Image Registration with Shape Mixtures

Simon Moss, Edwin R. Hancock

Abstract: This paper describes how mixtures of Gussins 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 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 the method is both robust to added noise and poor initialisation.