A Probabilistic Fitness Measure for Deformable Template Models

Abstract

Methods for automatic image interpretation based on the use of deformable template models have proved very successful. Whatever deformable template scheme is used, one of the basic requirements is a method for assessing the likelihood that a particular model instance is the correct interpretation of a given image. We describe a Bayesian 'fitness' measure which combines the likelihood of the model shape with the evidential support in a principled way. Image search is carried out by minimising the fitness measure using multi-scale quasi-Newtonian optimisation. We have previously compared the performance of different fitness measures. Here we give results for the new method and show that, by making optimal use of the image evidence, it achieves more accurate interpretation than the best of the methods we have previously tested.