Publications of 2002

Result of the query in the list of publications :

• 4 Articles
• 2 PhD Thesis and Habilitations
• 12 Conference articles
• 9 Technical and Research Reports
• 2 Collection articles or Books chapters

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12 Conference articles

5 - Evaluation Methodologies for Image Retrieval Systems. I. H. Jermyn and C. Shaffrey and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002. @INPROCEEDINGS{shaffreyij, author = {Jermyn, I. H. and Shaffrey, C. and Kingsbury, N.}, title = {Evaluation Methodologies for Image Retrieval Systems}, year = {2002}, month = {September}, booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems}, address = {Ghent, Belgique}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002final.pdf}, keyword = {} }

6 - Satellite and aerial image deconvolution using an EM method with complex wavelets. A. Jalobeanu and R. Nowak and J. Zerubia and M. Figueiredo. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002. @INPROCEEDINGS{nowakjalojz, author = {Jalobeanu, A. and Nowak, R. and Zerubia, J. and Figueiredo, M.}, title = {Satellite and aerial image deconvolution using an EM method with complex wavelets}, year = {2002}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1038028}, keyword = {} }

7 - Image processing for high resolution satellite and aerial data. J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, September 2002. @INPROCEEDINGS{jzEusipco, author = {Zerubia, J.}, title = {Image processing for high resolution satellite and aerial data}, year = {2002}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7072252}, keyword = {} }

8 - Unsupervised segmentation of textured satellite and aerial images with Bayesian methods. S. Wilson and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, September 2002. @INPROCEEDINGS{wilsonjz, author = {Wilson, S. and Zerubia, J.}, title = {Unsupervised segmentation of textured satellite and aerial images with Bayesian methods}, year = {2002}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7071914}, keyword = {} }

9 - A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas. G. Rellier and X. Descombes and J. Zerubia and F. Falzon. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{rellierXDfalzon, author = {Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, title = {A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1044850}, keyword = {} }

10 - Object Point Processes for Image Segmentation. S. Drot and X. Descombes and H. Le Men and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{drotXD, author = {Drot, S. and Descombes, X. and Le Men, H. and Zerubia, J.}, title = {Object Point Processes for Image Segmentation}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1048453}, keyword = {} }

11 - A variational approach to one dimensional phase unwrapping. C. Lacombe and P. Kornprobst and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{lacombekronp, author = {Lacombe, C. and Kornprobst, P. and Aubert, G. and Blanc-Féraud, L.}, title = {A variational approach to one dimensional phase unwrapping}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1048426}, keyword = {} }

12 - Estimation of blur and noise parameters in remote sensing. A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Orlando, USA, May 2002. @INPROCEEDINGS{jallbfjz, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Estimation of blur and noise parameters in remote sensing}, year = {2002}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Orlando, USA}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5745429}, keyword = {} }

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9 Technical and Research Reports

1 - Supervised Classification for Textured Images. J.F. Aujol and G. Aubert and L. Blanc-Féraud. Research Report 4640, Inria, France, November 2002. Keywords : Texture, Classification, Wavelets, Partial differential equation, Level sets. @TECHREPORT{4640, author = {Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, title = {Supervised Classification for Textured Images}, year = {2002}, month = {November}, institution = {Inria}, type = {Research Report}, number = {4640}, address = {France}, url = {https://hal.inria.fr/inria-00071945}, pdf = {https://hal.inria.fr/file/index/docid/71945/filename/RR-4640.pdf}, ps = {https://hal.inria.fr/docs/00/07/19/45/PS/RR-4640.ps}, keyword = {Texture, Classification, Wavelets, Partial differential equation, Level sets} } Résumé : Dans ce rapport, nous présentons un modèle de classification supervisée basé sur une approche variationnelle. Ce modèle s'applique spécifiquement aux images texturées. Nous souhaitons obtenir une partition optimale de l'image constituée de textures séparées par des interfaces régulières. Pour cela, nous représentons les régions définies par les classes ainsi que leurs interfaces par des fonctions d'ensemble de niveaux. Nous définissons une fonctionnelle sur ces ensembles de niveaux dont le minimum est une partition optimale. Cette fonctionnelle comporte en particulier un terme d'attache aux données spécifique aux textures. Nous utilisons une transformée en paquets d'ondelettes pour analyser les textures, ces dernières étant caractérisées par la distribution de leur énergie dans chaque sous-bande de la décompositon. Les équations aux dérivées partielles (EDP) relatives à la minimisation de la fonctionnelle sont couplées et plongées dans un schéma dynamique. En fixant un ensemble de niveaux initial, les différents termes des EDP guident l'évolution des interfaces (ensemble de niveau zéro) vers les frontières de la partion optimale, par le biais de forces externes (régularité de l'interface) et internes (attache aux données et contraintes partition). Nous avons effectué des tests sur des images synthétiques et sur des images réelles. Abstract : In this report, we present a supervised classification model based on a variational approach. This model is specifically devoted to textured images. We want to get an optimal partition of an image which is composed of textures separated by regular interfaces. To reach this goal, we represent the regions defined by the classes as well as their interfaces by level set functions. We define a functional on these level sets whose minimizers define an optimal partition. In particular, this functional owns a data term specific to textures. We use a packet wavelet transform to analyze the textures, these ones being characterized by their energy distribution in each sub-band of the decomposition. The partial differential equations (PDE) related to the minimization of the functional are embeded in a dynamical scheme. Given an initial interface set (zero level set), the different terms of the PDE's govern the motion of interfaces such that, at convergence, we get an optimal partition as defined above. Each interface is guided by external forces (regularity of the interface), and internal ones (data term and partition constraints). We have conducted several experiments on both synthetic and real images.

2 - On Bayesian Estimation in Manifolds. I. H. Jermyn. Research Report 4607, Inria, France, November 2002. Keywords : Rare event, Bayesian estimation, Invariant. @TECHREPORT{4607, author = {Jermyn, I. H.}, title = {On Bayesian Estimation in Manifolds}, year = {2002}, month = {November}, institution = {Inria}, type = {Research Report}, number = {4607}, address = {France}, url = {https://hal.inria.fr/inria-00071978}, pdf = {https://hal.inria.fr/file/index/docid/71978/filename/RR-4607.pdf}, ps = {https://hal.inria.fr/docs/00/07/19/78/PS/RR-4607.ps}, keyword = {Rare event, Bayesian estimation, Invariant} } Résumé : Il est fréquemment dit que les estimées au sens du maximum a posteriori (MAP) et du minimum de l'erreur quadratique moyenne (MMSE) d'un paramètre continu ne sont pas invariantes relativement aux «reparamètrisations» de l'espace des paramètres . Ce rapport clarifie les questions autour de ce problème, en soulignant la différence entre l'invariance aux changements de coordonnées, qui est une condition sine qua non pour un problème mathématiq- uement bien défini, et l'invariance aux difféomorphismes, qui est une question significative, et fournit une solution. On montre d'abord que la présence d'une structure métrique sur peut être utilisée pour définir les estimées aux sens du MAP et du MMSE qui sont invariantes aux changements de coordonnées, et on explique pourquoi cela est la fa on naturelle et nécessaire pour le faire. Le problème de l'estimation et les quantités géométriques qui y sont associées sont tous définis d'une fa on clairement invariante aux changements de coordonnées. On montre que la même estimée au sens du MAP est obtenue en utilisant soit la `maximisation d'une densité' soit une fonction de perte delta, définie de fa on invariante. Puis, on discute le choix d'une métrique pour . En imposant un critère d'invariance qui est naturel dans le cadre bayesien, on montre que ce choix est unique. Il ne correspond pas nécessairement à un choix de coordonnées. L'estimée au sens du MAP qui en résulte coincide avec l'estimée fondée sur la longueur minimum de message (MML), mais la demonstration n'utilise pas de discrétisation ou d'approximation. Abstract : It is frequently stated that the maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter are not invariant to arbitrary «reparametrizations» of the parameter space . This report clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and provides a solution. We first show that the presence of a metric structure on can be used to define coordinate-invari- ant MAP and MMSE estimates, and we argue that this is the natural and necessary way to proceed. The estimation problem and related geometrical quantities are all defined in a manifestly coordinate-invariant way. We show that the same MAP estimate results from `density maximization' or from using an invariantly-defined delta function loss. We then discuss the choice of a metric structure on . By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. The resulting MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation.

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