Learning Probability Models and Algorithms for Vision

摘要

It is theoretically attractive to formulate vision problems in terms of Bayesian probability theory using maximum a posteriori (MAP) estimation. This presupposes that we know the probability distributions and have fast algorithms for calculating the estimators. This paper describes theories for unsupervised learning of both the algorithms and the probability distributions.