  In this paper we address the uncertainty issues involved in the low-level
vision task of image segmentation. Researchers in computer vision have worked
extensively on this problem, in which the goal is to partition (or segment) an
image into regions that are homogeneous or uniform in some sense. This
segmentation is often utilized by some higher level process, such as an object
recognition system. We show that by considering uncertainty in a Bayesian
formalism, we can use statistical image models to build an approximate
representation of a probability distribution over a space of alternative
segmentations. We give detailed descriptions of the various levels of
uncertainty associated with this problem, discuss the interaction of prior and
posterior distributions, and provide the operations for constructing this
representation.
