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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.
G R, Cross, A K, Jain
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Modeling Stereopsis via Markov Random Field
Neural Computation, 2010Markov 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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2004
For decades, Markov random fields (MRF) have been used by statistical physicists to explain various phenomena occurring among neighboring particles because of their ability to describe local interactions between them. In Winkler (1995) and Bremaud (1999), an MRF model is used to explain why neighboring particles are more likely to rotate in the same ...
Ting Chen, Dimitris Metaxas
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For decades, Markov random fields (MRF) have been used by statistical physicists to explain various phenomena occurring among neighboring particles because of their ability to describe local interactions between them. In Winkler (1995) and Bremaud (1999), an MRF model is used to explain why neighboring particles are more likely to rotate in the same ...
Ting Chen, Dimitris Metaxas
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The hierarchical random field Ising model
Journal of Statistical Physics, 1988zbMATH 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, 1989Image 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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Random-field quantum spherical ferroelectric model
Journal of Mathematical Physics, 2004We study a (quenched) random-field quantum model of an anharmonic crystal for displacive structural phase transitions in spherical approximation: the random-field quantum spherical (ferroelectric) model. For stationary ergodic random fields its behavior depends on the quantum parameter of the model and on the expectation and covariance of the field. If
Gruber, Christian +1 more
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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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The Infinite Hidden Markov Random Field Model
IEEE Transactions on Neural Networks, 2009Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation
Tsechpenakis, Gabriel +1 more
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Physics Letters A, 1980
Abstract An exactly soluble “spherical” random field model is presented. It is shown that the lowest dimensionality in which a transition takes place is larger than 4, while in the absence of the field it is larger than 2. This is the result of the fact that in the presence of the field the effective propagator behaves like [ B + Aq 2 ] −2 for ...
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Abstract An exactly soluble “spherical” random field model is presented. It is shown that the lowest dimensionality in which a transition takes place is larger than 4, while in the absence of the field it is larger than 2. This is the result of the fact that in the presence of the field the effective propagator behaves like [ B + Aq 2 ] −2 for ...
openaire +1 more source