Results 231 to 240 of about 251,998 (281)

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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Markov Random Field Texture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983
We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models.
G R, Cross, A K, Jain
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2009
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Modeling Stereopsis via Markov Random Field

Neural Computation, 2010
Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local ...
Ming, Yansheng, Hu, Zhanyi
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Markov Argumentation Random Fields

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning ...
Yuqing Tang, Nir Oren, Katia Sycara
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Strong Markov Properties for Markov Random Fields

Journal of Theoretical Probability, 2000
Markov 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, 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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Semi-Markov Random Fields

1993
The spatial distribution of sedimentary fades in petroleum reservoirs is of major concern due to its influence on the flow regime in the reservoir, and thereby the production of oil and gas. A class of stochastic models, called semi-Markov Random Fields, for the facies distribution in petroleum reservoirs is presented.
Håkon Tjelmeland, Lars Holden
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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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