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.:
Date of Patent:
Jun. 21, 2022
Filed:
Oct. 16, 2020
Applicant:
Carnegie Mellon University, Pittsburgh, PA (US);
Inventors:
Daniel Clymer, Pittsburgh, PA (US);
Jonathan Cagan, Pittsburgh, PA (US);
Philip LeDuc, Pittsburgh, PA (US);
Assignee:
CARNEGIE MELLON UNIVERSITY, Pittsburgh, PA (US);
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06K 9/62 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06K 9/6247 (2013.01); G06K 9/6257 (2013.01); G06K 9/6268 (2013.01); G06K 9/6277 (2013.01); G06K 9/6298 (2013.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30101 (2013.01); G06V 2201/031 (2022.01);
Abstract
A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.