Results 1 to 10 of about 169,073 (281)
Deep Randomly-Connected Conditional Random Fields For Image Segmentation
IEEE Access, 2017 The use of Markov random fields (MRFs) is a common approach for performing image segmentation, where the problem is modeled using MRFs that incorporate priors on neighborhood nodes to allow for efficient Maximum a Posteriori inference.
Mohammad Javad Shafiee +2 more
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MCMC generation of cosmological fields far beyond Gaussianity
The Open Journal of Astrophysics, 2021 Structure formation in our Universe creates non-Gaussian random fields that will soon be observed over almost the entire sky by the Euclid satellite, the Vera-Rubin observatory, and the Square Kilometre Array.
Joey R. Braspenning, Elena Sellentin
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Learning in Markov Random Fields with Contrastive Free Energies [PDF]
, 2005 Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function.
Sutton, Charles, Welling, Max
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, 2008 We consider polygonal Markov fields originally introduced by Arak and Surgailis (1989). Our attention is focused on fields with nodes of order two, which can be regarded as continuum ensembles of non-intersecting contours in the plane, sharing a number ...
D. Surgailis +15 more
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Remote Sensing, 2019 Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels.
Vera Andrejchenko +3 more
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MAP entropy estimation: applications in robust image filtering [PDF]
Journal of the European Optical Society-Rapid Publications, 2013 We introduce a new approach for image filtering in a Bayesian framework. In this case the probability density function (pdf) of the likelihood function is approximated using the concept of non-parametric or kernel estimation.
de la Rosa J. I. +7 more
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Sensing Capacity for Markov Random Fields
, 2005 This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder.
Khosla, Pradeep +2 more
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Which graphical models are difficult to learn? [PDF]
, 2009 We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure.
Bento, Jose, Montanari, Andrea
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Texture Modelling with Nested High-order Markov-Gibbs Random Fields
, 2015 Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters.
Gimel'farb, Georgy +2 more
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, 2022 This chapter introduces pairwise Markov random fields (PMRFs), a class of models of which the parameters can be represented as an undirected network. In this undirected network nodes represent variables and edges represent the strength of association between two variables after conditioning on all other variables included in the model.
Epskamp, S. +3 more
openaire +3 more sources