公开(公告)号：US20190266706A1

公开(公告)日：2019-08-29

申请号：US15907526

申请日：2018-02-28

Applicant: Adobe Inc.

Inventor： Prasenjit Mondal ,  Ruppesh Nalwaya ,  Ram Bhushan Agrawal ,  Deepanshu Arora ,  Anuj Shara

IPC: G06T5/00 ,  G06T5/20

Abstract: Techniques are disclosed for generating a shadow map of a digital image. In some examples, a method may include generating a shadow mask of a digital image, generating a dilated de-noised binarized gradient image based on the shadow mask, generating a binarized median-filtered gray image based on the digital image and the dilated de-noised binarized gradient image, and generating a shadow map based on the shadow mask and the binarized median-filtered gray image. The generated shadow map can then be used to remove shadows from the digital image without degrading the quality of the image content in the digital image.

公开(公告)号：US10559067B2

公开(公告)日：2020-02-11

申请号：US15907526

申请日：2018-02-28

Applicant: Adobe Inc.

Inventor： Prasenjit Mondal ,  Ruppesh Nalwaya ,  Ram Bhushan Agrawal ,  Deepanshu Arora ,  Anuj Shara

IPC: G06K9/40 ,  G06T5/00 ,  G06T5/20

Abstract: Techniques are disclosed for generating a shadow map of a digital image. In some examples, a method may include generating a shadow mask of a digital image, generating a dilated de-noised binarized gradient image based on the shadow mask, generating a binarized median-filtered gray image based on the digital image and the dilated de-noised binarized gradient image, and generating a shadow map based on the shadow mask and the binarized median-filtered gray image. The generated shadow map can then be used to remove shadows from the digital image without degrading the quality of the image content in the digital image.

公开(公告)号：US10970847B2

公开(公告)日：2021-04-06

申请号：US16413922

申请日：2019-05-16

Applicant: Adobe Inc.

Inventor： Prasenjit Mondal ,  Anuj Shara ,  Ankit Bal ,  Deepanshu Arora ,  Siddharth Kumar

IPC: G06T7/13 ,  G06T5/00 ,  G06T7/136 ,  G06T5/20 ,  G06T5/40 ,  G06N20/00

Abstract: Techniques are disclosed for document boundary detection (BD) from an input image using a combination of deep learning model and image processing algorithms. Quadrilaterals approximating the document boundaries in the input image are determined and rated separately using both these approaches: deep leaning using convolutional neural network (CNN) and heuristics using image processing algorithms. Thereafter, the best rated quadrilateral is selected from the quadrilaterals obtained from both the approaches.