Publications of 2004

Result of the query in the list of publications :

• 10 Articles
• 4 PhD Thesis and Habilitations
• 18 Conference articles
• 9 Technical and Research Reports

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18 Conference articles

7 - Simultaneous structure and texture compact representation. J.F. Aujol and B. Matei. In Proc. Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, September 2004. @INPROCEEDINGS{jf_acivs, author = {Aujol, J.F. and Matei, B.}, title = {Simultaneous structure and texture compact representation}, year = {2004}, month = {September}, booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems}, address = {Brussels, Belgium}, pdf = {http://www.math.u-bordeaux1.fr/~jaujol/PAPERS/acivscompression.pdf}, keyword = {} }

8 - SAR amplitude probability density function estimation based on a generalized Gaussian scattering model. G. Moser and J. Zerubia and S.B. Serpico. In Proc. SPIE Symposium on Remote Sensing, Maspalomas, Gran Canaria, Spain, September 2004. @INPROCEEDINGS{moser2004a, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {SAR amplitude probability density function estimation based on a generalized Gaussian scattering model}, year = {2004}, month = {September}, booktitle = {Proc. SPIE Symposium on Remote Sensing}, address = {Maspalomas, Gran Canaria, Spain}, url = {http://dx.doi.org/10.1117/12.567853}, keyword = {} }

9 - Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families. G. Moser and J. Zerubia and S.B. Serpico. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage , USA, September 2004. @INPROCEEDINGS{moser2004b, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families}, year = {2004}, month = {September}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, address = {Anchorage , USA}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1368708}, keyword = {} }

10 - Tree Crown Extraction using Marked Point Processes. G. Perrin and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004. Keywords : RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry. @INPROCEEDINGS{perrin04a, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Tree Crown Extraction using Marked Point Processes}, year = {2004}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {University of Technology, Vienna, Austria}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.ps.gz}, keyword = {RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry} } Abstract : In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process. Then, we use a Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels. Results are shown on aerial images of poplars provided by IFN.

11 - Image Disocclusion Using a Probabilistic Gradient Orientation. E. Villéger and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Cambridge, United Kingdom, August 2004. @INPROCEEDINGS{Villeger04, author = {Villéger, E. and Aubert, G. and Blanc-Féraud, L.}, title = {Image Disocclusion Using a Probabilistic Gradient Orientation}, year = {2004}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Cambridge, United Kingdom}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1334034}, keyword = {} }

12 - A Reversible Jump MCMC sampler for building detection in image processing. M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo Methods and Quasi-Monte Carlo Methods, series Special Se, Juan les Pins (France), June 2004. @INPROCEEDINGS{mcmcqmc, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {A Reversible Jump MCMC sampler for building detection in image processing}, year = {2004}, month = {June}, booktitle = {Monte Carlo Methods and Quasi-Monte Carlo Methods}, series = {Special Se}, address = {Juan les Pins (France)}, url = {http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, keyword = {} }

13 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, May 2004. @INPROCEEDINGS{lacoste04a, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, year = {2004}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Montreal, Quebec, Canada}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, keyword = {} }

14 - A $l^1$-unified variational framework for image restoration. J. Bect and L. Blanc-Féraud and G. Aubert and A. Chambolle. In Proc. European Conference on Computer Vision (ECCV), Vol. LNCS 3024, pages 1--13, Ed. T. Pajdla and J. Matas, Publ. Springer, Prague, Czech Republic, May 2004. @INPROCEEDINGS{eccv04, author = {Bect, J. and Blanc-Féraud, L. and Aubert, G. and Chambolle, A.}, title = {A $l^1$-unified variational framework for image restoration}, year = {2004}, month = {May}, booktitle = {Proc. European Conference on Computer Vision (ECCV)}, volume = {LNCS 3024}, pages = {1--13}, editor = {T. Pajdla and J. Matas}, publisher = {Springer}, address = {Prague, Czech Republic}, url = {http://link.springer.com/chapter/10.1007%2F978-3-540-24673-2_1}, keyword = {} }

15 - Deconvolution in confocal microscopy with total variation regularization. N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, May 2004. @INPROCEEDINGS{Dey04b, author = {Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Deconvolution in confocal microscopy with total variation regularization}, year = {2004}, month = {May}, booktitle = {Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, pages = {117--120}, url = {http://www3.jouy.inra.fr/miaj/public/imaste/ssiab2004/program/abw92/}, keyword = {} }

16 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004. Keywords : Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics. @INPROCEEDINGS{cossu04a, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, year = {2004}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Montreal, Canada}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, keyword = {Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics} } Abstract : Although subband histograms of the wavelet coefficients of natural images possess a characteristic leptokurtotic form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, leptokurtotic, and interestingly, in some subbands, multimodal histograms. These subbands are closely linked to the structure of the texture, and guarantee that the most probable image is not flat. Motivated by these observations, we propose a probabilistic model that takes them into account. Adaptive wavelet packet subbands are modelled as Gaussian, generalized Gaussian, or a constrained Gaussian mixture. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling.

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