Results 261 to 270 of about 47,039 (298)

Strong markov random field model

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004
The strong Markov random field (strong-MRF) model is a submodel of the more general MRF-Gibbs model. The strong-MRF model defines a system whose field is Markovian with respect to a defined neighborhood, and all subneighborhoods are also Markovian. A checkerboard pattern is a perfect example of a strong Markovian system.
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Markov Random Fields

2011
Let’s give Bayesian networks a break, and let us go back to our favorite topic, namely soccer. Suppose you want to develop a probabilistic model of the ranking of your team in the domestic soccer league championship at any given time t throughout the current season.
Antonino Freno, Edmondo Trentin
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Multi-robot Markov random fields

International Joint Conference on Autonomous Agents and Multiagent Systems, 2008
We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selection. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions ...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian P. Gerkey   +2 more
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Markov random fields

2000
Imagine a set S of people, the inhabitants of your home town, say. For every s I S there is a subset 𝒩 s of S: the people whom s knows, his or her neighbours, friends or colleagues. It happens that some people are infected by a dangerous disease D, the probability that a particular person s has D will naturally depend on the number of t ∈𝒩 s with D ...
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Markov Random Fields

2015
This chapter presents an introduction to Markov random fields (MRFs), also known as Markov networks, which are undirected graphical models. We describe how a Markov random field is represented, including its structure and parameters, with emphasis on regular MRFs.
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“Markov Times” for Random Fields

Theory of Probability & Its Applications, 1978
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Subsampling of Markov random fields

Journal of Visual Communication and Image Representation, 1992
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Markov Random Fields.

Journal of the American Statistical Association, 1984
Robert J. Adler, Yu A. Rozanov
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Hidden markov random field model selection criteria based on mean field-like approximations

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
Florence Forbes
exaly  

Image denoising based on hierarchical Markov random field

Pattern Recognition Letters, 2011
Yupin Luo
exaly