MAP model matching

A simple MAP model-matching criterion that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combined with a simple prior on both the pose and the feature interpretations to yield a mixed objective function. The parameters that appear in the probabilistic models can be derived from images in the application domain. By extremizing the objective function, an optimal matching between model and image feature results. Within this framework, good models of feature uncertainty allow for robustness despite inaccuracy in feature detection. In addition, the relative likelihood of features arising from either the object or the background can be evaluated in a rational way. The objective function provides a simple and uniform means of evaluating match and pose hypotheses. Several linear projection and feature models are discussed. An experimental implementation of MAP model matching, among features derived from low-resolution edge images, is described.<<ETX>>