Point Pattern Matching Using Relative Shape Context and Relaxation Labeling

摘要：

This paper proposes a relative shape context and relaxation labeling (RSC-RL) based approach for point pattern matching (PPM). First of all, a new point set based invariant feature, Relative Shape Context (RSC), is proposed. Using the test statistic of relative shape context descriptors matching scores as the foundation of support function, the point pattern matching probability matrix can be iteratively updated by relaxation labeling (RL). In the end, the one-to-one matching can be achieved by dual-normalization of rows and columns in the finally obtained matching probability matrix. Experiments on both synthetic point sets and real world data show that the performance of the proposed technique is favorable under rigid geometric distortion, noises and outliers.

关键词： point pattern matching relative shape context relaxation labeling dual-normalization

作者: Jian Zhao Shilin Zhou Jixiang Sun Zhiyong Li

作者单位: School of Electronic Science and Engineering National University of Defense Technology Changsha,410073,Hunan,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 516-520

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）