公开(公告)号：US20210216806A1

公开(公告)日：2021-07-15

申请号：US16963140

申请日：2020-05-13

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Xin YANG ,  Xiaopeng WEI ,  Qiang ZHANG ,  Yuhao LIU ,  Yu QIAO

IPC: G06K9/46 ,  G06K9/62 ,  G06T7/11 ,  G06T7/194

Abstract: The invention belongs to the field of computer vision technology, and provides a fully automatic natural image matting method. For image matting of a single image, it is mainly composed of the extraction of high-level semantic features and low-level structural features, the filtering of pyramid features, the extraction of spatial structure information, and the late optimization of the discriminator network. The invention can generate accurate alpha matte without any auxiliary information, saving the time for scientific researchers to mark auxiliary information and the interaction time when users use it.

公开(公告)号：US20220215662A1

公开(公告)日：2022-07-07

申请号：US17557933

申请日：2021-12-21

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Xin YANG ,  Xiaopeng WEI ,  Yu QIAO ,  Qiang ZHANG ,  Baocai YIN ,  Haiyin PIAO ,  Zhenjun DU

IPC: G06V20/40 ,  G06T7/10 ,  G06V10/46 ,  G06V10/82 ,  G06T3/40 ,  G06T7/215 ,  G06T9/00 ,  G06K9/62 ,  G06V10/72 ,  G06V10/764 ,  G06V10/778 ,  G06V10/774 ,  G06V10/776

Abstract: The present invention belongs to the technical field of computer vision, and provides a video semantic segmentation method based on active learning, comprising an image semantic segmentation module, a data selection module based on the active learning and a label propagation module. The image semantic segmentation module is responsible for segmenting image results and extracting high-level features required by the data selection module; the data selection module selects a data subset with rich information at an image level, and selects pixel blocks to be labeled at a pixel level; and the label propagation module realizes migration from image to video tasks and completes the segmentation result of a video quickly to obtain weakly-supervised data. The present invention can rapidly generate weakly-supervised data sets, reduce the cost of manufacture of the data and optimize the performance of a semantic segmentation network.