Object recognition and performance bounds

Abstract

Object recognition is the classification of objects into one of many a priori known object classes. In addition, it may involve the estimation of the pose of the object and/or the track of the object in a sequence of images. Bayesian statistical pattern recognition, neural networks and rule based systems have been used to address the object recognition problem. In the case of statistical pattern recognition it is assumed that the a priori probability density functions are known or that they can be estimated from the given samples. For neural networks the samples may be used to train a network and the coefficients for the network function may be estimated. Whereas, in the case of the rule based system, rules may be given by an expert or they may be estimated from the samples. However, Bayesian framework provides a methodology for the estimation of error bounds on the performance of the recognition system. The paper discusses the Bayesian paradigm and contrasts its ability to provide performance bounds as compared to neural networks and rule based systems. Future direction of results on object recognition and performance bounds will also be discussed.