A Kernel View of Spectral Point Pattern Matching

This paper investigates spectral approaches to the problem of point pattern matching. Specifically, kernel principle component analysis (kernel PCA) methods are studied and compared with Shapiro and Brady’s approach and multidimensional scaling methods on both synthetic data and real world data. We demonstrate that kernel methods can be effectively used for solving the point correspondence matching problem with a performance that is comparable with other iterative-based algorithms in the literature under the existing of outliers and random position jitter. We also provide discussion of the theoretical support from kernel PCA to the earlier approach of Shapiro and Brady.