IP Library Granted Patent US 12,626,348
Granted Patent B2
US 12,626,348 · App. 18/252,599 · Granted May 12, 2026

Analysis apparatus, inspection system, and learning apparatus

Inventor: Takehiko Sashida (Tokyo, JP)
Assignee: Konica Minolta, Inc.
G06T7/0004G06T7/11G06T2207/30156
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Quick Facts
Patent No.
US 12,626,348
App. No.
18/252,599
Granted
May 12, 2026
Kind
B2
Abstract

An analysis apparatus includes: a hardware processor that: acquires image information items of a plurality of images regarding a target that are captured while the target is irradiated with light, extracts, based on the image information items, an image in which an irradiation region where the target is irradiated with the light and an inspection target region of the target have a predetermined relationship, from among the images, and analyzes a state of the inspection target region based on each of the image information items of the extracted image.

Claims (42)

1 . An analysis apparatus comprising:

a hardware processor that:

acquires image information items of images regarding a target that are captured while the target is irradiated with light,

extracts, based on the image information items, an image in which a peripheral portion of an irradiation region of the target overlaps at least a part of an inspection target region of the target, from among the images, wherein

the irradiation region is a region irradiated with the light, and includes:

a central portion that has uniform luminance; and

the peripheral portion that is outside of the central portion and has non-uniform luminance, and

analyzes a state of the inspection target region based on each of the image information items of the extracted image.

2 . The analysis apparatus according to claim 1 , wherein the hardware processor extracts an image in which a peripheral edge of the irradiation region overlaps the inspection target region, from among the images.

3 . The analysis apparatus according to claim 1 , wherein the hardware processor extracts an image in which the inspection target region has non-uniform luminance, from among the images.

4 . The analysis apparatus according to claim 1 , wherein the hardware processor extracts the image based on a difference between a maximum luminance and a minimum luminance of the inspection target region and an average luminance of the inspection target region.

5 . The analysis apparatus according to claim 1 , wherein the hardware processor extracts the image based on at least one of a distribution and a histogram of luminance of the inspection target region.

6 . The analysis apparatus according to claim 1 , wherein the hardware processor acquires the image information items of the images regarding the target that are captured while any one of the target, the irradiation region, and an image capturing position moves.

7 . The analysis apparatus according to claim 6 , wherein

the hardware processor further:

identifies the inspection target region in each of the images based on each of the image information items,

tracks the inspection target region in each of the images based on the identified inspection target region, and

extracts the image based on the tracked inspection target region.

8 . The analysis apparatus according to claim 1 , wherein

the hardware processor further:

extracts a plurality of the images in each of which the peripheral portion overlaps at least a part of the inspection target region, from among the images, and

analyzes the state of the inspection target region based on the image information items of the images.

9 . The analysis apparatus according to claim 1 , wherein the hardware processor analyzes the state of the inspection target region using a learned model.

10 . The analysis apparatus according to claim 9 , wherein the learned model is learned in advance by using training data of a combination of the inspection target region in the extracted image and a ground truth label of the state of the inspection target region.

11 . The analysis apparatus according to claim 1 , wherein the hardware processor analyzes the state of the inspection target region using deep learning.

12 . The analysis apparatus according to claim 1 , wherein

the inspection target region is a candidate region of a defect in the target, and

the hardware processor analyzes a shape of the defect.

13 . The analysis apparatus according to claim 12 , wherein the shape is a recessed shape or a protrusion shape.

14 . An inspection system comprising:

a light source apparatus that irradiates a target with light;

an imaging apparatus that images the target irradiated with the light from the light source apparatus; and

the analysis apparatus according to claim 1 .

15 . A learning apparatus comprising:

a hardware processor that:

acquires image information items of a plurality of images regarding a target that are captured while the target is irradiated with light,

extracts, based on the image information items, an image in which a peripheral portion of an irradiation region of the target overlaps at least a part of an inspection target region of the target, from among the images, wherein

the irradiation region is a region irradiated with the light, and includes:

a central portion that has uniform luminance; and

the peripheral portion that is outside of the central portion and has non-uniform luminance,

analyzes a state of the inspection target region based on each of the image information items of the image extracted using a learned model, and

causes the learned model to perform further learning.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 26, 2023
From: SASHIDA, TAKEHIKO
To: KONICA MINOLTA, INC.
Reel/Frame 064060/0792 →
Priority Claims (1)
JP 2020-198500 · Nov 30, 2020 · national
Continuity (1)
Related Publication 20240005473A1 · Jan 4, 2024
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