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IMAGE MODELING BY MARKOV RANDOM FIELDS

High resolution SAR-image classification by Markov random fields and finite mixtures

High resolution SAR-image classification by Markov random fields and finite mixtures

... several image products provided by novel high resolution satellite SAR systems are geocoded ellipsoid-corrected amplitude (intensity) images and because this modality was the main one for several earlier ...

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Unsupervised image segmentation with Gaussian Pairwise Markov Fields

Unsupervised image segmentation with Gaussian Pairwise Markov Fields

... Abstract Modeling strongly correlated random variables is a critical task in the context of latent variable ...Pairwise Markov Field, is presented to generalize existing Markov Fields ...

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Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields

Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields

... (SAR) image processing. This paper focuses on the supervised SAR image classification, which is one of the fundamental SAR image processing ...for modeling the single channel statistics of SAR ...

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Triplet markov trees for image segmentation

Triplet markov trees for image segmentation

... These results first show that, in any cases, the HMT-based segmentation is outperformed by its alternatives. Besides, the HMT/HMF and STMT model yields close error rates, and the achievement of the best average ...

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Online flowchart understanding by combining max-margin Markov random field with grammatical analysis

Online flowchart understanding by combining max-margin Markov random field with grammatical analysis

... are recognized in one step. However, this unified recog- nition framework did not use any statistical learning methods. To improve this method, research combined statistical and structural information [8]. They used de- ...

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Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

... the image by a Legendre transform, D(h) ≤ L(h) , inf q∈R [2 + qh − ζ(q)], and this link enables the practical assessment of multifractal ...fied by considering the coefficients of a polynomial ...

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Content-based Image  Retrieval by Indexing  Random Subwindows with Randomized Trees

Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

... or image retrieval methods that model image appearances locally by using the so- called “local features” ...subwindow random sampling scheme than [8]: square patches of random sizes are ...

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Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

... with image patches is studied in Section 4 by numeri- cal simulations conducted with synthetic multifractal ...values by one order of magnitude, as well as the Bayesian estimators in [3, 15, ...

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Random Galois extensions of Hilbertian fields

Random Galois extensions of Hilbertian fields

... However, if L is not pseudo algebraically closed (e.g. L = K tot,S , when- ever S 6= ∅), then also L[σ] K is never pseudo algebraically closed. Similarly, if Gal(L) is not projective (again for example L = K tot,S with S ...

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Self-similar random fields and rescaled random balls models

Self-similar random fields and rescaled random balls models

... where the intensity measure of N (dρ −1 x, dρ −1 r) is ρ −d dx F ρ (dr). It is natural from this viewpoint to have µ representing an observation window and interpret limits ρ → 0 as zoom-out and limits ρ → ∞ as zoom-in ...

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Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features

Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features

... and Markov random fields ...extracted by the greylevel co-occurrency method, are also integrated in the technique, as they allow to improve the discrimination of urban ...

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Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

... In comparison, other methods that use feature detectors and descriptors, and then perform an exhaustive nearest neighbor search in the database of local fea- tures, are much more time-consuming. For example, in Ref. 21) ...

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Random coverings and self-similar Markov processes

Random coverings and self-similar Markov processes

... a paraˆıtre dans Bernoulli. Enfin, le dernier chapitre ´ etend certains r´ esultats obtenus dans le troisi` eme chapitre. Ce travail peut ˆ etre divis´ e en deux parties : la premi` ere est consacr´ ee ` a la ...

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Pairwise Markov fields for segmentation in astronomical hyperspectral images

Pairwise Markov fields for segmentation in astronomical hyperspectral images

... addressed by dedicated methods within an hypothesis testing framework [7, 2, 1], in which the absence and presence of signal are two competing ...process modeling within a Monte Carlo Markov chain ...

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Weakly dependent random fields with infinite interactions - paru sous le titre "A fixed point approach to model random fields"

Weakly dependent random fields with infinite interactions - paru sous le titre "A fixed point approach to model random fields"

... suprema by integrals in (H2) in order to derive a contrac- tion ...of random fields has been considered in Helson and Lowdenslager (1959) [12]; we adapt this idea in order to relax the previous ...

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Random Subwindows for Robust Image Classification

Random Subwindows for Robust Image Classification

... For comparison, using a single, conventional decision tree instead of an ensemble of trees, we obtain a 19.08% error rate with the second COIL-100 protocol as opposed to 13.58% with extr[r] ...

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Image Variance Based Random Illumination Microscopy

Image Variance Based Random Illumination Microscopy

... I. I NTRODUCTION Super-resolution fluorescence microscopy is an indispens- able tool for studying the dynamics of macromolecules in cell biology. Presently, structured illumination microscopy (SIM) is the best compromise ...

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Directed polymers in random media and multifractal fields

Directed polymers in random media and multifractal fields

... undertaken by Imbrie, Spencer in 1988 ([30]) and carried out by numerous authors ([1],[7],[9],[11],[52],[53]); for an overview of the achieved results, we refer to ...

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Spectrum of Markov generators on sparse random graphs

Spectrum of Markov generators on sparse random graphs

... 1.3. Extremal eigenvalues and the invariant measure. Theorem 1.2 suggests that the bulk of the spectrum of L is concentrated around the value −mn in a two dimensional window of width σ √ n. Actually, it is possible to ...

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marmoteCore: a Markov Modeling Platform

marmoteCore: a Markov Modeling Platform

... Jean-Marie Markov chains, considered as entities richer than simple ma- ...performance modeling, the list of software maintained at [6] is typical of the fact that there is a large variety of solutions ...

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