Statistical approach to model matching

This paper describes a way of applying statistical estimation techniques to the model matching problem of computer vision. A detailed metrical object model is assumed. A simple MAP model matching method is described that captures important aspects ofrecognition in controlled situations. A probabilistic model of image features is combined with a simple prior on the pose and feature interpretations to yield a mixed objective function. Extremizing the objective function yields an optimal matching between model and image features. Good models of feature uncertainty allows for robustness with respect to inaccuracy in feature detection. Additionally the relative likelihood of a feature arising from the object or the background can be evaluated in a rational way. The parameters that appear in the probabilistic models may easily be derived from images in the application domain. The objective function takes a particularly simple form when feature deviations are modeled by Normal densities and the projection model is linear. Several linear projection and feature models are discussed. Evidence is provided to show that Normal feature deviation models can be appropriate for computer vision matching problems. Relation to other work and possible extensions and application areas are discussed.