公开(公告)号：US11468298B2

公开(公告)日：2022-10-11

申请号：US16573342

申请日：2019-09-17

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Curtis Wigington ,  Brian Price

IPC: G06N3/04 ,  G06F17/18 ,  G06K9/62 ,  G06N3/08 ,  G06N3/02 ,  G06V10/82

Abstract: Described techniques for multi-label classification, in which sequential data includes characters that have two or more aspects that require classification, are capable of providing separate classifications for different categories of components. Using an appropriately-trained neural network, the described techniques perform aligning and otherwise combining two or more classifications (e.g., categories, or types of labels) to obtain multi-label characters.

公开(公告)号：US11314982B2

公开(公告)日：2022-04-26

申请号：US16216739

申请日：2018-12-11

Applicant: Adobe Inc.

Inventor： Brian Price ,  Scott Cohen ,  Ning Xu

IPC: G06K9/32 ,  G06N3/08 ,  G06K9/46 ,  G06N3/04 ,  G06N5/00 ,  G06K9/36

Abstract: Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

公开(公告)号：US11282208B2

公开(公告)日：2022-03-22

申请号：US16231746

申请日：2018-12-24

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Long Mai ,  Jun Hao Liew ,  Brian Price

IPC: G06T7/12 ,  G06T7/194 ,  G06K9/32 ,  G06T3/40

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.

公开(公告)号：US10290112B2

公开(公告)日：2019-05-14

申请号：US15996833

申请日：2018-06-04

Applicant: ADOBE INC.

Inventor： Xiaohui Shen ,  Scott Cohen ,  Peng Wang ,  Bryan Russell ,  Brian Price ,  Jonathan Eisenmann

IPC: G06T7/50 ,  G06T7/62 ,  G06T7/13

Abstract: Techniques for planar region-guided estimates of 3D geometry of objects depicted in a single 2D image. The techniques estimate regions of an image that are part of planar regions (i.e., flat surfaces) and use those planar region estimates to estimate the 3D geometry of the objects in the image. The planar regions and resulting 3D geometry are estimated using only a single 2D image of the objects. Training data from images of other objects is used to train a CNN with a model that is then used to make planar region estimates using a single 2D image. The planar region estimates, in one example, are based on estimates of planarity (surface plane information) and estimates of edges (depth discontinuities and edges between surface planes) that are estimated using models trained using images of other scenes.