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Markov Random Field Models in Computer Vision
, 1994
"... . 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 l ..."
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Cited by 516 (18 self)
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. 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
Markov random fields with efficient approximations
 In IEEE Conference on Computer Vision and Pattern Recognition
, 1998
"... Markov Random Fields (MRF’s) can be used for a wide variety of vision problems. In this paper we focus on MRF’s with twovalued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut pro ..."
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Cited by 210 (23 self)
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Markov Random Fields (MRF’s) can be used for a wide variety of vision problems. In this paper we focus on MRF’s with twovalued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut
applied to binary Markov random fields
"... An approximate forwardbackward algorithm applied to binary Markov random fields by ..."
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Cited by 1 (0 self)
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An approximate forwardbackward algorithm applied to binary Markov random fields by
Markov random fields defined on graphs
"... Exact and approximate recursive calculations for binary Markov random fields defined on graphs by ..."
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Exact and approximate recursive calculations for binary Markov random fields defined on graphs by
Combinatorial Markov Random Fields
 In Proceedings of ECML17
"... Abstract. A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variab ..."
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Cited by 4 (3 self)
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Abstract. A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random
Factorial Markov Random Fields
 In European Conference on Computer Vision (ECCV
, 2002
"... In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous to the extension from Hidden Markov Models (HMM's) to Factorial HMM's. We present an efficient EMbased algorit ..."
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Cited by 10 (0 self)
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In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous to the extension from Hidden Markov Models (HMM's) to Factorial HMM's. We present an efficient EM
Hidden Markov Random Fields
, 1993
"... A noninvertible function of a first order Markov process, or of a nearest neighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. A ..."
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Cited by 5 (1 self)
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A noninvertible function of a first order Markov process, or of a nearest neighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility
A Markov random field model for term dependencies
"... This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. W ..."
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Cited by 289 (55 self)
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This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases
Markov Random Fields and Gibbs Measures
, 2004
"... A Markov random field is a name given to a natural generalization of the well known concept of a Markov chain. It arrises by looking at the chain itself ..."
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A Markov random field is a name given to a natural generalization of the well known concept of a Markov chain. It arrises by looking at the chain itself
Loss networks and Markov random fields
"... Abstract This paper examines the connection between loss networks without controls andMarkov random field theory. The approach taken yields insight into the structure ..."
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Abstract This paper examines the connection between loss networks without controls andMarkov random field theory. The approach taken yields insight into the structure
Results 1  10
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