Paper Info
Title
Weighted LIC-Based Structure Tensor With Application to Image Content Perception and Processing
Abstract
As a famous visual content perception and processing tool, structure tensor has been widely studied in the past decades. Among them, the anisotropic nonlocal structure tensor (ANLST) has received much attention, recently. However, the existing ANLST calculation methods fail to fully utilize the anisotropic characteristic of the tensor field, thus resulting in limited performance. For this problem, in this article, we present a novel ANLST construction method, by means of combining tensor decomposition with weighted line integral convolution (LIC) with the aim at deeply discovering and exploiting the spatial direction relevancy of the tensors for their regularization. At first, the tensors decomposition, computed by direction projection, yields multiple atomic vector fields, from which, for each point in the tensor field we obtain a family of integral curves that are associated with spatial direction related tensors. Then, LIC is employed with the nonlocal means filtering to smooth the tensors relevant to each integral curve, giving rise to curve-level structure tensor (CLST). At last, a weighted average scheme is carried out on the multiple CLSTs, leading to our proposed weighted anisotropic nonlocal structure tensor (WANST). Experimental results demonstrate that the proposed WANST is superior to the current representative nonlinear structure tensors. The proposed WANST can be applied to industrial surveillance system to enable it perceive image contents, such as flat regions, corners, textures, and edges. In addition, WANST can also help monitoring system improve its image quality.
Year
DOI
Venue
2021
10.1109/TII.2020.2980577
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Industrial intelligence,nonlocal structure tensor,tensor field regularization,visual information perception
Journal
17
Issue
ISSN
Citations 
3
1551-3203
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yuhui Zheng1439.40
Yahui Sun200.34
Khan Muhammad398667.67
Victor Hugo C. de Albuquerque491483.30