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VLSM
2005
Springer
14 years 25 days ago
Entropy Controlled Gauss-Markov Random Measure Field Models for Early Vision
We present a computationally efficient segmentationrestoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the para...
Mariano Rivera, Omar Ocegueda, José L. Marr...
ICMCS
2010
IEEE
193views Multimedia» more  ICMCS 2010»
13 years 8 months ago
Motion segmentation in compressed video using Markov Random Fields
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First,...
Yue-Meng Chen, Ivan V. Bajic, Parvaneh Saeedi
CORR
2006
Springer
76views Education» more  CORR 2006»
13 years 7 months ago
Inconsistent parameter estimation in Markov random fields: Benefits in the computation-limited setting
Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and th...
Martin J. Wainwright
TIP
2008
133views more  TIP 2008»
13 years 7 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
ICPR
2000
IEEE
14 years 8 months ago
A Markov Random Field Model for Automatic Speech Recognition
Speech can be represented as a time/frequency distribution of energy using a multi-band filter bank. A Markov random field model, which takes into account the possible time asynch...
Gérard Chollet, Guillaume Gravier, Marc Sig...