Hybrid parametric case-based approach to object recognition using Bayes decision theory

We consider the problem of recognition of rigid, manufactured objects, each from a predefined set of possible object classes, from their images. We describe a parametric statistical approach to this problem that is a hybrid between statistical modeling using Bayes decision theory with a generative model of images and a case-based approach. Our method is a variant of the Gibbs sampling method, commonly used to compute posterior probabilities in complex statistical models, but unlike standard Gibbs sampling methods, our method is based directly on analysis of a library of previously analyzed images. We also propose a simple gradient descent method to optimize the parameters of the models to maximize effective object recognition.