Results 261 to 270 of about 22,384 (303)
Some of the next articles are maybe not open access.
2010
Based on the modelling discussions of Chapter 5, the issues of computational and storage complexity for large problems have motivated an interest in sparse representations, and also in those models which allow some sort of decoupling, or domain decomposition, to allow a hierarchical approach.
Rue, H, Held, L
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Based on the modelling discussions of Chapter 5, the issues of computational and storage complexity for large problems have motivated an interest in sparse representations, and also in those models which allow some sort of decoupling, or domain decomposition, to allow a hierarchical approach.
Rue, H, Held, L
+4 more sources
2008 International Machine Vision and Image Processing Conference, 2008
In this talk the author will outline some of the recent work undertaken by the Oxford Brookes Vision Group, a common theme underlying much of the research is to cast vision problems in terms of combinatorial optimization which provides a rich a deep theory for understanding them, with many new and exciting results.
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In this talk the author will outline some of the recent work undertaken by the Oxford Brookes Vision Group, a common theme underlying much of the research is to cast vision problems in terms of combinatorial optimization which provides a rich a deep theory for understanding them, with many new and exciting results.
openaire +2 more sources
On markov models of random fields
Acta Mathematicae Applicatae Sinica, 1987The paper considers different types of Markov models for random fields, namely causal Markov models, semicausal and noncausal Markov models. Several theorems of spectral characterizations of the models are given.
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Strong Markov Properties for Markov Random Fields
Journal of Theoretical Probability, 2000Markov properties for random fields are established. The author presents a multidimensional extension of stopping times by introducing random membranes. A special case of the random membrane is considered to obtain strong Markov property for a point process under Evstigneev's nonanticipating sufficient conditions.
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Strong markov random field model
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004The 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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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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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, 2008We 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 +2 more
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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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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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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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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, 1978openaire +1 more source