基于相对形状上下文和谱匹配方法的点模式匹配算法

Point Pattern Matching Algorithm Based on Relative Shape Context and Spectral Matching Method

该文提出了一种将不变特征与谱匹配方法相结合的点模式匹配算法。该算法首先提出一种新的基于点集的不变特征相对形状上下文,然后利用点集间相对形状上下文的统计检验匹配测度来定义新的相容性度量,并以此为基础构造分配图及其亲近矩阵。最后利用分配图亲近矩阵的主特征向量以及匹配约束条件来实现点模式匹配问题的求解。模拟仿真与真实数据实验验证了该文算法的有效性和鲁棒性。

This paper presents a novel and robust point pattern matching algorithm in which the invariant feature and the method of spectral matching are combined.A new point-set based invariant feature,Relative Shape Context（RSC）,is proposed firstly.Using the test statistic of relative shape context descriptor＇s matching scores as the foundation of new compatibility measurement,the assignment graph and the affinity matrix of assignment graph are constructed based on the gained compatibility measurement.Finally,the correct matching results are recovered by using the principal eigenvector of affinity matrix of assignment graph and imposing the mapping constraints required by the overall correspondence mapping.Experiments on both synthetic point-sets and on real world data show that the proposed algorithm is effective and robust.