Robust feature matching via advanced neighborhood topology consensus

被引：0
作者：
Liu Y. [1 ,2 ]
Li Y. [3 ]
Dai L. [1 ]
Yang C. [1 ]
Wei L. [1 ]
Lai T. [4 ]
Chen R. [1 ]
机构：
[1] Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou
[2] School of Software Engineering, Tongji University, Shanghai
[3] Department of Computer Science and Technology, Tongji University, Shanghai
[4] College of Computer and Control Engineering, Minjiang University, Fuzhou
来源：
Neurocomputing | 2021年 / 421卷
基金：
中国国家自然科学基金;
关键词：
Feature matching; Guided matching strategy; Neighborhood topology; Outlier removal;
D O I：
10.1016/j.neucom.2020.09.047
中图分类号：
O144 [集合论]; O157 [组合数学（组合学）];
学科分类号：
070104 ;
摘要：
Feature matching is one of the key techniques in many vision-based tasks, which aims to establish reliable correspondences between two sets of features. In this paper, we present a new feature matching method, which formulates the matching of two feature sets as a mathematical model based on two common consistency constraints. We first propose an advanced consensus of neighborhood topology, which can better exploit the consensus of topological structures to identify inliers. In order to have reliable neighborhood information for the feature points, a subset with high percentage inliers obtained by a guided matching strategy from the putative matches for the neighborhood construction is used. We demonstrate the advantages of our proposed method on various real image pairs. The results demonstrate that the proposed method is superior to the state-of-the-art feature matching methods. © 2020 Elsevier B.V.
引用
收藏
页码：273 / 284
页数：11
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