2013.01); G01N 21/952 (2013.01); G01N 21/956 (2013.01); G06F 17/16 (2013.01); G06K 9/00201 (2013.01); G06K 9/00208 (2013.01); G06K 9/2018 (2013.01); G06K 9/2054 (2013.01); G06K 9/3208 (2013.01); G06K 9/3233 (2013.01); G06K 9/6257 (2013.01); G06N 3/04 (2013.01); G06N 3/0472 (2013.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06T 7/0004 (2013.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); G06T 7/45 (2017.01); G06T 7/586 (2017.01); G06T 7/97 (2017.01); G01N 2021/8887 (2013.01); G06T 2207/10152 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract
An artificial neural network-based method for detecting a surface pattern of an object includes receiving a plurality of object images, dividing each object image into a plurality of image areas, designating at least one region of interest from the plurality of image areas of each of the object images, and performing deep learning with the at least one region of interest to build a predictive model for identifying a surface pattern of the object.