The Publications

Result of the query in the list of publications :

• 101 Articles
• 31 PhD Thesis and Habilitations
• 245 Conference articles
• 90 Technical and Research Reports
• 14 Collection articles or Books chapters
• 5 Books

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245 Conference articles

239 - On the use of nonlinear regularization in inverse methods for the solar tachocline profile determination. T. Corbard and G. Berthomieu and J. Provost and L. Blanc-Féraud. In Structure and Dynamics of the Interior of the Sun and Sun-like Stars, Vol. ESA SP-418, Ed. S.G. Korzennik & A. Wilson, Publ. ESA Publications Division, Noordwijk, The Netherlands, July 1998. @INPROCEEDINGS{lbf98h, author = {Corbard, T. and Berthomieu, G. and Provost, J. and Blanc-Féraud, L.}, title = {On the use of nonlinear regularization in inverse methods for the solar tachocline profile determination}, year = {1998}, month = {July}, booktitle = {Structure and Dynamics of the Interior of the Sun and Sun-like Stars}, volume = {ESA SP-418}, editor = {S.G. Korzennik & A. Wilson}, publisher = {ESA Publications Division}, address = {Noordwijk, The Netherlands}, url = {http://arxiv.org/abs/astro-ph/9806359}, keyword = {} }

240 - Preprocessing of fMR Datasets. F. Kruggel and X. Descombes and Y. von Cramon. In Proc. IEEE Workshop on Biomedical Image Analysi, pages 211-220, Los Alamitos, USA, June 1998. @INPROCEEDINGS{descombes98c, author = {Kruggel, F. and Descombes, X. and von Cramon, Y.}, title = {Preprocessing of fMR Datasets}, year = {1998}, month = {June}, booktitle = {Proc. IEEE Workshop on Biomedical Image Analysi}, pages = {211-220}, address = {Los Alamitos, USA}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=692518}, keyword = {} }

241 - A step toward high resolution 3D SAR. B. Pairault and M. Berthod. In Proc. European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, May 1998. @INPROCEEDINGS{berthod98, author = {Pairault, B. and Berthod, M.}, title = {A step toward high resolution 3D SAR}, year = {1998}, month = {May}, booktitle = {Proc. European Conference on Synthetic Aperture Radar}, address = {Friedrichshafen, Germany}, keyword = {} }

242 - The two-dimensional Wold decomposition for segmentation and indexing in image libraries. R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998. @INPROCEEDINGS{stoica98b, author = {Stoica, R. and Zerubia, J. and Francos, J.M.}, title = {The two-dimensional Wold decomposition for segmentation and indexing in image libraries}, year = {1998}, month = {May}, 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=678151}, keyword = {} }

243 - Denoising by extracting fractional order singularities. H. Shekarforoush and J. Zerubia and M. Berthod. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998. @INPROCEEDINGS{jz98a, author = {Shekarforoush, H. and Zerubia, J. and Berthod, M.}, title = {Denoising by extracting fractional order singularities}, year = {1998}, month = {May}, 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 = {} }

244 - Die Vorerarbeitung von fMRI-Daten. F. Kruggel and X. Descombes and Y. von Cramon. In Bildverarbeitung für die Medizin, Algorithmen - Systeme - Anwendungen, Universitätsklinikum der RWTH Aachen, Germany, March 1998. @INPROCEEDINGS{descombes98b, author = {Kruggel, F. and Descombes, X. and von Cramon, Y.}, title = {Die Vorerarbeitung von fMRI-Daten}, year = {1998}, month = {March}, booktitle = {Bildverarbeitung für die Medizin, Algorithmen - Systeme - Anwendungen}, address = {Universitätsklinikum der RWTH Aachen, Germany}, pdf = {Articles/Vorerarbeitung.pdf}, keyword = {} }

245 - Fully Bayesian image segmentation-an engineering perspective. R. Morris and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vol. 3, pages 54-57, Santa Barbara, CA, USA, October 1997. Keywords : 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 = {October}, 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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90 Technical and Research Reports

1 - Classification of very high resolution SAR images of urban areas. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Rapport de recherche 7758, INRIA, October 2011. Keywords : 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 = {October}, 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.

2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Research Report 7666, INRIA, July 2011. Keywords : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (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 = {July}, institution = {INRIA}, type = {Research Report}, number = {7666}, url = {http://hal.archives-ouvertes.fr/inria-00605274/en/}, keyword = {Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (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.

3 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model. K. Kayabol and J. Zerubia. Research Report 7700, INRIA, July 2011. Keywords : 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 = {July}, institution = {INRIA}, type = {Research Report}, number = {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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