公开(公告)号：US11756204B2

公开(公告)日：2023-09-12

申请号：US17336702

申请日：2021-06-02

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Wen Dong ,  Xin Yang ,  Haiyang Mei ,  Xiaopeng Wei ,  Qiang Zhang

IPC: G06T7/11 ,  G06T7/73 ,  G06T7/174 ,  G06N3/08

CPC classification number: G06T7/11 ,  G06N3/08 ,  G06T7/174 ,  G06T7/74 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: The invention belongs to scene segmentation's field in computer vision and is a depth-aware method for mirror segmentation. PDNet successively includes a multi-layer feature extractor, a positioning module, and a delineating module. The multi-layer feature extractor uses a traditional feature extraction network to obtain contextual features; the positioning module combines RGB feature information with depth feature information to initially determine the position of the mirror in the image; the delineating module is based on the image RGB feature information, combined with depth information to adjust and determine the boundary of the mirror. This method is the first method that uses both RGB image and depth image to achieve mirror segmentation in an image. The present invention has also been further tested. For mirrors with a large area in a complex environment, the PDNet segmentation results are still excellent, and the results at the boundary of the mirrors are also satisfactory.

公开(公告)号：US11816843B2

公开(公告)日：2023-11-14

申请号：US17336748

申请日：2021-06-02

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Xin Yang ,  Haiyang Mei ,  Wen Dong ,  Xiaopeng Wei ,  Dengping Fan

IPC: G06T7/12 ,  G06T7/136 ,  G06T7/73

CPC classification number: G06T7/12 ,  G06T7/136 ,  G06T7/73 ,  G06T2207/20084 ,  G06T2207/30181

Abstract: A method for segmenting a camouflaged object image based on distraction mining is disclosed. PFNet successively includes a multi-layer feature extractor, a positioning module, and a focusing module. The multi-layer feature extractor uses a traditional feature extraction network to obtain different levels of contextual features; the positioning module first uses RGB feature information to initially determine the position of the camouflaged object in the image; the focusing module mines the information and removes the distraction information based on the image RGB feature information and preliminary position information, and finally determines the boundary of the camouflaged object step by step. The method of the present invention introduces the concept of distraction information into the problem of segmentation of the camouflaged object and develops a new information exploration and distraction information removal strategy to help the segmentation of the camouflaged object image.