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Gibbs Random Fields: Temperature And Parameter Analysis
 In Proc. ICASSP, pages III.45 III.48
, 1992
"... Gibbs random field (GRF) models work well for synthesizing complex naturallooking image data with a small number of parameters; however, estimation methods for these parameters have a lot of problems. This paper addresses the analysis problem in a new way by examining the role of the temperature pa ..."
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Cited by 4 (2 self)
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Gibbs random field (GRF) models work well for synthesizing complex naturallooking image data with a small number of parameters; however, estimation methods for these parameters have a lot of problems. This paper addresses the analysis problem in a new way by examining the role of the temperature
Modelling composite shapes by gibbs random fields
 In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
, 2011
"... We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shapes simultaneously. This allows to model and recognise complex shapes as spatial com ..."
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Cited by 1 (0 self)
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We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express spatial relations between shape parts and simple shapes simultaneously. This allows to model and recognise complex shapes as spatial
Steganography using gibbs random fields
 Proceedings of the 12th ACM Multimedia & Security Workshop, MM&#38;Sec ’10
, 2010
"... Many steganographic algorithms for empirical covers are designed to minimize embedding distortion. In this work, we provide a general framework and practical methods for embedding with an arbitrary distortion function that does not have to be additive over pixels and thus can consider interactions a ..."
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Cited by 3 (1 self)
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among embedding changes. The framework evolves naturally from a parallel made between steganography and statistical physics. The Gibbs sampler is the key tool for simulating the impact of optimal embedding and for constructing practical embedding algorithms. The proposed framework reduces the design
Spartan Gibbs Random Field Models For Geostatistical Applications
, 2003
"... The inverse problem of determining the spatial dependence of random fields from an experimental sample is a central issue in Geostatistics. We propose a computationally efficient approach based on Spartan Gibbs random fields. Their probability density function is determined by a small set of paramet ..."
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Cited by 10 (7 self)
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The inverse problem of determining the spatial dependence of random fields from an experimental sample is a central issue in Geostatistics. We propose a computationally efficient approach based on Spartan Gibbs random fields. Their probability density function is determined by a small set
Gibbs Random Fields, CoOccurrences, and Texture Modeling
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1993
"... : Gibbs random field (GRF) models and cooccurrence statistics are typically considered as separate but useful tools for texture discrimination. In this paper we show an explicit relationship between cooccurrences and a large class of GRF's. This result comes from a new framework based on a se ..."
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Cited by 38 (2 self)
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: Gibbs random field (GRF) models and cooccurrence statistics are typically considered as separate but useful tools for texture discrimination. In this paper we show an explicit relationship between cooccurrences and a large class of GRF's. This result comes from a new framework based on a
Stochastic Simulation Techniques for Partition Function Approximation of Gibbs Random Field Images
"... Abstract A MonteCarlo simulation technique for the calculation of the partition function of a general Gibbs rsndom field is prcsntced. We sho, that the partition function of a general Gibbs random field is equivalent to an expectation. This observation allows us to develop an importance sampling ..."
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Abstract A MonteCarlo simulation technique for the calculation of the partition function of a general Gibbs rsndom field is prcsntced. We sho, that the partition function of a general Gibbs random field is equivalent to an expectation. This observation allows us to develop an importance sampling
GIBBS RANDOM FIELDS WITH UNBOUNDED SPINS ON UNBOUNDED DEGREE GRAPHS
, 904
"... Abstract. Gibbs random fields corresponding to systems of realvalued spins (e.g. systems of interacting anharmonic oscillators) indexed by the vertices of unbounded degree graphs with a certain summability property are constructed. It is proven that the set of tempered Gibbs random fields is nonvo ..."
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Cited by 7 (2 self)
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Abstract. Gibbs random fields corresponding to systems of realvalued spins (e.g. systems of interacting anharmonic oscillators) indexed by the vertices of unbounded degree graphs with a certain summability property are constructed. It is proven that the set of tempered Gibbs random fields is non
Likelihoodfree methods for model choice in Gibbs random fields
 Bayesian Analysis
, 2009
"... Gibbs random fields 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 model choice meth ..."
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Cited by 20 (9 self)
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Gibbs random fields 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 model choice
Bayesian inference for Gibbs random fields using composite likelihoods
 In Simulation Conference (WSC), Proceedings of the 2012 Winter
, 2012
"... Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore nat ..."
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Cited by 3 (1 self)
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Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore
BAYESIAN INFERENCE FOR GIBBS RANDOM FIELDS USING COMPOSITE LIKELIHOODS
"... Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore nat ..."
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Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore
Results 1  10
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1,297,822