Publications of 2005

Result of the query in the list of publications :

• 9 Articles
• 2 PhD Thesis and Habilitations
• 13 Conference articles
• 10 Technical and Research Reports
• 1 Collection article or Book chapter

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9 Articles

1 - Detecting codimension-two objects in an image with Ginzburg-Landau models. G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005. Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images. @ARTICLE{laure-ijcv05, author = {Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, title = {Detecting codimension-two objects in an image with Ginzburg-Landau models}, year = {2005}, month = {November}, journal = {International Journal of Computer Vision}, volume = {65}, number = {1-2}, pages = {29-42}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, keyword = {Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} } Abstract : In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its mathematical properties and give some experimental results demonstrating its capability in image processing.

2 - Point Processes for Unsupervised Line Network Extraction in Remote Sensing. C. Lacoste and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, October 2005. @ARTICLE{lacoste05, author = {Lacoste, C. and Descombes, X. and Zerubia, J.}, title = {Point Processes for Unsupervised Line Network Extraction in Remote Sensing}, year = {2005}, month = {October}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {27}, number = {10}, pages = {1568-1579}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=32189&arnumber=1498752&count=18&index=4}, keyword = {} }

3 - Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model. G. Poggi and G. Scarpa and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, August 2005. Keywords : Classification, Segmentation, Markov Fields. @ARTICLE{ieeetgrs_05, author = {Poggi, G. and Scarpa, G. and Zerubia, J.}, title = {Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model}, year = {2005}, month = {August}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {43}, number = {8}, pages = {1901-1911}, pdf = {http://ieeexplore.ieee.org/iel5/36/32001/01487647.pdf?tp=&arnumber=1487647&isnumber=32001}, keyword = {Classification, Segmentation, Markov Fields} }

4 - Dual Norms and Image Decomposition Models. J.F. Aujol and A. Chambolle. International Journal of Computer Vision, 63(1): pages 85-104, June 2005. Keywords : Image decomposition. @ARTICLE{AujolChambolle, author = {Aujol, J.F. and Chambolle, A.}, title = {Dual Norms and Image Decomposition Models}, year = {2005}, month = {June}, journal = {International Journal of Computer Vision}, volume = {63}, number = {1}, pages = {85-104}, pdf = {http://link.springer.com/article/10.1007/s11263-005-4948-3}, keyword = {Image decomposition} }

5 - Invariant Bayesian estimation on manifolds. I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, April 2005. Keywords : Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's. @ARTICLE{jermyn_annstat05, author = {Jermyn, I. H.}, title = {Invariant Bayesian estimation on manifolds}, year = {2005}, month = {April}, journal = {Annals of Statistics}, volume = {33}, number = {2}, pages = {583--605}, url = {http://dx.doi.org/10.1214/009053604000001273}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_annstat05.pdf}, keyword = {Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's} } Abstract : A frequent and well-founded criticism of the maximum em a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter param taking values in a differentiable manifold paramspace is that they are not invariant to arbitrary `reparametrizations' of paramspace. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a em sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and then provides a solution. We first show that the presence of a metric structure on paramspace can be used to define coordinate-invariant MAP and MMSE estimates, and we argue that this is the natural way to proceed. We then discuss the choice of a metric structure on paramspace. 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. In cases of complete prior ignorance, when Jeffreys' prior is used, the invariant MAP estimate reduces to the maximum likelihood estimate. The invariant MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation.

6 - Modeling very Oscillating Signals. Application to Image Processing. G. Aubert and J.F. Aujol. Applied Mathematics and Optimization, 51(2): pages 163--182, March 2005. @ARTICLE{AujolAubert, author = {Aubert, G. and Aujol, J.F.}, title = {Modeling very Oscillating Signals. Application to Image Processing}, year = {2005}, month = {March}, journal = {Applied Mathematics and Optimization}, volume = {51}, number = {2}, pages = {163--182}, pdf = {http://link.springer.com/article/10.1007/s00245-004-0812-z}, keyword = {} }

7 - Optimal Partitions, Regularized Solutions, and Application to Image Classification. G. Aubert and J.F. Aujol. Applicable Analysis, 84(1): pages 15--35, January 2005. @ARTICLE{AujolAubertclassif, author = {Aubert, G. and Aujol, J.F.}, title = {Optimal Partitions, Regularized Solutions, and Application to Image Classification}, year = {2005}, month = {January}, journal = {Applicable Analysis}, volume = {84}, number = {1}, pages = {15--35}, pdf = {http://www.math.u-bordeaux1.fr/~jaujol/HDR/A2.pdf}, keyword = {} }

8 - Image Decomposition into a Bounded Variation Component and an Oscillating Component. J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, January 2005. @ARTICLE{BLA05, author = {Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, title = {Image Decomposition into a Bounded Variation Component and an Oscillating Component}, year = {2005}, month = {January}, journal = {Journal of Mathematical Imaging and Vision}, volume = {22}, number = {1}, pages = {71--88}, pdf = {http://link.springer.com/article/10.1007/s10851-005-4783-8}, keyword = {} }

9 - Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005. Keywords : 3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images. Copyright : SFPT @ARTICLE{lafarge_sfpt05, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution}, year = {2005}, journal = {Revue Française de Photogrammétrie et de Télédétection (SFPT)}, volume = {180}, pages = {4--12}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_sfpt05.pdf}, keyword = {3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images} }

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2 PhD Thesis and Habilitations

1 - Constance de largeur et désocclusion dans les images digitales. E. Villéger. PhD Thesis, Universite de Nice Sophia Antipolis, December 2005. @PHDTHESIS{villeger_these, author = {Villéger, E.}, title = {Constance de largeur et désocclusion dans les images digitales}, year = {2005}, month = {December}, school = {Universite de Nice Sophia Antipolis}, pdf = { http://www-sop.inria.fr/dias/Theses/phd-12.php}, keyword = {} }

2 - Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection. M. Rochery. PhD Thesis, Universite de Nice Sophia Antipolis, Sophia Antipolis, September 2005. Keywords : Active contour, Higher-order, Phase Field, Line networks, Road network. @PHDTHESIS{rochery_these, author = {Rochery, M.}, title = {Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection}, year = {2005}, month = {September}, school = {Universite de Nice Sophia Antipolis}, address = {Sophia Antipolis}, pdf = {http://hal.inria.fr/docs/00/04/86/28/PDF/tel-00010631.pdf}, keyword = {Active contour, Higher-order, Phase Field, Line networks, Road network} }

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13 Conference articles

1 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application. G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005. Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC. Copyright : Springer Verlag @INPROCEEDINGS{perrin_emmcvpr05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, year = {2005}, month = {November}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {St Augustine, Florida, USA}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, keyword = {Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} } Abstract : We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects. We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels.

2 - Phase field models and higher-order active contours. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005. Keywords : Active contour, Higher-order, Shape, Line networks, Road network, Phase Field. @INPROCEEDINGS{rochery_iccv05, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Phase field models and higher-order active contours}, year = {2005}, month = {October}, booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)}, address = {Beijing, China}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_iccv05.pdf}, keyword = {Active contour, Higher-order, Shape, Line networks, Road network, Phase Field} } Abstract : The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a `phase field' function. The function is not constrained, eg to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce `higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points. In addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images.

3 - Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares. F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu-Marni. In Proc. GRETSI Symposium on Signal and Image Processing, Louvain-la-Neuve, Belgique, September 2005. Keywords : Rare event, Forest fires, Gaussian Field. @INPROCEEDINGS{lafarge_gretsi05, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu-Marni, S.}, title = {Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares}, year = {2005}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Louvain-la-Neuve, Belgique}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_gretsi05.pdf}, keyword = {Rare event, Forest fires, Gaussian Field} }

4 - New Higher-order Active Contour Energies for Network Extraction. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Gap closure, Shape, Prior, Higher-order, Active contour. @INPROCEEDINGS{rochery_icip05, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {New Higher-order Active Contour Energies for Network Extraction}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip05.pdf}, keyword = {Gap closure, Shape, Prior, Higher-order, Active contour} } Abstract : Using the framework of higher-order active contours, we present a new quadratic em continuation energy for the extraction of line networks (e.g. road, hydrographic, vascular) in the presence of occlusions. Occlusions create gaps in the data that frequently translate to gaps in the extracted network. The new energy penalizes earby opposing extremities of the network, and thus favours the closure of the gaps created by occlusions. Nearby opposing extremities are identified using a sophisticated interaction between pairs of points on the contour. This new model allows the extraction of fully connected networks, even though occlusions violate common assumptions about the homogeneity of the interior, and high contrast with the exterior, of the network. We present experimental results on real aerial images that demonstrate the effectiveness of the new model for network extraction tasks.

5 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005. @INPROCEEDINGS{lacoste_eusipco05, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, year = {2005}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Antalya, Turkey}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, keyword = {} }

6 - Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods. S. Le Hegarat-Mascle and A. Kallel and X. Descombes. In Proc. SPIE Symposium on Remote Sensing, Vol. 5982, Bruges, Belgium, September 2005. @INPROCEEDINGS{mascle_spie_05, author = {Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, title = {Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods}, year = {2005}, month = {September}, booktitle = {Proc. SPIE Symposium on Remote Sensing}, volume = {5982}, address = {Bruges, Belgium}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=879756}, keyword = {} }

7 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction. F. Lafarge and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas. Copyright : IEEE @INPROCEEDINGS{lafarge_icip05, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_icip05.pdf}, keyword = {Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas} }

8 - Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model. A. Achim and E.E. Kuruoglu and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005. @INPROCEEDINGS{achim_eusipco_05, author = {Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, title = {Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model}, year = {2005}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Antalya, Turkey}, pdf = {http://kilyos.ee.bilkent.edu.tr/~signal/defevent/papers/cr1741.pdf}, keyword = {} }

9 - Texture-adaptive mother wavelet selection for texture analysis. G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Texture, Wavelet packet, Adaptive, Mother. @INPROCEEDINGS{abhayaratne_icip05, author = {Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture-adaptive mother wavelet selection for texture analysis}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, keyword = {Texture, Wavelet packet, Adaptive, Mother} } Abstract : Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length.

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