Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 225,648,001 papers from all fields of science
Search
Sign In
Create Free Account
Markov random field
Known as:
Markov field
, Markov net
, Markov graph
Expand
In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
49 relations
Artificial intelligence
Bayesian network
Belief propagation
Boltzmann machine
Expand
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2010
Highly Cited
2010
Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction
K. Rimstad
,
H. Omre
2010
Corpus ID: 128603350
Early assessments of petroleum reservoirs are usually based on seismic data and observations in a small number of wells. Decision…
Expand
Highly Cited
2004
Highly Cited
2004
Investigations of a magnetorheological fluid damper
Marin Liţă
,
Nicolae Călin Popa
,
C. Velescu
,
L. Vékás
IEEE transactions on magnetics
2004
Corpus ID: 31127243
This paper presents the results of a study of a magnetorheological fluid (MRF) damper. The principle could be used for such…
Expand
Highly Cited
2003
Highly Cited
2003
Multi-objective super resolution: concepts and examples
D. Rajan
,
S. Chaudhuri
,
M. Joshi
IEEE Signal Processing Magazine
2003
Corpus ID: 119825469
Described methods for simultaneously generating the super-resolved depth map and the image from LR observations. Structural…
Expand
2002
2002
Factorial Markov Random Fields
Junh-Nam Kim
,
R. Zabih
European Conference on Computer Vision
2002
Corpus ID: 18841675
In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension…
Expand
Review
2002
Review
2002
Multiscale Data Integration Using Markov Random Fields
Sang Heon Lee
,
A. Malallah
,
A. Datta-Gupta
,
D. Higdon
2002
Corpus ID: 14110989
This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract…
Expand
Highly Cited
2002
Highly Cited
2002
Conditional maximum likelihood estimation under various specifications of exponential random graph models
T. Snijders
,
M. Duijn
2002
Corpus ID: 5578385
One among the major contributions by Ove Frank to the statistical analysis of social networks was the introduction, in Frank and…
Expand
Highly Cited
1999
Highly Cited
1999
Filtering methods for texture discrimination
Chien-Chang Chen
,
Chaur-Chin Chen
Pattern Recognition Letters
1999
Corpus ID: 21601563
Highly Cited
1991
Highly Cited
1991
Texture segmentation based on a hierarchical Markov random field model
R. Hu
,
M. Fahmy
., IEEE International Sympoisum on Circuits and…
1991
Corpus ID: 61286525
A novel texture segmentation technique for both supervised and unsupervised segmentation is presented. The textured images under…
Expand
1989
1989
Color image segmentation using Markov random fields
M. Daily
Proceedings CVPR '89: IEEE Computer Society…
1989
Corpus ID: 39605452
The use of Markov random fields (MRFs) in color image segmentation of natural outdoor scenes is discussed. MRFs provide an…
Expand
Highly Cited
1985
Highly Cited
1985
Maximum likelihood discriminant analysis on the plane using a Markovian model of spatial context
J. Haslett
Pattern Recognition
1985
Corpus ID: 47562457
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE