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:
May. 13, 2025

Filed:

Jul. 21, 2022
Applicant:

Honeywell International Inc., Charlotte, NC (US);

Inventors:

Andrey Shtylenko, McKinney, TX (US);

Andy Walker Brown, Richardson, TX (US);

Adam Dewey McBrady, Dallas, TX (US);

Ratna Dinakar Tumuluri, Alpharetta, GA (US);

Assignee:

Honeywell International Inc., Charlotte, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/73 (2024.01); G06T 5/50 (2006.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06T 5/73 (2024.01); G06T 5/50 (2013.01); G06V 10/774 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/10148 (2013.01);
Abstract

Embodiments of the present disclosure provide for improved generation and outputting of object identification data indicating object classifications for object representations. Such objects representations may correspond to depictions of objects in images captured using digital holographic microscopy. Some embodiments generate object identification data by comparing object representations in focused image(s) with specially configured annotated focused images, for example using a specially trained neural network or other machine learning model trained based on such annotated focused images. The annotated focused images are generated including a plurality of channels, each associated with a different grayscale focused image at a different target focal length of a range of target focal lengths. In this regard, model(s), algorithm(s), and/or other specially configured implementations may learn the spatial features of particular object representations and associated object identification data. The trained models may be used to perform accurate comparisons with the annotated focused images.


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