Results 261 to 270 of about 698,300 (306)

Potts model in a random field [PDF]

Physical Review B, 1989
The q-state Potts model in a random field with a discrete distribution of statistically independent fields ordered along any of the q states is studied in mean-field theory.
, Fontanari, , Theumann, , Dominguez
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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.
George R. Cross, Anil K. Jain 0001
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Model fusion of conditional random fields

2007 IEEE International Conference on Systems, Man and Cybernetics, 2007
This paper introduces two model fusion methods on a series of sub-models of Conditional Random Fields (CRFs): majority voting and feature fusion. The former performs on the results of each participant without any consideration about the underlying details of each sub-model, and the latter takes place on feature level to produce modified feature weights
Lu Li, Xuan Wang 0002, Yanbing Yu, Xiaolong Wang 0001   +3 more
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On the spin – S random field Ising model [PDF]

Journal of Magnetism and Magnetic Materials, 2019
General Spin-S Ising model under the effect of the random field has been investigated within the effective field approximation. Effect of the bimodal distribution as a discrete, and Gaussian distribution as a continuous random field distribution, on the ...
Umit Akinci
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Discriminative Random Fields for Behavior Modeling

2009 WRI World Congress on Computer Science and Information Engineering, 2009
This paper proposed an approach of human behavior modeling based on Discriminative Random Fields. In this model, by introducing the hidden behavior feature functions and time window parameters, the Classical CRFs models was extended to spatio-temporal fields. And feature templates were designed to capture the dynamics of human motions.
Tianyu Huang, Chongde Shi, Fengxia Li
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Metastability in the random-field Ising model

Physical Review B, 1985
Effects of metastability in random-field Ising systems are calculated for domains that are both curved and rough. Villain’s and Bruinsma and Aeppli’s scaling forms for the domain size are obtained from the same approach and the crossover between them is simply explained. Generalizations to random fields with nonzero averages lead to a ‘‘freezing line’’
Andelman, David, Joanny, J.-F.
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On markov models of random fields

Acta Mathematicae Applicatae Sinica, 1987
The 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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The hierarchical random field Ising model

Journal of Statistical Physics, 1988
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bricmont, J., Kupiainen, A.
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Random field models in image analysis

Journal of Applied Statistics, 1989
Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis.
Richard C. Dubes, Anil K. Jain
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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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