Adobe, Inc.

Huawei Technologies Co., Limited

Xiaoyong Shen

Elya Shechtman

Brian Lynn Price

Sylvain Philippe Paris

Aaron Phillip Hertzmann

Ian D Sachs

Jiaya Jia

Kwok Wai Hung

An image alignment method and apparatus, where the method and apparatus include obtaining image information of two to-be-aligned images, determining, using a cross-correlation measurement model, first coordinate offset according to the image information of the two images, where the first coordinate offset are used to indicate position deviations of to-be-aligned pixels between the two images in the coordinate system, and aligning the two images according to coordinates of pixels in the first image in the coordinate system and the first coordinate offset.

Image alignment method and apparatus

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.

Utilizing deep learning for automatic digital image segmentation and stylization

Growing community of inventors

Hong Kong, China

Xiaoyong Shen