公开(公告)号：US20220114705A1

公开(公告)日：2022-04-14

申请号：US17557431

申请日：2021-12-21

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Yinan Zhao ,  Scott David Cohen

IPC: G06T5/00 ,  G06T5/50 ,  G06T11/40 ,  G06T7/33 ,  G06N3/08 ,  G06T3/00 ,  G06N3/04

Abstract: Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

公开(公告)号：US20220092790A1

公开(公告)日：2022-03-24

申请号：US17544048

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Peng Zhou ,  Scott David Cohen ,  Gregg Darryl Wilensky

IPC: G06T7/13

Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

公开(公告)号：US11244430B2

公开(公告)日：2022-02-08

申请号：US16830005

申请日：2020-03-25

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Yinan Zhao ,  Scott David Cohen

IPC: G06T5/00 ,  G06N3/04 ,  G06T7/33 ,  G06N3/08 ,  G06T3/00 ,  G06T11/40 ,  G06T5/50

Abstract: Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

公开(公告)号：US20210295525A1

公开(公告)日：2021-09-23

申请号：US16822853

申请日：2020-03-18

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Peng Zhou ,  Scott David Cohen ,  Gregg Darryl Wilensky

IPC: G06T7/13

Abstract: In implementations of object boundary generation, a computing device implements a boundary system to receive a mask defining a contour of an object depicted in a digital image, the mask having a lower resolution than the digital image. The boundary system maps a curve to the contour of the object and extracts strips of pixels from the digital image which are normal to points of the curve. A sample of the digital image is generated using the extracted strips of pixels which is input to a machine learning model. The machine learning model outputs a representation of a boundary of the object by processing the sample of the digital image.

公开(公告)号：US20200226725A1

公开(公告)日：2020-07-16

申请号：US16830005

申请日：2020-03-25

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Yinan Zhao ,  Scott David Cohen

IPC: G06T5/00 ,  G06T5/50 ,  G06T11/40 ,  G06T7/33 ,  G06N3/08 ,  G06T3/00 ,  G06N3/04

Abstract: Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

公开(公告)号：US10699388B2

公开(公告)日：2020-06-30

申请号：US15879354

申请日：2018-01-24

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Yinan Zhao ,  Scott David Cohen

IPC: G06T5/00 ,  G06T5/50 ,  G06T7/33 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04 ,  G06T11/40

Abstract: Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

公开(公告)号：US20190279074A1

公开(公告)日：2019-09-12

申请号：US15913829

申请日：2018-03-06

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Yufei Wang ,  Xiaohui Shen ,  Scott David Cohen ,  Jianming Zhang

IPC: G06N3/04 ,  G06T7/10 ,  G06F17/30 ,  G06F15/18

Abstract: Semantic segmentation techniques and systems are described that overcome the challenges of limited availability of training data to describe the potentially millions of tags that may be used to describe semantic classes in digital images. In one example, the techniques are configured to train neural networks to leverage different types of training datasets using sequential neural networks and use of vector representations to represent the different semantic classes.

公开(公告)号：US20190228508A1

公开(公告)日：2019-07-25

申请号：US15879354

申请日：2018-01-24

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Yinan Zhao ,  Scott David Cohen

IPC: G06T5/00 ,  G06T5/50 ,  G06T11/40 ,  G06T7/33 ,  G06T3/00 ,  G06N3/08

Abstract: Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.