Patent · US Active

Utilizing deep learning for automatic digital image segmentation and stylization

US9773196B2 · kind B2 · utility

24Cited by

0References

20Claims

0Family size

Assignee

Adobe Systems Incorporated · US

Inventors

Ian Sachs · San Francisco, US
Xiaoyong Shen · Lo Wu, CN
Sylvain Paris · Brookline, US
Aaron Hertzmann · San Francisco, US
Elya Shechtman · Seattle, US
Brian Price · San Jose, US

Key dates

Filing date	Jan 25, 2016
Grant date	Sep 26, 2017
Priority date	—
Expiry date	Jan 25, 2036

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30201
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Systems and methods are disclosed for segregating target individuals represented in a probe digital image from background pixels in the probe digital image. In particular, in one or more embodiments, the disclosed systems and methods train a neural network based on two or more of training position channels, training shape input channels, training color channels, or training object data. Moreover, in one or more embodiments, the disclosed systems and methods utilize the trained neural network to select a target individual in a probe digital image. Specifically, in one or more embodiments, the disclosed systems and methods generate position channels, training shape input channels, and color channels corresponding the probe digital image, and utilize the generated channels in conjunction with the trained neural network to select the target individual.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.