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:
Dec. 23, 2025

Filed:

Jun. 09, 2023
Applicants:

Young-jin Cha, Winnipeg, CA;

Rahmat Ali, Winnipeg, CA;

Inventors:

Young-Jin Cha, Winnipeg, CA;

Rahmat Ali, Winnipeg, CA;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06V 10/77 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06T 7/0002 (2013.01); G06V 10/7715 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/10048 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30184 (2013.01);
Abstract

A computer-implemented method for analyzing a thermographic image to detect an article of interest (AOI) comprises processing the image using a machine learning algorithm configured to detect the AOI and comprising a convolutional neural network (CNN); and displaying the image with location of the AOI being indicated if determined to be present. The CNN features a series pair of convolution modules configured to receive the image and form a reduced size feature map; an in-depth module thereafter and configured to learn correlations and contextual features of the image; and a superficial module after a first of the series convolution module pair and configured to extract features relevant to the AOI. Also, a computer-implemented method for generating synthetic training data based on authentic training data comprises a first neural network configured to generate the synthetic data and a second neural network configured to compare it to the authentic data to determine closeness.


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