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. 21, 2023

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:
Int. Cl.
CPC ...
G01N 21/88 (2006.01); G06T 7/586 (2017.01); G06T 7/00 (2017.01); G06N 3/08 (2023.01); G01N 21/3581 (2014.01); G06T 7/40 (2017.01); G06N 3/04 (2023.01); 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 (2023.01); G01N 21/01 (2006.01); G01N 21/952 (2006.01); G06V 10/22 (2022.01); G06V 20/64 (2022.01); G06F 18/214 (2023.01); G06N 3/047 (2023.01); G06V 10/145 (2022.01);
U.S. Cl.
CPC ...
G01N 21/8806 (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); G06F 18/2148 (2023.01); G06N 3/04 (2013.01); G06N 3/047 (2023.01); 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); G06V 10/145 (2022.01); G06V 10/22 (2022.01); G06V 20/64 (2022.01); G06V 20/647 (2022.01); 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 selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.


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