BibTeX
@MISC{Li94markovrandom,
author = {S. Z. Li},
title = {Markov Random Field Models in Computer Vision},
year = {1994}
}
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Abstract
. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined 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 unified approach for MRF modeling in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles. 1 Introduction Since its beginning in early 1960's, computer vision research has been evolving from heuristic design of algorithms to syste...
Keyphrases
markov random field model computer vision prior model likelihood model unified approach computer vision problem sound mathematical principle probability estimate heuristic design true labeling problem domain dependent various prior constraint high level computer vision high level object recognition latter relates recent advance former depends posterior probability contextual constraint bayesian labeling systematic approach prior probability computer vision research
