This paper describes a structural method for object alignment by pose clust
ering. The idea underlying pose clustering is to decompose the objects unde
r consideration into k-tuples of primitive parts. By bringing pairs of k-tu
ples into correspondence, sets of alignment parameters are estimated. The g
lobal alignment corresponds to the set of parameters with maximum votes. Th
e work reported here offers two novel contributions. Firstly, we impose str
uctural constraints on the arrangement of the k-tuples of primitives used f
or pose clustering This limits problems of combinatorial nature and eases t
he search for consistent pose clusters. Secondly, we use the EM algorithm t
o estimate maximum likelihood alignment parameters. Here we fit a mixture m
odel 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 rada
r images of aerial scenes. (C) 1999 Published by Elsevier Science B.V. All
rights reserved.