Markov Random Field Models in Computer Vision

A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is dened as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a uni ed approach for MRF modeling in low and high level computer vision. The uni cation is made possible due to a recent advance in MRF modeling for high level object recognition. Such uni cation provides a systematic approach for vision modeling based on sound mathematical principles.

• Type: Conference
• Year: 1994
• Where: ECCV
• Authors: Stan Z. Li
• Keywords: Computer Vision, ECCV 1994, MRF Modeling, Such Uni Cation, Uni Cation