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Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983We 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.
Anil K Jain
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Pattern recognition using Markov random field models [PDF]
Pattern Recognition, 2002 In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems.
Jinhai Cai
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Markov Argumentation Random Fields
Proceedings of the AAAI Conference on Artificial Intelligence, 2016We 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 0001 +2 more
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Markov random fields and gibbs random fields
Israel Journal of Mathematics, 1973Spitzer has shown that every Markov random field (MRF) is a Gibbs random field (GRF) and vice versa when (i) both are translation invariant, (ii) the MRF is of first order, and (iii) the GRF is defined by a binary, nearest neighbor potential. In both cases, the field (iv) is defined onZ v, and (v) at anyxeZv, takes on one of two states.
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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.
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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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