Shape matching based on skeletonization and alignment of primitive chains

Abstract

We introduce a new shape matching approach based on skeletonization and alignment of primitive chains. At the first stage the skeleton of a binary image is traversed counterclockwise in order to encode it by chain of primitives. A primitive describes topological properties of the correlated edge and consists of a pair of numbers: the length of some edge and the angle between this and the next edges. We offer to expand a primitive by the information about the radial function of the skeleton rib. To get the compact width description we interpolate radial function by Legendre polynomials and find the vector of Legendre coefficients. Thus the resulting shape representation by the chain of primitives includes not only topological properties but also the contour ones. Then we suggest the dynamic programming procedure of the alignment of two primitive chains in order to match correspondent shapes. Based on the optimal alignment we propose the pair-wise dissimilarity function which is evaluated on artificial image dataset and the Flavia leaf dataset.