Probabilistic Solution of Ill-Posed Problems in Computational Vision

Abstract

Abstract Computational vision is a set of inverse problems. We review standard regularization theory, discuss its limitations, and present new stochastic (in particular, Bayesian) methods for their solution. We derive efficient algorithms and describe parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers.