On using priors in affine matching

In this paper, we consider the generative model for affine transformations on point sets and show how a priori information on the noise and the transformation can be incorporated into the model resulting in more accurate algorithms. While invariants have been widely used, the existing literature fails to fully account for the uncertainties introduced by both noise and the transformation. We show how using such priors leads to algorithms for Bayesian estimation and a probabilistic interpretation of invariants which addresses the limitations of current methods. We present synthetic and real results for object recognition, image registration and determining object planarity to demonstrate the power of using priors for image comparison.