A mixture model for pose clustering

This paper describes a structural method for object alignment by pose clustering. The idea underlying pose clustering is to decompose the objects under consideration into k-tuples of primitive parts. By bringing pairs of k-tuples into correspondence, sets of alignment parameters are estimated. The global alignment corresponds to the set of parameters with maximum votes. The work reported here o€ers two novel contributions. Firstly, we impose structural constraints on the arrangement of the k-tuples of primitives used for pose clustering. This limits problems of combinatorial nature and eases the search for consistent pose clusters. Secondly, we use the EM algorithm to estimate maximum likelihood alignment parameters. Here we ®t a mixture model to the set of transformation parameter votes. We control the order of the underlying mixture model using a minimum description length criterion. The new alignment method is illustrated on the matching of optical and radar images of aerial scenes. Ó 1999 Published by Elsevier Science B.V. All rights