2013/03/06 by Steven M. LaValle, LaValle, Steven M., Seth Hutchinson +2
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning and Data Classification #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.1303.1460
Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
arxiv created 2013/03/06 · openalex publication_date 2013/03/06 · arxiv updated 2013/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.