Publications of 2010

Result of the query in the list of publications :

• 11 Articles
• 1 PhD Thesis and Habilitation
• 17 Conference articles
• 2 Technical and Research Reports
• 2 Collection articles or Books chapters

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17 Conference articles

9 - Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles. C. Benedek and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010. Keywords : Marked point process, multiple birth-and-death dynamics, Building extraction. @INPROCEEDINGS{benedekICPR10, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, year = {2010}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Istanbul, Turkey}, pdf = {http://hal.archives-ouvertes.fr/inria-00481019/en/}, keyword = {Marked point process, multiple birth-and-death dynamics, Building extraction} } Abstract : In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques.

10 - Graph-based Analysis of Textured Images for Hierarchical Segmentation. R. Gaetano and G. Scarpa and T. Sziranyi. In Proc. British Machine Vision Conference (BMVC), Aberystwyth, UK, August 2010. @INPROCEEDINGS{Gaetano2010, author = {Gaetano, R. and Scarpa, G. and Sziranyi, T.}, title = {Graph-based Analysis of Textured Images for Hierarchical Segmentation}, year = {2010}, month = {August}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Aberystwyth, UK}, url = {http://hal.archives-ouvertes.fr/inria-00506596}, keyword = {} } Abstract : The Texture Fragmentation and Reconstruction (TFR) algorithm has beenrecently introduced to address the problem of image segmentationby textural properties, based on a suitable image description toolknown as the Hierarchical Multiple Markov Chain (H-MMC) model. TFRprovides a hierarchical set of nested segmentation maps by firstidentifying the elementary image patterns, and then merging themsequentially to identify complete textures at different scales ofobservation.In this work, we propose a major modification to the TFR by resortingto a graph based description of the image content and a graph clusteringtechnique for the enhancement and extraction of image patterns. Aprocedure based on mathematical morphology will be introduced thatallows for the construction of a color-wise image representationby means of multiple graph structures, along with a simple clusteringtechnique aimed at cutting the graphs and correspondingly segmentgroups of connected components with a similar spatial context.The performance assessment, realized both on synthetic compositionsof real-world textures and images from the remote sensing domain,confirm the effectiveness and potential of the proposed method.

11 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, July 2010. Keywords : multichannel SAR, Classification, probability density function estimation, Markov random field, copula. Copyright : AIP @INPROCEEDINGS{krylovMaxEnt10, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields}, year = {2010}, month = {July}, booktitle = {Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)}, volume = {1305}, pages = {319-326}, address = {Chamonix, France}, url = {http://hal.archives-ouvertes.fr/inria-00495557/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/49/55/57/PDF/krylov_MaxEnt2010.pdf}, keyword = {multichannel SAR, Classification, probability density function estimation, Markov random field, copula} } Abstract : The last decades have witnessed an intensive development and a significant increase of interest to remote sensing, and, in particular, to synthetic aperture radar (SAR) imagery. In this paper we develop a supervised classification approach for medium and high resolution multichannel SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and the Markov random field approach to Bayesian image classification. The finite mixture modeling is done via a recently proposed SAR-specific dictionary-based stochastic expectation maximization approach to class-conditional amplitude probability density function estimation, which is applied separately to all the SAR channels. For modeling the class-conditional joint distributions of multichannel data the statistical concept of copulas is employed, and a dictionary-based copula selection method is proposed. Finally, the Markov random field approach enables to take into account the contextual information and to gain robustness against the inherent noise-like phenomenon of SAR known as speckle. The designed method is an extension and a generalization to multichannel SAR of a recently developed single-channel and Dual-pol SAR image classification technique. The accuracy of the developed multichannel SAR classification approach is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.

12 - Hybrid Multi-view Reconstruction by Jump-Diffusion. F. Lafarge and R. Keriven and M. Brédif and H. Vu. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), San Franscico, U.S., June 2010. @INPROCEEDINGS{lafarge_cvpr10, author = {Lafarge, F. and Keriven, R. and Brédif, M. and Vu, H.}, title = {Hybrid Multi-view Reconstruction by Jump-Diffusion}, year = {2010}, month = {June}, booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, address = {San Franscico, U.S.}, pdf = {http://certis.enpc.fr/publications/papers/CVPR10a.pdf}, keyword = {} }

13 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification. S. Prigent and X. Descombes and D. Zugaj and J. Zerubia. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, June 2010. Keywords : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation. @INPROCEEDINGS{sp01, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, title = {Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, year = {2010}, month = {June}, booktitle = {Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, address = {Reykjavik, Iceland}, pdf = {http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, keyword = {Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} } Abstract : Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.

14 - Hidden fuzzy Markov chain model with K discrete classes. A. Gamal Eldin and Fabien Salzenstein and Christophe Collet. In Information Sciences Signal Processing and their Applications (ISSPA), May 2010. Keywords : hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density. @INPROCEEDINGS{fuzzy_segmentation10, author = {Gamal Eldin, A. and Salzenstein, Fabien and Collet, Christophe}, title = {Hidden fuzzy Markov chain model with K discrete classes}, year = {2010}, month = {May}, booktitle = {Information Sciences Signal Processing and their Applications (ISSPA)}, url = {http://hal.inria.fr/hal-00616372}, keyword = {hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density} } Abstract : This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data.

15 - Detection and tracking of threats in aerial infrared images by a minimal path approach. G. Aubert and A. Baudour and L. Blanc-Féraud and L. Guillot and Y. Le Guilloux. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, Texas, USA, March 2010. @INPROCEEDINGS{ICASSP10, author = {Aubert, G. and Baudour, A. and Blanc-Féraud, L. and Guillot, L. and Le Guilloux, Y.}, title = {Detection and tracking of threats in aerial infrared images by a minimal path approach}, year = {2010}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Dallas, Texas, USA}, pdf = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5495518}, keyword = {} }

16 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, January 2010. Keywords : Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics. Copyright : Copyright 2010 by SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE Electronic Imaging 2010 Conference in San Jose, USA, and is made available as an electronic reprint with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. @INPROCEEDINGS{Kulikova10a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours}, year = {2010}, month = {January}, booktitle = {Proc. IS&T/SPIE Electronic Imaging}, address = {San Jose, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/46/54/72/PDF/Kulikova_SPIE2010.pdf}, keyword = {Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics} } Abstract : We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. From an alternative point of view, the approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined on the configuration space of an arbitrary number of objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data.

17 - High resolution SAR-image classification by Markov random fields and finite mixtures. G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010), Vol. 7533, pages 753308, San Jose, USA, January 2010. Keywords : SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula. Copyright : SPIE @INPROCEEDINGS{moserSPIE2010a, author = {Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, title = {High resolution SAR-image classification by Markov random fields and finite mixtures}, year = {2010}, month = {January}, booktitle = {Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010)}, volume = {7533}, pages = {753308}, address = {San Jose, USA}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776565}, pdf = {http://hal.archives-ouvertes.fr/inria-00442348/en/}, keyword = {SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula} } Abstract : In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed.

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2 Technical and Research Reports

1 - Complex wavelet regularization for 3D confocal microscopy deconvolution. M. Carlavan and L. Blanc-Féraud. Research Report 7366, INRIA, August 2010. Keywords : 3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique. @TECHREPORT{RR-7366, author = {Carlavan, M. and Blanc-Féraud, L.}, title = {Complex wavelet regularization for 3D confocal microscopy deconvolution}, year = {2010}, month = {August}, institution = {INRIA}, type = {Research Report}, number = {7366}, url = {http://hal.inria.fr/inria-00509447/fr/}, keyword = {3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique} } Abstract : Confocal microscopy is an increasingly popular technique for 3D imaging of biological specimens which gives images with a very good resolution (several tenths of micrometers), even though degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, some of them being regularized on a Total Variation prior, which gives good results in image restoration but does not allow to retrieve the thin details (including the textures) of the specimens. We rst propose here to use instead a wavelet prior based on the Dual-Tree Complex Wavelet transform to retrieve the thin details of the object. As the regularizing prior eciency also depends on the choice of its regularizing parameter, we secondly propose a method to select the regularizing parameter following a discrepancy principle for Poisson noise. Finally, in order to implement the proposed deconvolution method, we introduce an algorithm based on the Alternating Direction technique which allows to avoid inherent stability problems of the Richardson-Lucy multiplicative algorithm which is widely used in 3D image restoration. We show some results on real and synthetic data, and compare these results to the ones obtained with the Total Variation and the Curvelets priors. We also give preliminary results on a modication of the wavelet transform allowing to deal with the anisotropic sampling of 3D confocal images.

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