公开(公告)号：US20220172331A1

公开(公告)日：2022-06-02

申请号：US17651435

申请日：2022-02-17

Applicant: Adobe Inc.

Inventor： Connelly Barnes ,  Sohrab Amirghodsi ,  Elya Shechtman

IPC: G06T5/00 ,  G06T7/33 ,  G06T5/50

Abstract: Techniques are disclosed for filling or otherwise replacing a target region of a primary image with a corresponding region of an auxiliary image. The filling or replacing can be done with an overlay (no subtractive process need be run on the primary image). Because the primary and auxiliary images may not be aligned, both geometric and photometric transformations are applied to the primary and/or auxiliary images. For instance, a geometric transformation of the auxiliary image is performed, to better align features of the auxiliary image with corresponding features of the primary image. Also, a photometric transformation of the auxiliary image is performed, to better match color of one or more pixels of the auxiliary image with color of corresponding one or more pixels of the primary image. The corresponding region of the transformed auxiliary image is then copied and overlaid on the target region of the primary image.

公开(公告)号：US20210357684A1

公开(公告)日：2021-11-18

申请号：US15930539

申请日：2020-05-13

Applicant: Adobe Inc.

Inventor： Sohrab Amirghodsi ,  Zhe Lin ,  Yilin Wang ,  Tianshu Yu ,  Connelly Barnes ,  Elya Shechtman

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00 ,  G06F17/18

Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

公开(公告)号：US20200372619A1

公开(公告)日：2020-11-26

申请号：US16420782

申请日：2019-05-23

Applicant: ADOBE INC.

Inventor： Sohrab Amirghodsi ,  Elya Shechtman ,  Derek Novo

IPC: G06T5/00 ,  G06T7/194 ,  G06T7/90 ,  G06T7/70

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware sampling region for a hole-filling algorithm such as content-aware fill. Given a source image and a hole (or other target region to fill), a sampling region can be synthesized by identifying a band of pixels surrounding the hole, clustering these pixels based on one or more characteristics (e.g., color, x/y coordinates, depth, focus, etc.), passing each of the resulting clusters as foreground pixels to a segmentation algorithm, and unioning the resulting pixels to form the sampling region. The sampling region can be stored in a constraint mask and passed to a hole-filling algorithm such as content-aware fill to synthesize a fill for the hole (or other target region) from patches sampled from the synthesized sampling region.