A Bayesian approach to learn and classify 3D objects from intensity images

This contribution treats the problem of learning and recognizing 3D objects using 2D views. We present a new Bayesian approach to 3D computer vision based on the expectation-maximization algorithm, where learning and classification of objects correspond to parameter estimation algorithms. We give a formal description of different learning and recognition stages and conclude the associated statistical optimization problems for each Bayesian decision. The training stage is supposed to be unsupervised in the sense that no explicit feature matching among different views is necessary. Finally, the experimental part of the paper considers the special case, where observable point features are assumed to be normally distributed and the object and its projections are modeled by mixture density functions