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:
Jul. 11, 2023

Filed:

Jan. 28, 2022
Applicant:

Pictometry International Corp., Rochester, NY (US);

Inventors:

Stephen Ng, Rochester, NY (US);

David R. Nilosek, Rochester, NY (US);

Phillip Salvaggio, Rochester, NY (US);

Shadrian Strong, Bellevue, WA (US);

Assignee:

Pictometry International Corp., Rochester, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/33 (2017.01); G06T 7/13 (2017.01); G06T 7/70 (2017.01); G06N 3/088 (2023.01); G06V 20/10 (2022.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 7/344 (2017.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 7/13 (2017.01); G06T 7/70 (2017.01); G06V 10/44 (2022.01); G06V 10/7553 (2022.01); G06V 10/82 (2022.01); G06V 20/176 (2022.01); G06V 20/182 (2022.01); G06V 20/653 (2022.01); G06T 2207/10032 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.


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