Optimal Image Segmentation Methods Based on Energy Minimization

Abstract

In this chapter we present three “case studies” as representative of recent work on solving several segmentation problems (region segmentation, deformable templates matching and grouping) from the energy minimization perspective. Each of the latter problems is solved via an optimization approach, respectively: jump-diffusion, belief propagation and Bayesian inference. Our purpose is to show the connection between the formulation of the corresponding cost function and the optimization algorithm and also to present some useful ideas coming from Bayesian and information theory. This selection of only three problems (and solutions) allows us to present the fundamental elements of optimization in each particular case and to bring the readers to the arena of optimization-based segmentation.