Image registration with shape mixtures

Abstract

This paper describes how mixtures of Gaussians can be used for multiple shape template registration. The EM algorithm is applied to the shape mixture model to compute both maximum likelihood registration parameters together with a set of a posteriori matching probabilities. This architecture can be viewed as providing simultaneous registration and hypothesis verification. The different templates compete to account for data through the imposed probability normalisation. Based on a sensitivity study, our main conclusions are that the method is both robust to added image noise and poor initialisation.