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Markov Random Field Models in Computer Vision

by S. Z. Li , 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 ..."
Abstract - Cited by 516 (18 self) - Add to MetaCart
. 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

Representation-based classifications with Markov random field model for hyperspectral urban data

by Mingming Xiong, Fan Zhang, Qiong Ran, Wei Hu, Wei Li, Mingming Xiong, Fan Zhang, Qiong Ran, Wei Hu, Wei Li
"... with Markov random field model for ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
with Markov random field model for

Strong Markov Random Field Model

by Rupert Paget
"... The strong Markov random field (MRF) model is a sub-model of the more general MRF-Gibbs model. The strong-MRF model defines a system whereby not only is the field Markovian with respect to a defined neighbourhood, but all sub-neighbourhoods also define a Markovian system. A checkerboard pattern is a ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
The strong Markov random field (MRF) model is a sub-model of the more general MRF-Gibbs model. The strong-MRF model defines a system whereby not only is the field Markovian with respect to a defined neighbourhood, but all sub-neighbourhoods also define a Markovian system. A checkerboard pattern

A Markov random field model for term dependencies

by Donald Metzler, W. Bruce Croft
"... 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 ..."
Abstract - Cited by 289 (55 self) - Add to MetaCart
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

Sub-pixel Land Cover Mapping Based on Markov Random Field Models

by P. K. Varshney, Teerasit Kasetkasem, Manoj K. Arora, Pramod K. Varshney , 2003
"... Sub-pixel land cover mapping based on Markov random field models ..."
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Sub-pixel land cover mapping based on Markov random field models

Markov Random Field Models in Image Processing

by Anand Rangarajan, Rama Chellappa, Anand Rangarajan , 1995
"... INTRODUCTION Markov random field models have become useful in several areas of image processing. The success of Markov random fields (MRFs) can be attributed to the fact that they give rise to good, flexible, stochastic image models. The goal of image modeling is to find an adequate representation o ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
INTRODUCTION Markov random field models have become useful in several areas of image processing. The success of Markov random fields (MRFs) can be attributed to the fact that they give rise to good, flexible, stochastic image models. The goal of image modeling is to find an adequate representation

Statistical Dependence in Markov Random Field Models

by Mark S. Kaiser , 2007
"... Statistical models based on Markov random fields present a flexible means for mod-eling statistical dependencies in a variety of situations including, but not limited to, spatial problems with observations on a lattice. The simplest of such models, some-times called “auto-models ” are formulated fro ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Statistical models based on Markov random fields present a flexible means for mod-eling statistical dependencies in a variety of situations including, but not limited to, spatial problems with observations on a lattice. The simplest of such models, some-times called “auto-models ” are formulated

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - 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 histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
-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

On Block Updating in Markov Random Field Models For . . .

by Leonhard Knorr-Held, Håvard Rue - SCANDINAVIAN JOURNAL OF STATISTICS , 2002
"... Gaussian Markov random field (GMRF) models are commonlyufz to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-siteuinglealgorithms have been considered. However, convergence and mixing properties ofsuD algorithms can be extremely ..."
Abstract - Cited by 85 (8 self) - Add to MetaCart
Gaussian Markov random field (GMRF) models are commonlyufz to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-siteuinglealgorithms have been considered. However, convergence and mixing properties ofsuD algorithms can be extremely

Automatic Feature Selection in the Markov Random Field Model for Information Retrieval

by Donald Metzler - In Proceedings of CIKM’07 , 2007
"... Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature s ..."
Abstract - Cited by 39 (6 self) - Add to MetaCart
Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature
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