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:
Apr. 08, 2025

Filed:

Jul. 21, 2022
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Mang Tik Chiu, Champaign, IL (US);

Connelly Barnes, Seattle, WA (US);

Zijun Wei, San Jose, CA (US);

Zhe Lin, Clyde Hill, WA (US);

Yuqian Zhou, Champaign, IL (US);

Xuaner Zhang, Union City, CA (US);

Sohrab Amirghodsi, Seattle, WA (US);

Florian Kainz, San Rafael, CA (US);

Elya Shechtman, Seattle, WA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/045 (2023.01); G06T 3/40 (2024.01); G06T 5/30 (2006.01); G06T 5/77 (2024.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/187 (2017.01); G06T 7/62 (2017.01);
U.S. Cl.
CPC ...
G06N 3/045 (2023.01); G06T 3/40 (2013.01); G06T 5/30 (2013.01); G06T 5/77 (2024.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/187 (2017.01); G06T 7/62 (2017.01); G06T 2207/20084 (2013.01);
Abstract

Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.


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