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Publications de Josiane Zerubia
Résultat de la recherche dans la liste des publications :
173 Articles de conférence |
172 - Denoising by extracting fractional order singularities.
H. Shekarforoush et J. Zerubia et M. Berthod. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, mai 1998.
@INPROCEEDINGS{jz98a,
|
| author |
= |
{Shekarforoush, H. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Denoising by extracting fractional order singularities}, |
| year |
= |
{1998}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=678129}, |
| keyword |
= |
{} |
| } |
|
173 - Fully Bayesian image segmentation-an engineering perspective.
R. Morris et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Vol. 3, pages 54-57, Santa Barbara, CA, USA, octobre 1997.
Mots-clés : Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation.
@INPROCEEDINGS{MorrisICIP97,
|
| author |
= |
{Morris, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{ Fully Bayesian image segmentation-an engineering perspective}, |
| year |
= |
{1997}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| volume |
= |
{3}, |
| pages |
= |
{54-57}, |
| address |
= |
{Santa Barbara, CA, USA}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=631978&isnumber=13718}, |
| keyword |
= |
{Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation} |
| } |
Abstract :
Developments in Markov chain Monte Carlo procedures have made it possible to perform fully Bayesian image segmentation. By this we mean that all the parameters are treated identically, be they the segmentation labels, the class parameters or the Markov random field prior parameters. We perform the analysis by sampling from the posterior distribution of all the parameters. Sampling from the MRF parameters has traditionally been considered if not intractable then at least computationally prohibitive. In the statistics literature there are descriptions of experiments showing that the MRF parameters may be sampled by approximating the partition function. These experiments are all, however, on `toy' problems; for the typical size of image encountered in engineering applications the phase transition behaviour of the models becomes a major limiting factor in the estimation of the partition function. Nevertheless, we show that, with some care, fully Bayesian segmentation can be performed on realistic sized images. We also compare the fully Bayesian approach with the approximate pseudolikelihood method |
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64 Rapports de recherche et Rapports techniques |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de recherche 7758, INRIA, octobre 2011.
Mots-clés : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
@TECHREPORT{RR-7758,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas}, |
| year |
= |
{2011}, |
| month |
= |
{octobre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7758}, |
| url |
= |
{http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
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2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de Recherche 7666, INRIA, juillet 2011.
Mots-clés : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification.
Copyright : INRIA/ARIANA
@TECHREPORT{RR-7666,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
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{Research Report}, |
| number |
= |
{7666}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00605274/en/}, |
| keyword |
= |
{Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification} |
| } |
Résumé :
L'estimation de paramètres de fonctions de densité de probabilité est une étape majeure dans le domaine du traitement statistique du signal et des images. Dans ce rapport, nous étudions les propriétés et les limites de l'estimation de paramètres par la méthode des cumulants logarithmiques (MoLC), qui est une alternative à la fois au maximum de vraisemblance (MV) classique et à la méthode des moments. Nous dérivons la condition générale suffisante de consistance forte de l'estimation par la méthode MoLC, qui représente une propriété asymptotique importante de tout estimateur statistique. Grâce à cela, nous démontrons la consistance forte de l'estimation par la méthode MoLC pour une sélection de familles de distributions particulièrement adaptées (mais non restreintes) au traitement d'images acquises par radar à synthèse d'ouverture (RSO). Nous dérivons ensuite les conditions analytiques d'applicabilité de la méthode MoLC à des échantillons générés qui suivent les lois des différentes familles de distribution de notre sélection. Enfin, nous testons la méthode MoLC sur des données synthétiques et réelles afin de comparer les différentes propriétés inhérentes aux différents types d'images, l'applicabilité de la méthode et les effets d'un nombre restreint d'échantillons. Nous avons, en particulier, considéré les distributions gamma généralisée et K. Comme exemple d'application, nous avons réalisé des classifications supervisées d'images médicales à ultrason ainsi que d'images de télédétection acquises par des capteurs RSO. Les résultats obtenus montrent que la méthode MoLC est une bonne alternative à la méthode des moments, bien qu'elle contienne certaines limitations. Elle est particulièrement utile lorsqu'une approche directe par MV n'est pas possible. |
Abstract :
Parameter estimation of probability density functions is one of the major steps in the mainframe of statistical image and signal processing. In this report we explore the properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition of strong consistency of MoLC estimates which represents an important asymptotic property of any statistical estimator. With its help we demonstrate the strong consistency of MoLC estimates for a selection of widely used distribution families originating (but not restricted to) synthetic aperture radar (SAR) image processing. We then derive the analytical conditions of applicability of MoLC to samples generated from several distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K family of distributions. Supervised image classification experiments are considered for medical ultrasound and remote sensing SAR imagery. The obtained results suggest MoLC to be a feasible yet not universally applicable alternative to MoM that can be considered when the direct ML approach turns out to be unfeasible. |
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3 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model.
K. Kayabol et J. Zerubia. Rapport de Recherche 7700, INRIA, juillet 2011.
Mots-clés : High resolution SAR, Classification, Texture.
@TECHREPORT{Kayabol11,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
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= |
{INRIA}, |
| type |
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{Research Report}, |
| number |
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{7700}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00612491/fr/}, |
| keyword |
= |
{High resolution SAR, Classification, Texture} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.
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4 - Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution .
S. Ben Hadj et F. Chatelain et X. Descombes et J. Zerubia. Rapport de recherche 7350, INRIA, juillet 2010.
Mots-clés : Processus ponctuels marques, RJMCMC, Recuit Simule, EM Stochastique (SEM), pseudo-vraisemblance, Extraction d'objets.
@TECHREPORT{RR-7350,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution }, |
| year |
= |
{2010}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7350}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00508431/fr/}, |
| keyword |
= |
{Processus ponctuels marques, RJMCMC, Recuit Simule, EM Stochastique (SEM), pseudo-vraisemblance, Extraction d'objets} |
| } |
|
5 - Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach.
C. Benedek et X. Descombes et J. Zerubia. Rapport de Recherche 7143, INRIA, Sophia Antipolis, décembre 2009.
Mots-clés : Change detection, Building extraction, Processus ponctuels marques, MAP, multiple birth-and-death dynamics.
@TECHREPORT{benedekRR_09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, |
| year |
= |
{2009}, |
| month |
= |
{décembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7143}, |
| address |
= |
{Sophia Antipolis}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00426615}, |
| keyword |
= |
{Change detection, Building extraction, Processus ponctuels marques, MAP, multiple birth-and-death dynamics} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. |
Abstract :
In this report we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features. |
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6 - Space non-invariant point-spread function and its estimation in fluorescence microscopy.
P. Pankajakshan et L. Blanc-Féraud et Z. Kam et J. Zerubia. Research Report 7157, INRIA, décembre 2009.
Mots-clés : Confocal Laser Scanning Microscopy, point spread function, Estimation bayesienne, Estimation MAP, Deconvolution, fluorescence microscopy.
@TECHREPORT{ppankajakshan09c,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Space non-invariant point-spread function and its estimation in fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{décembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7157}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00438719/en/}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, point spread function, Estimation bayesienne, Estimation MAP, Deconvolution, fluorescence microscopy} |
| } |
Résumé :
Dans ce rapport de recherche, nous rappelons brièvement comment la nature limitée de diffraction de l'objectif d'un microscope optique, et le bruit
intrinsèque peuvent affecter la résolution d'une image observée. Un algorithme de déconvolution aveugle a été proposé en vue de restaurer les fréquences manquants au delà de la limite de diffraction. Cependant, sous d'autres conditions, l'approximation du systéme imageur l'imagerie sans aberration n'est plus valide et donc les aberrations de la phase du front d'onde émergeant d'un médium ne sont plus ignorées. Dans la deuxième partie de
ce rapport de recherche, nous montrons que la distribution d'intensité originelle et la localisation d'un objet peuvent être retrouvées uniquement en obtenant de la phase du front d'onde
réfracté, à partir d'images d'intensité observées. Nous démontrons cela par obtention de la fonction de ou a partir d'une microsphère imagée. Le bruit et l'infl uence de la taille de la
microsphère peuvent être diminués et parfois complètement supprimes des images observées en utilisant un estimateur maximum a posteriori. Néanmoins, a cause de l'incohérence du système d'acquisition, une récupération de phase a partir d'intensités observées n'est possible que si la restauration de la phase est contrainte. Nous avons utilisé l'optique géométrique
pour modéliser la phase du front d'onde réfracté, et nous avons teste l'algorithme sur des images simulées. |
Abstract :
In this research report, we recall briefly how the diffraction-limited nature of an optical microscope's objective, and the intrinsic noise can affect the observed images' resolution. A blind deconvolution algorithm can restore the lost frequencies beyond the diffraction limit. However, under other imaging conditions, the approximation of aberration-free imaging, is not applicable, and the phase aberrations of the emerging wavefront from a specimen immersion medium cannot be ignored any more. We show that an object's location and its original intensity distribution can be recovered by retrieving the refracted wavefront's phase from the observed intensity images. We demonstrate this by retrieving the point-spread function from an imaged microsphere. The noise and the influence of the microsphere size can be mitigated and sometimes completely removed from the observed images by using a maximum a posteriori estimate. However, due to the incoherent nature of the acquisition system, phase retrieval from the observed intensities will be possible only if the phase is constrained. We have used geometrical optics to model the phase of the refracted wavefront, and tested the algorithm on some simulated images. |
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7 - High resolution SAR-image classification.
V. Krylov et J. Zerubia. Research Report 7108, INRIA, novembre 2009.
Mots-clés : SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula.
Copyright : INRIA/ARIANA, 2009
@TECHREPORT{RR-7108,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{High resolution SAR-image classification}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7108}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00433036/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/44/81/40/PDF/RR-7108.pdf}, |
| keyword |
= |
{SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle approche pour la classification des images de type Radar à Synthèse d’Ouverture (RSO) haute résolution. Cette approche combine la méthode des champs Markoviens (MRF) pour la classification bayésienne et un modèle de mélange fini pour l’estimation des densités de probabilité. Ce modèle de mélange fini est realisé grace à une approche fondée sur une espérance-maximisation stochastique, à partir d'un dictionnaire, pour l’estimation des densités de probabilité d’amplitude. Cette approche semi-automatique est étendue au cas important des images RSO avec plusieurs polarisations, en utilisant des copulas pour modéliser les distributions jointes. Des résultats expérimentaux, sur plusieurs images RSO réelles (Dual-Pol TerraSAR-X et Single-Pol COSMO-SkyMed), pour la classification de zones humides, sont présentés pour montrer l’efficacité de l’algorithme proposé. |
Abstract :
In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
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8 - Modeling the statistics of high resolution SAR images.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Research Report 6722, INRIA, novembre 2008.
Mots-clés : Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM).
Copyright : INRIA/ARIANA, 2008
@TECHREPORT{krylovDSEM08,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Modeling the statistics of high resolution SAR images}, |
| year |
= |
{2008}, |
| month |
= |
{novembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6722}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00342681/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/35/76/27/PDF/RR-6722.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM)} |
| } |
Abstract :
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images. |
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