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ABC likelihoodfree methods for model choice in Gibbs random fields
, 807
"... Abstract. Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard m ..."
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Cited by 15 (0 self)
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Abstract. Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard
IMPACTS OF A RESOLUTION PYRAMID ON GIBBS RANDOM FIELD CLASSIFICATION
"... In this paper we describe how to extend the Markov/Gibbs random field model used for texture classification to a multiresolution approach in the context of land cover analysis. Our method allows a scaleinvariant analysis of image data by applying single scale classification on different resolution ..."
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In this paper we describe how to extend the Markov/Gibbs random field model used for texture classification to a multiresolution approach in the context of land cover analysis. Our method allows a scaleinvariant analysis of image data by applying single scale classification on different
Stochastic approximation algorithms for partition function estimation of Gibbs random fields
 IEEE Trans. Inform. Theory
, 1997
"... Abstract—We present an analysis of recently proposed Monte Carlo algorithms for estimating the partition function of a Gibbs random field. We show that this problem reduces to estimating one or more expectations of suitable functionals of the Gibbs states with respect to properly chosen Gibbs distri ..."
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Cited by 21 (0 self)
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Abstract—We present an analysis of recently proposed Monte Carlo algorithms for estimating the partition function of a Gibbs random field. We show that this problem reduces to estimating one or more expectations of suitable functionals of the Gibbs states with respect to properly chosen Gibbs
Gibbs Random Field Models: A Toolbox for Spatial Information Extraction
, 2000
"... In this paper, we present Gibbs random field models as a powerful toolbox for spatial information extraction from remote sensing images. These models are defined via an energy function with a certain set of parameters. After shortly revisiting the information theoretical concept and defining a famil ..."
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Cited by 4 (0 self)
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In this paper, we present Gibbs random field models as a powerful toolbox for spatial information extraction from remote sensing images. These models are defined via an energy function with a certain set of parameters. After shortly revisiting the information theoretical concept and defining a
Extracting the local clique set from a Markov/Gibbs random field
"... Introduction: Markov Random Field (MRF) models are used in image restoration [1], region segmentation [2] and texture analysis [3]. However the preferred method of analysis in these applications is to use the equivalent Gibbs Random Field (GRF) model. To obtain this GRF model it is first necessary t ..."
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Introduction: Markov Random Field (MRF) models are used in image restoration [1], region segmentation [2] and texture analysis [3]. However the preferred method of analysis in these applications is to use the equivalent Gibbs Random Field (GRF) model. To obtain this GRF model it is first necessary
Conditional large deviation principle for finite state Gibbs random fields
, 2002
"... Abstract. Let X = {Xt}t∈Zd ∼ P and Y = {Yt}t∈Zd ∼ Q be two independent stationary random fields with finite state spaces. Suppose Y is a Gibbs field with summable potential. Given a random realization x of X, the conditional large deviation principle (LDP) associated with (xt, Yt)t∈Zd are establishe ..."
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Cited by 3 (0 self)
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Abstract. Let X = {Xt}t∈Zd ∼ P and Y = {Yt}t∈Zd ∼ Q be two independent stationary random fields with finite state spaces. Suppose Y is a Gibbs field with summable potential. Given a random realization x of X, the conditional large deviation principle (LDP) associated with (xt, Yt
Miscibility matrices explain the behavior of grayscale textures generated by Gibbs random fields
 in Proc. SPIE Conf. on Intell. Robots and
, 1990
"... This paper describes an original approach tothe analysis and prediction of graylevel textures generated as equilibrium states of Gibbs/Markov random elds. This approach isphysically motivated by the analogy that exists between the graylevel textures and the miscibility patterns of multiphase ows. Th ..."
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Cited by 4 (2 self)
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This paper describes an original approach tothe analysis and prediction of graylevel textures generated as equilibrium states of Gibbs/Markov random elds. This approach isphysically motivated by the analogy that exists between the graylevel textures and the miscibility patterns of multiphase ows
On Approximate PatternMatching for a Class of Gibbs Random Fields
, 2008
"... Abstract: We prove an exponential approximation for the law of approximate occurrence of typical patterns for a class of Gibbsian sources on the lattice Z d, d ≥ 2. From this result, we deduce a law of large numbers and a large deviation result for the waitingtime of distorted patterns. Keywords: ..."
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Abstract: We prove an exponential approximation for the law of approximate occurrence of typical patterns for a class of Gibbsian sources on the lattice Z d, d ≥ 2. From this result, we deduce a law of large numbers and a large deviation result for the waitingtime of distorted patterns. Keywords: hittingtime, exponential law, lossy data compression, law of large numbers, large deviations. 1
Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields
, 1993
"... This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking ..."
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Cited by 9 (2 self)
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This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented
Elogne, Analytic properties and covariance functions for a new class of generalized Gibbs random fields
 IEEE Trans. Inform. Theor
, 2007
"... ar ..."
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