Publications of Josiane Zerubia

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• 59 Articles
• 173 Conference articles
• 64 Technical and Research Reports
• 7 Collection articles or Books chapters
• 3 Books

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59 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 - 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.

7 - 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.

8 - 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.

9 - 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.} }

10 - 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.

11 - 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.

12 - 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.

13 - 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.

14 - 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.

15 - 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.

16 - 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.

17 - 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.

18 - 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.

19 - 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.

20 - 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} }

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