Recognition of spatial point patterns

摘要

Complex patterns may sometimes be efficiently represented by a small number of points extracted from the image on the basis of atypical properties of neighborhoods of the points. In this paper we describe theoretical and experimental results on the use of sequences of interpoint distances extracted from a noisy point image to match the image with one of a set of prototypes. Minimal spanning tree and nearest neighbor distances are used for these computations. The methods applied are invariant under translation and rotation of the image and could be made invariant to scale changes. Several distance measures between classes are defined. Sufficient conditions for the separability of classes are given in terms of these measures.