Bayesian Methods for Image Segmentation (Preprint)

摘要

This paper presents an introduction to Bayesian methods for image segmentation, and provides some examples of the performance of these methods. Bayesian image segmentation methods represent a class of statistical approaches to the problem of segmentation. The idea behind Bayesian techniques is to use statistical image models to incorporate prior information into the segmentation process. This is typically done by specifying a model for the observed image to be segmented and a model for the segmentation image itself. These image models are then used to create a cost function which is optimized to obtain the segmentation result. Different Bayesian segmentation schemes are distinguished by several aspects: the image models used, the optimization criterion used to obtain the cost function, and the computational approach used to perform the optimization.