公开(公告)号：US10008004B1

公开(公告)日：2018-06-26

申请号：US15406504

申请日：2017-01-13

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Fangfang Liang ,  Yuanhua Qiao ,  Wei Ma ,  Jun Miao

IPC: G06K9/00 ,  G06T7/593 ,  G06T17/00 ,  G06K9/52 ,  G06K9/46 ,  G06T15/20 ,  G06T7/194

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.

公开(公告)号：US20180182118A1

公开(公告)日：2018-06-28

申请号：US15406504

申请日：2017-01-13

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Fangfang Liang ,  Yuanhua Qiao ,  Wei Ma ,  Jun Miao

IPC: G06T7/593 ,  G06T17/00 ,  G06K9/52 ,  G06K9/46 ,  G06T15/20 ,  G06T7/194

CPC classification number: G06T7/593 ,  G06K9/00201 ,  G06K9/4652 ,  G06K9/4676 ,  G06T7/194 ,  G06T15/20 ,  G06T17/00 ,  G06T2200/04

Abstract: A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.