US 12,444,034 B2
Teacher data generation method, trained learning model, and system
Takeru Ohya, Tokyo (JP); Kazuya Hizume, Kanagawa (JP); Yuta Okabe, Kanagawa (JP); and Takayuki Hashimoto, Kanagawa (JP)
Assigned to Canon Kabushiki Kaisha, Tokyo (JP)
Filed by CANON KABUSHIKI KAISHA, Tokyo (JP)
Filed on Apr. 5, 2022, as Appl. No. 17/713,956.
Application 17/713,956 is a continuation of application No. PCT/JP2020/036879, filed on Sep. 29, 2020.
Claims priority of application No. 2019-185542 (JP), filed on Oct. 8, 2019.
Prior Publication US 2022/0284567 A1, Sep. 8, 2022
Int. Cl. G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 7/12 (2017.01)
CPC G06T 7/0004 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/12 (2017.01); G06T 2207/20081 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A system comprising:
an image capturing apparatus;
one or more processors; and
one or more memories storing instructions and a trained learning model configured to receive image data obtained by capturing an image by the image capturing apparatus,
wherein the trained learning model was created
by inputting difference information indicating a difference between first image data obtained by capturing an image of a first workpiece and second image data different from the first image data, wherein the first image data and the second image data were obtained by capturing one or more images of a first workpiece model, wherein the first workpiece is an instance of the first workpiece model, and
by inputting difference information indicating a difference between third image data obtained by capturing an image of a second workpiece and fourth image data different from the third image data, wherein the third image data and the fourth image data were obtained by capturing one or more images of a second workpiece model that is different from the first workpiece model, wherein the second workpiece is an instance of the second workpiece model,
wherein executing the instructions causes the one or more processors and the one or more memories to input difference information indicating a difference between fifth image data obtained by capturing an image of a third workpiece by the image capturing apparatus and sixth image data different from the fifth image data to the trained learning model and make a determination on the third workpiece,
wherein the difference information indicating the difference between the fifth image data and the sixth image data to be input to the learning model indicates information subjected to predetermined processing, and
wherein in a case where an absolute value of a difference value in an area including the difference indicated by the difference information between the fifth image data and the sixth image data exceeds a predetermined threshold, the predetermined processing is processing of correcting the area to an area including no difference.