公开(公告)号：US20210303825A1

公开(公告)日：2021-09-30

申请号：US17345240

申请日：2021-06-11

Applicant: ADOBE INC.

Inventor： Sachin Soni ,  Siddharth Kumar ,  Ram Bhushan Agrawal ,  Ajay Jain

IPC: G06K9/00 ,  G06T7/11 ,  G06N3/08 ,  G06T7/70

Abstract: Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

公开(公告)号：US11703949B2

公开(公告)日：2023-07-18

申请号：US17345240

申请日：2021-06-11

Applicant: ADOBE INC.

Inventor： Sachin Soni ,  Siddharth Kumar ,  Ram Bhushan Agrawal ,  Ajay Jain

IPC: G06F3/01 ,  G06V40/16 ,  G06T7/11 ,  G06N3/08 ,  G06T7/70 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V40/60

CPC classification number: G06F3/016 ,  G06N3/08 ,  G06T7/11 ,  G06T7/70 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V40/161 ,  G06V40/168 ,  G06V40/67

Abstract: Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

公开(公告)号：US11074430B2

公开(公告)日：2021-07-27

申请号：US15991296

申请日：2018-05-29

Applicant: ADOBE INC.

Inventor： Sachin Soni ,  Siddharth Kumar ,  Ram Bhushan Agrawal ,  Ajay Jain

IPC: G06K9/00 ,  G06T7/11 ,  G06N3/08 ,  G06T7/70

Abstract: Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

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.