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A Fast and Exact Simulation Algorithm for General Gaussian Markov Random Fields
, 1999
"... This paper presents a fast and exact simulation algorithm for a general Gaussian Markov Random Field (GMRF) defined on a n r \Theta n c lattice, n r n c . For a 5 \Theta 5 neighborhood, the algorithm has initialization cost of 4n r n ..."
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Cited by 7 (3 self)
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This paper presents a fast and exact simulation algorithm for a general Gaussian Markov Random Field (GMRF) defined on a n r \Theta n c lattice, n r n c . For a 5 \Theta 5 neighborhood, the algorithm has initialization cost of 4n r n
A Fast and Exact Simulation Algorithm for General Gaussian Markov Random Fields
, 1999
"... This report has URL http://www.math.ntnu.no/preprint/statistics/1999/S81999.ps ..."
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This report has URL http://www.math.ntnu.no/preprint/statistics/1999/S81999.ps
GENERALIZED GAUSSIAN MARKOV RANDOM FIELD IMAGE RESTORATION USING VARIATIONAL DISTRIBUTION APPROXIMATION
"... In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field prior utilizing variational distribution approximations. The restored image and the unknown hyperparameters for both the image prior and the image degradation nois ..."
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Cited by 2 (2 self)
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In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field prior utilizing variational distribution approximations. The restored image and the unknown hyperparameters for both the image prior and the image degradation
Shape parameter estimation for generalized Gaussian Markov random field models used in MAP image restoration
 In 29th Asilomar Conference on Signals, Systems, and Computers, October 29
, 1995
"... In this paper, we propose using the Generalized ..."
A Divide And Conquer Algorithm For Exact Simulation Of General Gaussian Markov Random Field
, 1999
"... This report has URL ..."
A generalized Gaussian image model for edgepreserving MAP estimation
 IEEE Trans. on Image Processing
, 1993
"... Absfrucf We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distri ..."
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Cited by 301 (37 self)
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Absfrucf We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian
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
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
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Cited by 752 (14 self)
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An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
, 2001
"... We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions ..."
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Cited by 3485 (85 self)
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We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions
Results 1  10
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10,757