Results 21 to 30 of about 22,384 (303)
Factorial Markov Random Fields [PDF]
, 2002 In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous to the extension from Hidden Markov Models (HMM's) to Factorial HMM's. We present an efficient EM-based algorithm for inference on Factorial MRF's.
Junhwan Kim, Ramin Zabih
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Combinatorial Markov Random Fields [PDF]
, 2006 A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables.
Bekkerman, R +2 more
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Efficient and Scalable Approach to Equilibrium Conditional Simulation of Gibbs Markov Random Fields [PDF]
EPJ Web of Conferences, 2020 We study the performance of an automated hybrid Monte Carlo (HMC) approach for conditional simulation of a recently proposed, single-parameter Gibbs Markov random field.
Žukovič Milan +1 more
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Bottleneck Potentials in Markov Random Fields [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 We consider general discrete Markov Random Fields(MRFs) with additional bottleneck potentials which penalize the maximum (instead of the sum) over local potential value taken by the MRF-assignment. Bottleneck potentials or analogous constructions have been considered in (i) combinatorial optimization (e.g.
Ahmed Abbas, Paul Swoboda
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Learning loosely connected Markov random fields
Stochastic Systems, 2014 We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm ...
Rui Wu, R. Srikant, Jian Ni
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One-dimensional Markov random fields, Markov chains and topological Markov fields [PDF]
Proceedings of the American Mathematical Society, 2013 15 ...
Marcus, B +4 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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Simulating Lagrangian Subgrid‐Scale Dispersion on Neutral Surfaces in the Ocean
Journal of Advances in Modeling Earth Systems, 2022 To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse ...
Daan Reijnders +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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Markov Random Field Surface Reconstruction [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2010 A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaptation of Markov Random Field regularization of a distance field. The Markov Random Field formulation allows us to integrate both knowledge about the type of surface we wish to reconstruct (the prior) and knowledge about data (the observation model) in an ...
Rasmus R. Paulsen +2 more
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