The Publications

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• 101 Articles
• 31 PhD Thesis and Habilitations
• 245 Conference articles
• 90 Technical and Research Reports
• 14 Collection articles or Books chapters
• 5 Books

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101 Articles

1 - Unsupervised amplitude and texture classification of SAR images with multinomial latent model. K. Kayabol and J. Zerubia. IEEE Trans. on Image Processing, 22(2): pages 561-572, February 2013. Keywords : COSMOSkyMed, Classification EM, High resolution SAR, Jensen-Shannon criterion, Classification, Multinomial logistic. @ARTICLE{KorayTIP2013, author = {Kayabol, K. and Zerubia, J.}, title = {Unsupervised amplitude and texture classification of SAR images with multinomial latent model}, year = {2013}, month = {February}, journal = {IEEE Trans. on Image Processing}, volume = {22}, number = {2}, pages = {561-572}, url = {http://hal.inria.fr/hal-00745387}, keyword = {COSMOSkyMed, Classification EM, High resolution SAR, Jensen-Shannon criterion, Classification, Multinomial logistic} }

2 - Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1): pages 33-50, January 2012. Keywords : Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics. Copyright : IEEE @ARTICLE{benedekPAMI11, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics}, year = {2012}, month = {January}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {34}, number = {1}, pages = {33-50}, url = {http://dx.doi.org/10.1109/TPAMI.2011.94}, keyword = {Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics} } Abstract : In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features.

3 - Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. IEEE Geoscience and Remote Sensing Letters, 2012. Note : to appear in 2013 Keywords : Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets. @ARTICLE{Voisin13, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model}, year = {2012}, journal = {IEEE Geoscience and Remote Sensing Letters}, note = {to appear in 2013}, url = {http://dx.doi.org/10.1109/LGRS.2012.2193869}, keyword = {Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets} }

4 - Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, June 2011. Keywords : Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula. Copyright : IEEE @ARTICLE{krylovJSTSP2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas}, year = {2011}, month = {June}, journal = { IEEE Journal of Selected Topics in Signal Processing}, volume = {5}, number = {3}, pages = {554-566}, url = {http://dx.doi.org/10.1109/JSTSP.2010.2103925}, pdf = {http://hal.archives-ouvertes.fr/inria-00562326/en/}, keyword = {Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula} } Abstract : In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene.

5 - An automatic counter for aerial images of aggregations of large birds. S. Descamps and A. Béchet and X. Descombes and A. Arnaud and J. Zerubia. Bird Study, : pages 1-7, June 2011. @ARTICLE{BirdStudy, author = {Descamps, S. and Béchet, A. and Descombes, X. and Arnaud, A. and Zerubia, J.}, title = {An automatic counter for aerial images of aggregations of large birds}, year = {2011}, month = {June}, journal = {Bird Study}, pages = {1-7}, url = {http://hal.inria.fr/inria-00624587}, pdf = {http://www-sop.inria.fr/ariana/Publis/Descamps2011BS.pdf}, keyword = {} }

6 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection. P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, March 2011. Keywords : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing. @ARTICLE{SIIMS_2011, author = {Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, title = {On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, year = {2011}, month = {March}, journal = {SIAM Journal on Imaging Sciences}, volume = {4}, number = {1}, pages = {448-471}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, keyword = {Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} } Abstract : We analyze the illumination invariance of the level lines of an image. We show that if the scene surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition for the level lines to be illumination invariant is that the three-dimensional scene be developable and that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost” invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This allows us to devise a fast and simple algorithm that detects changes between pairs of remotely sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results illustrate the theory developed in this paper. The two real QuickBird images show that the proposed change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on demanding scenes.

7 - Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. IEEE-Geoscience and Remote Sensing Letters, 8(1): pages 148-152, January 2011. Keywords : finite mixture models, parametric estimation, probability-density-function estimation, Stochastic EM (SEM), synthetic aperture radar. Copyright : IEEE @ARTICLE{krylovGRSL2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data}, year = {2011}, month = {January}, journal = {IEEE-Geoscience and Remote Sensing Letters}, volume = {8}, number = {1}, pages = {148-152}, url = {http://dx.doi.org/10.1109/LGRS.2010.2053517}, pdf = {http://hal.archives-ouvertes.fr/inria-00503893/en/}, keyword = {finite mixture models, parametric estimation, probability-density-function estimation, Stochastic EM (SEM), synthetic aperture radar} } Abstract : In this letter, we address the problem of estimating the amplitude probability density function (pdf) of single-channel synthetic aperture radar (SAR) images. A novel flexible method is developed to solve this problem, extending the recently proposed dictionary-based stochastic expectation maximization approach (developed for a medium-resolution SAR) to very high resolution (VHR) satellite imagery, and enhanced by introduction of a novel procedure for estimating the number of mixture components, that permits to reduce appreciably its computational complexity. The specific interest is the estimation of heterogeneous statistics, and the developed method is validated in the case of the VHR SAR imagery, acquired by the last-generation satellite SAR systems, TerraSAR-X and COSMO-SkyMed. This VHR imagery allows the appreciation of various ground materials resulting in highly mixed distributions, thus posing a difficult estimation problem that has not been addressed so far. We also conduct an experimental study of the extended dictionary of state-of-the-art SAR-specific pdf models and consider the dictionary refinements.

8 - Multiple Birth and Cut Algorithm for Multiple Object Detection. A. Gamal Eldin and X. Descombes and Charpiat G. and J. Zerubia. Journal of Multimedia Processing and Technologies, 2011. Keywords : Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting. @ARTICLE{MBC_BP10, author = {Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, title = {Multiple Birth and Cut Algorithm for Multiple Object Detection}, year = {2011}, journal = {Journal of Multimedia Processing and Technologies}, url = {http://hal.inria.fr/hal-00616371}, keyword = {Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting} } Abstract : In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing their respective advantages and drawbacks, where the most important advantage of the MBC is the reduction of number of parameters. We demonstrate that by proposing good candidates throughout the selection phase in the birth step, the speed of convergence is increased. In this selection phase, the best candidates are chosen from object sets by a belief propagation algorithm. We validate our algorithm on the flamingo counting problem in a colony and demonstrate that our algorithm outperforms the MBD algorithm.

9 - A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011. Keywords : Active contour, Marked point process, multiple birth-and-death dynamics, multiple object extraction, Shape prior. @ARTICLE{kulikova_ijcvip2010, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images}, year = {2011}, journal = {International Journal of Computer Vision and Image Processing}, volume = {1}, number = {2}, pages = {1-12}, url = {http://hal.archives-ouvertes.fr/hal-00804118}, keyword = {Active contour, Marked point process, multiple birth-and-death dynamics, multiple object extraction, Shape prior} } Abstract : Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images.

10 - Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires. S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Revue Française de Photogrammétrie et de Télédétection (SFPT), (194): pages 2-15, 2011. Keywords : processus ponctuel marqué, RJMCMC, Simulated Annealing, SEM, pseudo-vraisemblance, extraction d'objet.. @ARTICLE{RFPT_SBH_11, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires}, year = {2011}, journal = {Revue Française de Photogrammétrie et de Télédétection (SFPT)}, number = {194}, pages = {2-15}, url = {http://hal.inria.fr/hal-00638665}, keyword = {processus ponctuel marqué, RJMCMC, Simulated Annealing, SEM, pseudo-vraisemblance, extraction d'objet.} }

11 - A Marked Point Process for Modeling Lidar Waveforms. C. Mallet and F. Lafarge and M. Roux and U. Soergel and F. Bretar and C. Heipke. IEEE Trans. Image Processing, 19(12): pages 3204-3221, December 2010. Keywords : Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model. @ARTICLE{mallet_tip2010, author = {Mallet, C. and Lafarge, F. and Roux, M. and Soergel, U. and Bretar, F. and Heipke, C.}, title = {A Marked Point Process for Modeling Lidar Waveforms}, year = {2010}, month = {December}, journal = {IEEE Trans. Image Processing}, volume = {19}, number = {12}, pages = {3204-3221}, url = {http://dx.doi.org/10.1109/TIP.2010.2052825}, keyword = {Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model} } Abstract : Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based on a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a Reversible Jump Markov Chain Monte Carlo sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.

12 - Geometric Feature Extraction by a Multi-Marked Point Process . F. Lafarge and G. Gimel'farb and X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, September 2010. Keywords : Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing. @ARTICLE{pami09b_lafarge, author = {Lafarge, F. and Gimel'farb, G. and Descombes, X.}, title = {Geometric Feature Extraction by a Multi-Marked Point Process }, year = {2010}, month = {September}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {32}, number = {9}, pages = {1597-1609}, url = {http://dx.doi.org/10.1109/TPAMI.2009.152}, keyword = {Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing} } Abstract : This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency.

13 - A formal Gamma-convergence approach for the detection of points in 2-D biological images. D. Graziani and G. Aubert and L. Blanc-Féraud. SIAM Journal on Imaging Sciences, 3(3): pages 578-594, September 2010. Keywords : points detection, curvature-depending functionals, divergence-measure fields. @ARTICLE{2, author = {Graziani, D. and Aubert, G. and Blanc-Féraud, L.}, title = {A formal Gamma-convergence approach for the detection of points in 2-D biological images}, year = {2010}, month = {September}, journal = {SIAM Journal on Imaging Sciences}, volume = {3}, number = {3}, pages = {578-594}, url = {http://hal.inria.fr/inria-00503152/}, keyword = {points detection, curvature-depending functionals, divergence-measure fields} } Abstract : We propose a new variational model to locate points in 2-dimensional biological images. To this purpose we introduce a suitable functional whose minimizers are given by the points we want to detect. In order to provide numerical experiments we replace this energy with a sequence of a more treatable functionals by means of the notion of Gamma-convergence.

14 - Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons. M. Carlavan and P. Weiss and L. Blanc-Féraud. Traitement du Signal, 27(2): pages 189-219, September 2010. Keywords : Inverse Problems, Regularization, Total variation, Wavelets. @ARTICLE{TSCarlavan2010, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L.}, title = {Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons}, year = {2010}, month = {September}, journal = {Traitement du Signal}, volume = {27}, number = {2}, pages = {189-219}, url = {http://hal.inria.fr/inria-00503050/fr/}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/TS_Carlavan_Weiss_BlancFeraud_2010.pdf}, keyword = {Inverse Problems, Regularization, Total variation, Wavelets} } Résumé : Dans cet article, nous nous intéressons à la régularisation de problèmes inverses reposant sur des critères l1 . Nous séparons ces critères en deux catégories : ceux qui favorisent la régularisation des signaux (à variation totale bornée par exemple) et ceux qui expriment le fait qu'un signal admet une représentation parcimonieuse dans un dictionnaire. Dans une première partie, nous donnons quelques éléments de comparaisons théoriques et pratiques sur les deux a priori, pour aider le lecteur à choisir l'un ou l'autre en fonction de son problème. Pour cette étude, nous utilisons les transformées communément utilisées telles que la variation totale, les ondelettes redondantes ou les curvelets. Dans une deuxième partie, nous proposons un état des lieux des algorithmes de premier ordre adaptés à la minimisation de ces critères.

15 - Variational approximation for detecting point-like target problems. D. Graziani and G. Aubert. COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029, August 2010. Keywords : points detection, Biological images, divergence-measure fields. @ARTICLE{COCV2010, author = {Graziani, D. and Aubert, G.}, title = {Variational approximation for detecting point-like target problems}, year = {2010}, month = {August}, journal = {COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029}, url = {http://dx.doi.org/10.1051/cocv/2010029}, keyword = {points detection, Biological images, divergence-measure fields} } Abstract : The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one.

16 - Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details. F. Lafarge and R. Keriven and M. Brédif. IEEE Trans. Image Processing, 19(7): pages 1683-1694, July 2010. Keywords : 3-D reconstruction, Graph-cut , Shape extraction, urban scenes. @ARTICLE{lafarge_tip2010, author = {Lafarge, F. and Keriven, R. and Brédif, M.}, title = {Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details}, year = {2010}, month = {July}, journal = {IEEE Trans. Image Processing}, volume = {19}, number = {7}, pages = {1683-1694}, url = {http://dx.doi.org/10.1109/TIP.2010.2045695}, keyword = {3-D reconstruction, Graph-cut , Shape extraction, urban scenes} } Abstract : We propose an original hybrid modeling process of urban scenes that represents 3-D models as a combination of mesh-based surfaces and geometric 3-D-primitives. Meshes describe details such as ornaments and statues, whereas 3-D-primitives code for regular shapes such as walls and columns. Starting from an 3-D-surface obtained by multiview stereo techniques, these primitives are inserted into the surface after being detected. This strategy allows the introduction of semantic knowledge, the simplification of the modeling, and even correction of errors generated by the acquisition process. We design a hierarchical approach exploring different scales of an observed scene. Each level consists first in segmenting the surface using a multilabel energy model optimized by -expansion and then in fitting 3-D-primitives such as planes, cylinders or tori on the obtained partition where relevant. Experiments on real meshes, depth maps and synthetic surfaces show good potential for the proposed approach.

17 - Extended Phase Field Higher-Order Active Contour Models for Networks. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, May 2010. Keywords : Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction. @ARTICLE{Peng09, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = { Extended Phase Field Higher-Order Active Contour Models for Networks}, year = {2010}, month = {May}, journal = {International Journal of Computer Vision}, volume = {88}, number = {1}, pages = { 111-128}, url = {http://www.springerlink.com/content/d3641g2227316w58/}, keyword = {Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction} } Abstract : This paper addresses the segmentation from an image of entities that have the form of a ‘network’, i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present new phase field higher-order active contour (HOAC) prior models for network regions, and apply them to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much ‘noise’ in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular.

18 - Unsupervised line network extraction in remote sensing using a polyline process. C. Lacoste and X. Descombes and J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, April 2010. Keywords : Marked point process, Line networks, Road network extraction. @ARTICLE{lacoste10, author = {Lacoste, C. and Descombes, X. and Zerubia, J.}, title = {Unsupervised line network extraction in remote sensing using a polyline process}, year = {2010}, month = {April}, journal = {Pattern Recognition}, volume = {43}, number = {4}, pages = {1631-1641}, url = {http://dx.doi.org/10.1016/j.patcog.2009.11.003}, keyword = {Marked point process, Line networks, Road network extraction} } Abstract : Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one.

19 - Structural approach for building reconstruction from a single DSM. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(1): pages 135-147, January 2010. @ARTICLE{lafarge_pami09, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Structural approach for building reconstruction from a single DSM}, year = {2010}, month = {January}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {32}, number = {1}, pages = {135-147}, url = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.281}, keyword = {} } Abstract : We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D--blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail.

20 - Shape Analysis of Elastic Curves in Euclidean Spaces. S. Joshi and E. Klassen and W. Liu and I. H. Jermyn and A. Srivastava. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(7): pages 1415-1428, 2010. Note : to appear Keywords : shape analysis, elastic deformations, Riemannian elastic metric. @ARTICLE{Joshi2010, author = {Joshi, S. and Klassen, E. and Liu, W. and Jermyn, I. H. and Srivastava, A.}, title = {Shape Analysis of Elastic Curves in Euclidean Spaces}, year = {2010}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {33}, number = {7}, pages = {1415-1428}, note = {to appear}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5601739}, keyword = {shape analysis, elastic deformations, Riemannian elastic metric} }

21 - A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images. P. Cariou and X. Descombes and E. Zhizhina. Problems of Information Transmission, 10(3): pages 247-256, 2010. Keywords : Marked point process, birth and death process, Road network extraction. @ARTICLE{cariou2010, author = {Cariou, P. and Descombes, X. and Zhizhina, E.}, title = {A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images}, year = {2010}, journal = {Problems of Information Transmission}, volume = {10}, number = {3}, pages = {247-256}, url = { http://www.jip.ru/2010/247-256-2010.pdf}, keyword = {Marked point process, birth and death process, Road network extraction} }

22 - Comparative study on the performance of multi paramater SAR data for operational urban areas extraction. C. Corbane and N. Baghdadi and X. Descombes and M. Petit. IEEE-Geoscience and Remote Sensing Letters, 6(4): pages 728-732, October 2009. Keywords : Markov random field model, synthetic aperture radar, urban remote sensing. @ARTICLE{COR-09, author = {Corbane, C. and Baghdadi, N. and Descombes, X. and Petit, M.}, title = {Comparative study on the performance of multi paramater SAR data for operational urban areas extraction}, year = {2009}, month = {October}, journal = {IEEE-Geoscience and Remote Sensing Letters}, volume = {6}, number = {4}, pages = {728-732}, url = {http://dx.doi.org/10.1109/LGRS.2009.2024225}, keyword = {Markov random field model, synthetic aperture radar, urban remote sensing} } Abstract : The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.

23 - Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. IEEE Trans. Image Processing, 18(10): pages 2303-2315, October 2009. Keywords : Change detection, Aerial images, Camera motion, MRF. @ARTICLE{benedekTIP09, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Image Processing}, volume = {18}, number = {10}, pages = {2303-2315}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5089480}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov Random Field model which integrates information from two different features, and ensures connected homogeneous regions in the segmented images. Validation is given on real aerial photos.

24 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model. C. Benedek and T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, October 2009. Keywords : mixed Markov models, Change detection, Aerial images, MAP estimation. Copyright : IEEE @ARTICLE{benedekTGRS09, author = {Benedek, C. and Szirányi, T.}, title = {Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {47}, number = {10}, pages = {3416-3430}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5257398&arnumber=5169964&count=26&index=11}, keyword = {mixed Markov models, Change detection, Aerial images, MAP estimation} } Abstract : In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth.

25 - Looking for shapes in two-dimensional, cluttered point clouds. A. Srivastava and I. H. Jermyn. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(9): pages 1616-1629, September 2009. Keywords : Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{SrivastavaJermyn09, author = {Srivastava, A. and Jermyn, I. H.}, title = {Looking for shapes in two-dimensional, cluttered point clouds}, year = {2009}, month = {September}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {31}, number = {9}, pages = {1616-1629}, url = {http://dx.doi.org/10.1109/TPAMI.2008.223}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/SrivastavaJermyn09.pdf}, keyword = {Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao} } Abstract : We study the problem of identifying shape classes in point clouds. These clouds contain sampled contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learnt from training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: (i) shape variations within classes, (ii) variability in sampling continuous curves, (iii) pose and scale variability, (iv) observation noise, and (v) points introduced by clutter. The variability in sampling closed curves into finite points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their squareroot forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of posterior probabilities of different classes and, hence, Bayesian classification.

26 - Blind deconvoltion for thin layered confocal imaging. P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Applied Optics, 48(22): pages 4437-4448, August 2009. Keywords : Blind Deconvolution, Confocal microscopy, Inverse Problems. Copyright : Optical Society of America @ARTICLE{ppankajakshan09b, author = {Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Blind deconvoltion for thin layered confocal imaging}, year = {2009}, month = {August}, journal = {Applied Optics}, volume = {48}, number = {22}, pages = {4437-4448}, pdf = {http://hal.inria.fr/docs/00/39/55/23/PDF/AppliedOpticsPaperTypesetting.pdf}, keyword = {Blind Deconvolution, Confocal microscopy, Inverse Problems} } Abstract : We propose an alternate minimization algorithm for estimating the point-spread function (PSF) of a confocal laser scanning microscope and the specimen fluorescence distribution. A three-dimensional separable Gaussian model is used to restrict the PSF solution space and a constraint on the specimen is used so as to favor the stabilization and convergence of the algorithm. The results obtained from the simulation show that the PSF can be estimated to a high degree of accuracy, and those on real data show better deconvolution as compared to a full theoretical PSF model.

27 - Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation. G. Scarpa and R. Gaetano and M. Haindl and J. Zerubia. IEEE Trans. on Image Processing, 18(8): pages 1830-1843, August 2009. Keywords : Hierarchical Image Models, Markov Process, Pattern Analysis. @ARTICLE{ScarpaTIP09, author = {Scarpa, G. and Gaetano, R. and Haindl, M. and Zerubia, J.}, title = { Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation}, year = {2009}, month = {August}, journal = {IEEE Trans. on Image Processing}, volume = {18}, number = {8}, pages = {1830-1843}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5161445&arnumber=4914796&count=21&index=11}, keyword = {Hierarchical Image Models, Markov Process, Pattern Analysis} } Abstract : In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.

28 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations. S. Descamps and X. Descombes and A. Béchet and J. Zerubia. Traitement du Signal, 26(2): pages 95-108, July 2009. Keywords : flamants roses. @ARTICLE{flamTS, author = {Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, title = {Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, year = {2009}, month = {July}, journal = {Traitement du Signal}, volume = {26}, number = {2}, pages = {95-108}, pdf = {http://documents.irevues.inist.fr/handle/2042/28809}, keyword = {flamants roses} } Résumé : Nous présentons dans cet article une nouvelle technique de détection de flamants roses sur des images aériennes. Nous considérons une approche stochastique fondée sur les processus ponctuels marqués, aussi appelés processus objets. Ici, les objets représentent les flamants, qui sont modélisés par des ellipses. La densité associée au processus ponctuel marqué d'ellipses est définie par rapport à une mesure de Poisson. Dans un cadre gibbsien, le problème se réduit à la minimisation d'une énergie, qui est constituée d'un terme de régularisation (densité a priori), qui introduit des contraintes sur les objets et leurs interactions; et un terme d'attache aux données, qui permet de localiser sur l'image les flamants à extraire. Nous échantillonnons le processus pour extraire la configuration d'objets minimisant l'énergie grâce à une nouvelle dynamique de Naissances et Morts multiples, amenant finalement à une estimation du nombre total de flamants présents sur l'image. Cette approche donne des comptes avec une bonne précision comparée aux comptes manuels. De plus, elle ne nécessite aucun traitement préalable ou intervention manuelle, ce qui réduit considérablement le temps d'obtention des comptes.

29 - A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Pattern Recognition, 42(5): pages 699-709, May 2009. Keywords : Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian. @ARTICLE{Horvath09, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction}, year = {2009}, month = {May}, journal = {Pattern Recognition}, volume = {42}, number = {5}, pages = {699-709}, url = {http://dx.doi.org/10.1016/j.patcog.2008.09.008}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Horvathetal09.pdf}, keyword = {Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian} } Abstract : We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown number of circles of approximately the same radius. The model has applications to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed using higher-order active contours (HOACs) in order to include non-trivial prior knowledge about region shape without constraining topology. The main theoretical contribution is an analysis of the local minima of the HOAC energy that allows us to guarantee stable circles, fix one of the model parameters, and constrain the rest. We apply the model to tree crown extraction from aerial images of plantations. Numerical experiments both confirm the theoretical analysis and show the empirical importance of the prior shape information.

30 - Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum. X. Descombes and R. Minlos and E. Zhizhina. Journal of Mathematical Imaging and Vision, 33(3): pages 347-359, 2009. Keywords : birth and death process, Marked point process, Object extraction. Copyright : Springer @ARTICLE{DZM08, author = {Descombes, X. and Minlos, R. and Zhizhina, E.}, title = {Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum}, year = {2009}, journal = {Journal of Mathematical Imaging and Vision}, volume = {33}, number = {3}, pages = {347-359}, pdf = {http://dx.doi.org/10.1007/s10851-008-0117-y}, keyword = {birth and death process, Marked point process, Object extraction} } Abstract : We define a new birth and death dynamics dealing with configurations of disks in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in detecting a configuration of objects from a digital image. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data.

31 - Efficient schemes for total variation minimization under constraints in image processing. P. Weiss and L. Blanc-Féraud and G. Aubert. SIAM journal on Scientific Computing, 31(3): pages 2047-2080, 2009. Keywords : Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time. Copyright : Copyright Siam Society for Industrial and Applied @ARTICLE{SIAM_JSC_PWEISS, author = {Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {Efficient schemes for total variation minimization under constraints in image processing}, year = {2009}, journal = {SIAM journal on Scientific Computing}, volume = {31}, number = {3}, pages = {2047-2080}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, keyword = {Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time} }

32 - Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 1(2): pages 139--146, June 2008. Keywords : Dense urban areas, Geographic Information System (GIS), Multiscale, Road network, Variational methods, Very high resolution. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{Peng08b, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = {Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images}, year = {2008}, month = {June}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {1}, number = {2}, pages = {139--146}, url = {http://dx.doi.org/10.1109/JSTARS.2008.922318}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/PengetalTGRS08.pdf}, keyword = {Dense urban areas, Geographic Information System (GIS), Multiscale, Road network, Variational methods, Very high resolution} } Abstract : This paper addresses the problem of updating digital road maps in dense urban areas by extracting the main road network from very high resolution (VHR) satellite images. Building on the work of Rochery et al. (2005), we represent the road region as a 'phase field'. In order to overcome the difficulties due to the complexity of the information contained in VHR images, we propose a multi-scale statistical data model. It enables the integration of segmentation results from coarse resolution, which furnishes a simplified representation of the data, and fine resolution, which provides accurate details. Moreover, an outdated GIS digital map is introduced into the model, providing specific prior knowledge of the road network. This new term balances the effect of the generic prior knowledge describing the geometric shape of road networks (i.e. elongated and of low-curvature) carried by a 'phase field higher-order active contour' term. Promising results on QuickBird panchromatic images and comparisons with several other methods demonstrate the effectiveness of our approach.

33 - Automatic Building Extraction from DEMs using an Object Approach and Application to the 3D-city Modeling. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Journal of Photogrammetry and Remote Sensing, 63(3): pages 365-381, May 2008. Keywords : Building extraction, 3D reconstruction, Digital Elevation Model, Stochastic geometry. @ARTICLE{lafarge_jprs08, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Automatic Building Extraction from DEMs using an Object Approach and Application to the 3D-city Modeling}, year = {2008}, month = {May}, journal = {Journal of Photogrammetry and Remote Sensing}, volume = {63}, number = {3}, pages = {365-381}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_lafarge_jprs08.pdf}, keyword = {Building extraction, 3D reconstruction, Digital Elevation Model, Stochastic geometry} } Abstract : In this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach. First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process.

34 - Gap Filling of 3-D Microvascular Networs by Tensor Voting. L. Risser and F. Plouraboue and X. Descombes. IEEE Trans. Medical Imaging, 27(5): pages 674-687, May 2008. Copyright : @ARTICLE{xavTMI3, author = {Risser, L. and Plouraboue, F. and Descombes, X.}, title = {Gap Filling of 3-D Microvascular Networs by Tensor Voting}, year = {2008}, month = {May}, journal = {IEEE Trans. Medical Imaging}, volume = {27}, number = {5}, pages = {674-687}, pdf = {http://ieeexplore.ieee.org/iel5/42/4497376/04389807.pdf?isnumber=4497376&prod=JNL&arnumber=4389807&arSt=674&ared=687&arAuthor=Risser%2C+L.%3B+Plouraboue%2C+F.%3B+Descombes%2C+X.}, keyword = {} }

35 - A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.. M. Ortner and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 2008. Keywords : Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register. Copyright : @ARTICLE{ortner08, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.}, year = {2008}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, pdf = {http://hal.inria.fr/docs/00/27/88/82/PDF/ortner08.pdf}, keyword = {Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register} }

36 - The Gibbs fields approach and related dynamics in image processing. X. Descombes and E. Zhizhina. Condensed Matter Physics, 11(2(54)): pages 293-312, 2008. Copyright : Institute for Condensed Matter @ARTICLE{LNA08, author = {Descombes, X. and Zhizhina, E.}, title = {The Gibbs fields approach and related dynamics in image processing}, year = {2008}, journal = {Condensed Matter Physics}, volume = {11}, number = {2(54)}, pages = {293-312}, keyword = {} }

37 - Higher-Order Active Contour Energies for Gap Closure. M. Rochery and I. H. Jermyn and J. Zerubia. Journal of Mathematical Imaging and Vision, 29(1): pages 1-20, September 2007. Keywords : Gap closure, Higher-order, Active contour, Shape, Prior, Road network. @ARTICLE{Rochery07, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher-Order Active Contour Energies for Gap Closure}, year = {2007}, month = {September}, journal = {Journal of Mathematical Imaging and Vision}, volume = {29}, number = {1}, pages = {1-20}, url = {http://dx.doi.org/10.1007/s10851-007-0021-x}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Rochery07.pdf}, keyword = {Gap closure, Higher-order, Active contour, Shape, Prior, Road network} } Abstract : One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing ‘nearby opposing extremities’, thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions.

38 - Gaussian approximations of fluorescence microscope point-spread function models. B. Zhang and J. Zerubia and J.C. Olivo-Marin. Applied Optics, 46(10): pages 1819-1829, April 2007. Copyright : © 2007 Optical Society of America @ARTICLE{jz_applied_photo, author = {Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Gaussian approximations of fluorescence microscope point-spread function models}, year = {2007}, month = {April}, journal = {Applied Optics}, volume = {46}, number = {10}, pages = {1819-1829}, keyword = {} } Abstract : We comprehensively study the least-squares Gaussian approximations of the diffraction-limited 2D-3D paraxial-nonparaxial point-spread functions (PSFs) of the wide field fluorescence microscope (WFFM), the laser scanning confocal microscope (LSCM), and the disk scanning confocal microscope (DSCM). The PSFs are expressed using the Debye integral. Under an L∞ constraint imposing peak matching, optimal and near-optimal Gaussian parameters are derived for the PSFs. With an L1 constraint imposing energy conservation, an optimal Gaussian parameter is derived for the 2D paraxial WFFM PSF. We found that (1) the 2D approximations are all very accurate; (2) no accurate Gaussian approximation exists for 3D WFFM PSFs; and (3) with typical pinhole sizes, the 3D approximations are accurate for the DSCM and nearly perfect for the LSCM. All the Gaussian parameters derived in this study are in explicit analytical form, allowing their direct use in practical applications.

39 - Building Outline Extraction from Digital Elevation Models using Marked Point Processes. M. Ortner and X. Descombes and J. Zerubia. International Journal of Computer Vision, 72(2): pages 107-132, April 2007. Keywords : RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM). @ARTICLE{ortner_ijcv_05, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Building Outline Extraction from Digital Elevation Models using Marked Point Processes}, year = {2007}, month = {April}, journal = {International Journal of Computer Vision}, volume = {72}, number = {2}, pages = {107-132}, url = {http://www.springerlink.com/content/d563v16957427102/?p=873bd324c7c14049a45cc1f2905b5a86&pi=0}, keyword = {RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM)} }

40 - ant colony optimization for image regularization based on a non-stationary Markov modeling. S. Le Hegarat-Mascle and A. Kallel and X. Descombes. IEEE Trans. on Image Processing, 16(3): pages 865-878, March 2007. Keywords : Markov Random Fields, Ants colonization. @ARTICLE{Ants07, author = {Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, title = {ant colony optimization for image regularization based on a non-stationary Markov modeling}, year = {2007}, month = {March}, journal = {IEEE Trans. on Image Processing}, volume = {16}, number = {3}, pages = {865-878}, keyword = {Markov Random Fields, Ants colonization} } Abstract : Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images

41 - Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques. F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu. Traitement du Signal, 24(1), 2007. Note : copyright Traitement du Signal Keywords : Gaussian Field, Rare event, DT-caracteristic, Intensity peak. @ARTICLE{lafarge_ts06, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu, S.}, title = {Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques}, year = {2007}, journal = {Traitement du Signal}, volume = {24}, number = {1}, note = {copyright Traitement du Signal}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_ts06.pdf}, keyword = {Gaussian Field, Rare event, DT-caracteristic, Intensity peak} }

42 - Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images. A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. International Journal of Simulation Modelling, 6(2): pages 73--83, 2007. @ARTICLE{Bhattacharya07, author = {Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, title = {Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images}, year = {2007}, journal = {International Journal of Simulation Modelling}, volume = {6}, number = {2}, pages = {73--83}, url = {http://www.ijsimm.com/Full_Papers/Fulltext2007/text6-2_73-83.pdf}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07.pdf}, keyword = {} } Abstract : Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database.

43 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model. A. Achim and E.E. Kuruoglu and J. Zerubia. IEEE Trans. on Image Processing, 15(9): pages 2686-2693, September 2006. Keywords : SAR Images. @ARTICLE{jz_ieee_tr_ip_06, author = {Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, title = {SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model}, year = {2006}, month = {September}, journal = {IEEE Trans. on Image Processing}, volume = {15}, number = {9}, pages = {2686-2693}, pdf = {http://dx.doi.org/10.1109/TIP.2006.877362}, keyword = {SAR Images} } Abstract : Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal

44 - Higher Order Active Contours. M. Rochery and I. H. Jermyn and J. Zerubia. International Journal of Computer Vision, 69(1): pages 27--42, August 2006. Keywords : Active contour, Shape, Higher-order, Prior, Road network. @ARTICLE{mr_ijcv_06, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher Order Active Contours}, year = {2006}, month = {August}, journal = {International Journal of Computer Vision}, volume = {69}, number = {1}, pages = {27--42}, url = {http://dx.doi.org/10.1007/s11263-006-6851-y}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_mr_ijcv_06.pdf}, keyword = {Active contour, Shape, Higher-order, Prior, Road network} } Abstract : We introduce a new class of active contour models that hold great promise for region and shape modelling, and we apply a special case of these models to the extraction of road networks from satellite and aerial imagery. The new models are arbitrary polynomial functionals on the space of boundaries, and thus greatly generalize the linear functionals used in classical contour energies. While classical energies are expressed as single integrals over the contour, the new energies incorporate multiple integrals, and thus describe long-range interactions between different sets of contour points. As prior terms, they describe families of contours that share complex geometric properties, without making reference to any particular shape, and they require no pose estimation. As likelihood terms, they can describe multi-point interactions between the contour and the data. To optimize the energies, we use a level set approach. The forces derived from the new energies are non-local however, thus necessitating an extension of standard level set methods. Networks are a shape family of great importance in a number of applications, including remote sensing imagery. To model them, we make a particular choice of prior quadratic energy that describes reticulated structures, and augment it with a likelihood term that couples the data at pairs of contour points to their joint geometry. Promising experimental results are shown on real images.

45 - SAR amplitude probability density function estimation based on a generalized Gaussian model. G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. on Image Processing, 15(6): pages 1429-1442, June 2006. Keywords : SAR Images, Generalised Gaussians, Mellin transform. Copyright : IEEE @ARTICLE{moser_ieeeip05, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {SAR amplitude probability density function estimation based on a generalized Gaussian model}, year = {2006}, month = {June}, journal = {IEEE Trans. on Image Processing}, volume = {15}, number = {6}, pages = {1429-1442}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1632197}, pdf = {http://hal.archives-ouvertes.fr/inria-00561372/en/}, keyword = {SAR Images, Generalised Gaussians, Mellin transform} } Abstract : In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena.

46 - Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution. N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and P. Roux and J.C. Olivo-Marin and J. Zerubia. Microscopy Research Technique, 69: pages 260-266, April 2006. Keywords : Confocal microscopy, Variational methods, Total variation, Deconvolution. @ARTICLE{dey_mrt_05, author = {Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Roux, P. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution}, year = {2006}, month = {April}, journal = {Microscopy Research Technique}, volume = {69}, pages = {260-266}, url = {http://dx.doi.org/10.1002/jemt.20294}, keyword = {Confocal microscopy, Variational methods, Total variation, Deconvolution} } Abstract : Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results as compared to the unregularized Richardson-Lucy algorithm, both visually and quantitatively.

47 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation. H. Permuter and J.M. Francos and I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, April 2006. Keywords : Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion. @ARTICLE{permuter_pr06, author = {Permuter, H. and Francos, J.M. and Jermyn, I. H.}, title = {A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, year = {2006}, month = {April}, journal = {Pattern Recognition}, volume = {39}, number = {4}, pages = {695--706}, url = {http://dx.doi.org/10.1016/j.patcog.2005.10.028}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, keyword = {Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion} } Abstract : The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'colour' and 'structure' information in order to improve the classification performance. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.

48 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation. G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. Geoscience and Remote Sensing, 44(1): pages 188-200, January 2006. Keywords : SAR Images, Stochastic EM (SEM), Dictionary. Copyright : IEEE @ARTICLE{moser_ieeetgrs_05, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation}, year = {2006}, month = {January}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {44}, number = {1}, pages = {188-200}, url = {http://dx.doi.org/10.1109/TGRS.2005.859349}, pdf = {http://hal.archives-ouvertes.fr/inria-00561369/en/}, keyword = {SAR Images, Stochastic EM (SEM), Dictionary} } Abstract : In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation–maximization iterative methodology with the recently developed “method-of-log-cumulants” for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data.

49 - An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals. G. Aubert and L. Blanc-Féraud and R. March. Nonlinear Analysis, 64: pages 1908-1930, 2006. Keywords : Gamma Convergence, Finite Element, Segmentation. @ARTICLE{laure-na05, author = {Aubert, G. and Blanc-Féraud, L. and March, R.}, title = {An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals}, year = {2006}, journal = {Nonlinear Analysis}, volume = {64}, pages = {1908-1930}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_laure-na05.pdf}, keyword = {Gamma Convergence, Finite Element, Segmentation} } Abstract : We show the Gamma-convergence of a family of discrete functionals to the Mumford and Shah image segmentation functional. The functionals of the family are constructed by modifying the elliptic approximating functionals proposed by Ambrosio and Tortorelli. The quadratic term of the energy related to the edges of the segmentation is replaced by a nonconvex functional.

50 - Automatic building 3D reconstruction from DEMs. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 184: pages 48--53, 2006. Keywords : 3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas. @ARTICLE{lafarge_sfpt06, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Automatic building 3D reconstruction from DEMs}, year = {2006}, journal = {Revue Française de Photogrammétrie et de Télédétection (SFPT)}, volume = {184}, pages = {48--53}, url = {http://isprs.free.fr/documents/Papers/T07-32.pdf}, keyword = {3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas} } Abstract : This paper is about an example of PLEIADES applications, the 3D building reconstruction. The future PLEIADES satellites are especially well adapted to deal with 3D building reconstruction through the sub-metric resolution of images and its stereoscopic characteristics. We propose a fully automatic 3D-city model of dense urban areas using a parametric approach. First, a Digital Elevation Model (DEM) is generated using an algorithm based on a maximum-flow formulation using three views. Then, building footprints are extracted from the DEM through an automatic method based on marked point processes : they are represented by an association of rectangles that we regularize by improving the connection of the neighboring rectangles and the facade discontinuity detection. Finally, a 3D-reconstruction method based on a skeleton process which allows to model the rooftops is proposed from the DEM and the building footprints. The different building heights constitute parameters which are estimated and then regularized by the ”K-means” algorithm including an entropy term.

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