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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.

Patent No.:

US 11978249 B1

PDFFull Text
Date of Patent:
May. 07, 2024

Filed:

Aug. 24, 2019

Automated mapping of features of interest

Applicant:

Fugro N.v., Leidschendam, NL;

Inventors:

Christine Devine, Perth, AU;

William Haneberg, Lexington, KY (US);

Assignee:

Fugro N.V., Leidschendam, NL;

Attorney:

Polsinelli PC

Primary Examiner:

David F Dunphy

Int. Cl.
CPC ...
G06K 9/62 (2022.01); G01S 17/88 (2006.01); G06F 18/2113 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/25 (2023.01); G06K 9/00 (2022.01); G06K 9/34 (2006.01); G06N 20/00 (2019.01); G06V 10/26 (2022.01); G06V 20/13 (2022.01); G01S 7/481 (2006.01); G06V 20/10 (2022.01);
U.S. Cl.
CPC ...
G06V 20/13 (2022.01); G01S 17/88 (2013.01); G06F 18/2113 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/253 (2023.01); G06N 20/00 (2019.01); G06V 10/26 (2022.01); G06V 10/273 (2022.01); G01S 7/4814 (2013.01); G06V 20/194 (2022.01);
Abstract

A computer-implemented method for identifying features of interest in a data image. The method includes identifying data variations in a data image or set of data images, each data image comprising rendered data, identifying one or more features of interest in the data image or set of data images based on the identified data variations, identifying a feature of interest genus corresponding to each identified feature of interest, reclassifying the rendered data based on each of the identified features of interest genuses so as to eliminate background data in the rendered data thereby producing an eliminated background dataset, and generating a feature of interest map for each identified feature of interest genus. A machine learning method, including a training phase, for automatically identifying features of interest in a data image is further provided.

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