Methods for numerical integration of high-dimensional posterior densities with application to statistical image models

Many image processing and computer vision applications use Bayesian analysis, but it is limited by the computational requirements of extracting data from high-dimensional probability spaces. Recently, new techniques have been developed that enhance Bayesian analysis. A survey of previous and present methods for computing marginal density values for common image models is presented, focusing on implicit polynomial surface models, a Markov random field formulation and parametric polynomial surface models.