IDIOSYNCRASY SENSING SYSTEM AND IDIOSYNCRASY SENSING METHOD

• US 20200134807A1
• Filed: 06/04/2018
• Published: 04/30/2020
• Est. Priority Date: 06/29/2017
• Status: Abandoned Application

• Current Assignee: Sumitomo Chemical Company Limited
• Original Assignee: Sumitomo Chemical Company Limited
• Inventors: OZAKI, Maya, SUZUKI, Takashi
• Application Number:
  US16/621,969
• Publication Number:
  US 20200134807A1
• Time in Patent Office: Days
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/20132 Image cropping
• CPC Class Codes: G06T 2207/30108 Industrial image inspection
• CPC Class Codes: G06T 7/0004 Industrial image inspection

Abstract

In a singular part detection system (1), a singular part (13) having an arbitrary feature is extracted from a captured image (12) of a subject captured by an imaging unit (2) by a singular part extracting unit (3), a singular part image (17) with an arbitrary size is cut out from the captured image (12) by a singular part image cutting unit (4) such that the singular part (13) overlaps the center (C) of the singular part image (17), and a type of the singular part (13) is identified by an identification unit (5) using machine learning with the singular part image (17) as an input.

Claims

1-6. -6. (canceled)

7. A singular part detection system comprising:

an imaging unit that images a subject;

a singular part extracting unit that extracts a singular part having an arbitrary feature from a captured image of the subject captured by the imaging unit;

a singular part image cutting unit that cuts out a singular part image with an arbitrary size from the captured image such that the singular part extracted by the singular part extracting unit overlaps the center of the singular part image; and

an identification unit that identifies a type of the singular part using machine learning with the singular part image cut out by the singular part image cutting unit as an input.

8. The singular part detection system according to claim 7, wherein the singular part extracting unit extracts the singular part having at least one feature selected from the group consisting of a luminance, a color, a size, and a shape from the captured image.

9. The singular part detection system according to claim 7, wherein the identification unit identifies the type of the singular part using a convolutional neural network.

10. The singular part detection system according to claim 8, wherein the identification unit identifies the type of the singular part using a convolutional neural network.

11. A singular part detection method comprising:

an imaging step of imaging a subject;

a singular part extracting step of extracting a singular part having an arbitrary feature from a captured image of the subject captured in the imaging step;

a singular part image cutting step of cutting a singular part image with an arbitrary size from the captured image such that the singular part extracted in the singular part extracting step overlaps the center of the singular part image; and

an identification step of identifying a type of the singular part using machine learning with the singular part image cut out in the singular part image cutting step as an input.

12. The singular part detection method according to claim 11, wherein the singular part extracting step includes extracting the singular part having at least one feature selected from the group consisting of a luminance, a color, a size, and a shape from the captured image.

13. The singular part detection method according to claim 11, wherein the identification step includes identifying the type of the singular part using a convolutional neural network.

14. The singular part detection method according to claim 12, wherein the identification step includes identifying the type of the singular part using a convolutional neural network.