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Publications de Josiane Zerubia
Résultat de la recherche dans la liste des publications :
173 Articles de conférence |
102 - Texture analysis using adaptative biorthogonal wavelet packets.
G.C.K. Abhayaratne et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Singapore, octobre 2004.
Mots-clés : Adaptatif, Paquet d'ondelettes, Biorthogonal, Texture, Statistics.
@INPROCEEDINGS{Abhayratne_icip04,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using adaptative biorthogonal wavelet packets}, |
| year |
= |
{2004}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne04icip.pdf}, |
| keyword |
= |
{Adaptatif, Paquet d'ondelettes, Biorthogonal, Texture, Statistics} |
| } |
Abstract :
We discuss the use of adaptive biorthogonal wavelet packet bases
in a probabilistic approach to texture analysis, thus combining the
advantages of biorthogonal wavelets (FIR, linear phase) with those
of a coherent texture model. The computation of the probability
uses both the primal and dual coefcients of the adapted biorthogonal
wavelet packet basis. The computation of the biorthogonal
wavelet packet coefcients is done using a lifting scheme, which
is very efficient. The model is applied to the classification of mosaics
of Brodatz textures, the results showing improvement over
the performance of the corresponding orthogonal wavelets. |
|
103 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, septembre 2004.
@INPROCEEDINGS{lacoste04b,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
| keyword |
= |
{} |
| } |
|
104 - A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry.
M. Ortner et X. Descombes et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, septembre 2004.
Note : this paper has received a Young Authors award
@INPROCEEDINGS{ortner04b,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| note |
= |
{this paper has received a Young Authors award}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7079720}, |
| keyword |
= |
{} |
| } |
|
105 - SAR amplitude probability density function estimation based on a generalized Gaussian scattering model.
G. Moser et J. Zerubia et S.B. Serpico. Dans Proc. SPIE Symposium on Remote Sensing, Maspalomas, Gran Canaria, Spain, septembre 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 |
= |
{septembre}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| address |
= |
{Maspalomas, Gran Canaria, Spain}, |
| url |
= |
{http://dx.doi.org/10.1117/12.567853}, |
| keyword |
= |
{} |
| } |
|
106 - Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families.
G. Moser et J. Zerubia et S.B. Serpico. Dans Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage , USA, septembre 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 |
= |
{septembre}, |
| 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 |
= |
{} |
| } |
|
107 - Tree Crown Extraction using Marked Point Processes.
G. Perrin et X. Descombes et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, septembre 2004.
Mots-clés : RJMCMC, Processus ponctuels marques, Recuit Simule, Extraction de Houppiers, Extraction d'objets, Geometrie stochastique.
@INPROCEEDINGS{perrin04a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| 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, Processus ponctuels marques, Recuit Simule, Extraction de Houppiers, Extraction d'objets, Geometrie stochastique} |
| } |
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. |
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108 - A Reversible Jump MCMC sampler for building detection in image processing.
M. Ortner et X. Descombes et J. Zerubia. Dans Monte Carlo Methods and Quasi-Monte Carlo Methods, series Special Se, Juan les Pins (France), juin 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 |
= |
{juin}, |
| 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 |
= |
{} |
| } |
|
109 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, mai 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 |
= |
{mai}, |
| 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 |
= |
{} |
| } |
|
110 - Deconvolution in confocal microscopy with total variation regularization.
N. Dey et L. Blanc-Féraud et C. Zimmer et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, mai 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 |
= |
{mai}, |
| 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 |
= |
{} |
| } |
|
111 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients.
R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, mai 2004.
Mots-clés : Bimodale, Adaptatif, Paquet d'ondelettes, Texture, Mixture de gaussiennes, 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 |
= |
{mai}, |
| 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 |
= |
{Bimodale, Adaptatif, Paquet d'ondelettes, Texture, Mixture de gaussiennes, 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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