Results 11 to 20 of about 122,014 (258)
Augmented Gaussian random field: Theory and computation
Discrete and Continuous Dynamical Systems - S, 2022 We propose the novel augmented Gaussian random field (AGRF), which is a universal framework incorporating the data of observable and derivatives of any order. Rigorous theory is established. We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned
Zhang, Sheng +3 more
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Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters [PDF]
, 2012 In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.Instead of using model parameters as texture features, we exploit the variations ...
Michael Bennet +7 more
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Adaptive Gaussian Markov Random Fields with Applications in Human Brain Mapping [PDF]
, 2005 Functional magnetic resonance imaging (fMRI) has become the standard technology in human brain mapping. Analyses of the massive spatio-temporal fMRI data sets often focus on parametric or nonparametric modeling of the temporal component, while spatial ...
Brezger, Andreas +2 more
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Snake based Unsupervised Texture Segmentation using Gaussian Markov Random Field Models [PDF]
, 2011 A functional for unsupervised texture segmentation is investigated in this paper. An auto-normal model based on Markov Random Fields is employed to model textures. The functional investigated here is optimized with respect to the model parameters and the
Mahmoodi, Sasan +3 more
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Correlations and screening of topological charges in Gaussian random fields [PDF]
, 2003 Two-point topological charge correlation functions of several types of geometric singularity in Gaussian random fields are calculated explicitly, using a general scheme: zeros of n-dimensional random vectors, signed by the sign of their Jacobian ...
Dennis, MR +2 more
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The curvature effect in Gaussian random fields
Journal of Physics: Complexity, 2022 Abstract Random field models are mathematical structures used in the study of stochastic complex systems. In this paper, we compute the shape operator of Gaussian random field manifolds using the first and second fundamental forms (Fisher information matrices).
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Gaussian Process Random Fields
CoRR, 2015 Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials.
David A. Moore, Stuart J. Russell
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Ergodicity and Gaussianity for spherical random fields [PDF]
Journal of Mathematical Physics, 2010 We investigate the relationship between ergodicity and asymptotic Gaussianity of isotropic spherical random fields in the high-resolution (or high-frequency) limit. In particular, our results suggest that under a wide variety of circumstances the two conditions are equivalent, i.e., the sample angular power spectrum may converge to the population value
MARINUCCI, DOMENICO, Peccati, G.
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Bayesian mapping of brain regions using compound Markov random field priors [PDF]
, 2003 Human brain mapping, i.e. the detection of functional regions and their connections, has experienced enormous progress through the use of functional magnetic resonance imaging (fMRI).
Gössl, Christoff +2 more
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Learning in Gaussian Markov random fields [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information on the states and improve the quality of the state estimates, it is necessary to learn this unknown
Thomas J. Riedl +2 more
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