Adaptive Graph Matching

Xu Yang

doi:10.1109/TCYB.2017.2697968

2018-05-01

PRE

杨絮 (Xu Yang)

刘

刘志勇 (Zhiyong Liu) *

DOI：10.1109/TCYB.2017.2697968

原文链接

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摘要

En 中文

Establishing correspondence between point sets lays the foundation for many computer vision and pattern recognition tasks. It can be well defined and solved by graph matching. However, outliers may significantly deteriorate its performance, especially when outliers exist in both point sets and meanwhile the inlier number is unknown. In this paper, we propose an adaptive graph matching algorithm to tackle this problem. Specifically, a novel formulation is proposed to make the graph matching model adaptively determine the number of inliers and match them, then by relaxing the discrete domain to its convex hull the discrete optimization problem is relaxed to be a continuous one, and finally a graduated projection scheme is used to get a discrete matching solution. Consequently, the proposed algorithm could realize inlier number estimation, inlier selection, and inlier matching in one optimization framework. Experiments on both synthetic data and real world images witness the effectiveness of the proposed algorithm.

Keyword:

Graduated projection

graph matching

point correspondence

regularization method

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Transactions on Cybernetics

IF:

10.5

论文数:

1.1W

被引数:

5.0W

机构

chinese academy of sciences

学者数:

56.7W

论文数: 45.0W

被引数: 704

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