A Novel Probabilistic Model for Object Recognition and Pose Estimation

In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.