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

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.