Publications de Josiane Zerubia

Résultat de la recherche dans la liste des publications :

• 59 Articles
• 173 Articles de conférence
• 64 Rapports de recherche et Rapports techniques
• 7 Articles de collection ou Chapitres de livres
• 3 Livres

nouvelle recherche | bibtex

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59 Articles

1 - Unsupervised amplitude and texture classification of SAR images with multinomial latent model. K. Kayabol et J. Zerubia. IEEE Trans. on Image Processing, 22(2): pages 561-572, février 2013. Mots-clés : 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 = {février}, 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 et X. Descombes et J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1): pages 33-50, janvier 2012. Mots-clés : Building extraction, Change detection, Processus ponctuels marques, 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 = {janvier}, 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, Processus ponctuels marques, 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 et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Geoscience and Remote Sensing Letters, 2012. Note : to appear in 2013 Mots-clés : 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 et G. Moser et S.B. Serpico et J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, juin 2011. Mots-clés : 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 = {juin}, 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 et A. Béchet et X. Descombes et A. Arnaud et J. Zerubia. Bird Study, : pages 1-7, juin 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 = {juin}, 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 et G. Moser et S.B. Serpico et J. Zerubia. IEEE-Geoscience and Remote Sensing Letters, 8(1): pages 148-152, janvier 2011. Mots-clés : finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (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 = {janvier}, 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, EM Stochastique (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 et X. Descombes et Charpiat G. et J. Zerubia. Journal of Multimedia Processing and Technologies, 2011. Mots-clés : 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 et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011. Mots-clés : Contour actif, Processus ponctuels marques, 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 = {Contour actif, Processus ponctuels marques, 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 et F. Chatelain et X. Descombes et J. Zerubia. Revue Française de Photogrammétrie et de Télédétection (SFPT), (194): pages 2-15, 2011. Mots-clés : processus ponctuel marqué, RJMCMC, Recuit Simule, 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, Recuit Simule, SEM, pseudo-vraisemblance, extraction d'objet.} }

10 - Extended Phase Field Higher-Order Active Contour Models for Networks. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, mai 2010. Mots-clés : Contour actif, Champ de Phase, 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 = {mai}, journal = {International Journal of Computer Vision}, volume = {88}, number = {1}, pages = { 111-128}, url = {http://www.springerlink.com/content/d3641g2227316w58/}, keyword = {Contour actif, Champ de Phase, 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 et X. Descombes et J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, avril 2010. Mots-clés : Processus ponctuels marques, Reseaux lineiques, 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 = {avril}, journal = {Pattern Recognition}, volume = {43}, number = {4}, pages = {1631-1641}, url = {http://dx.doi.org/10.1016/j.patcog.2009.11.003}, keyword = {Processus ponctuels marques, Reseaux lineiques, 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 et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(1): pages 135-147, janvier 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 = {janvier}, 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 et T. Szirányi et Z. Kato et J. Zerubia. IEEE Trans. Image Processing, 18(10): pages 2303-2315, octobre 2009. Mots-clés : 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 = {octobre}, 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 et B. Zhang et L. Blanc-Féraud et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Applied Optics, 48(22): pages 4437-4448, août 2009. Mots-clés : Blind Deconvolution, Microscopie confocale, Problèmes Inverses. 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 = {août}, 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, Microscopie confocale, Problèmes Inverses} } 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 et R. Gaetano et M. Haindl et J. Zerubia. IEEE Trans. on Image Processing, 18(8): pages 1830-1843, août 2009. Mots-clés : 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 = {août}, 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 et X. Descombes et A. Béchet et J. Zerubia. Traitement du Signal, 26(2): pages 95-108, juillet 2009. Mots-clés : 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 = {juillet}, 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 et I. H. Jermyn et Z. Kato et J. Zerubia. Pattern Recognition, 42(5): pages 699-709, mai 2009. Mots-clés : Forme, Ordre superieur, Contour actif, Gaz de cercles, Extraction de Houppiers, 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 = {mai}, 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 = {Forme, Ordre superieur, Contour actif, Gaz de cercles, Extraction de Houppiers, 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 et I. H. Jermyn et V. Prinet et J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 1(2): pages 139--146, juin 2008. Mots-clés : Zones urbaines denses, Système d'Information Géographique (SIG), Multiscale, Reseaux routiers, Methodes variationnelles, 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 = {juin}, 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 = {Zones urbaines denses, Système d'Information Géographique (SIG), Multiscale, Reseaux routiers, Methodes variationnelles, 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 et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Journal of Photogrammetry and Remote Sensing, 63(3): pages 365-381, mai 2008. Mots-clés : Building extraction, Reconstruction en 3D, Digital Elevation Model, Geometrie stochastique. @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 = {mai}, 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, Reconstruction en 3D, Digital Elevation Model, Geometrie stochastique} } 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 et X. Descombes et J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 2008. Mots-clés : Traitement d'image, 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 = {Traitement d'image, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register} }

21 - Higher-Order Active Contour Energies for Gap Closure. M. Rochery et I. H. Jermyn et J. Zerubia. Journal of Mathematical Imaging and Vision, 29(1): pages 1-20, septembre 2007. Mots-clés : Gap closure, Ordre superieur, Contour actif, Forme, A priori, Reseaux routiers. @ARTICLE{Rochery07, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher-Order Active Contour Energies for Gap Closure}, year = {2007}, month = {septembre}, 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, Ordre superieur, Contour actif, Forme, A priori, Reseaux routiers} } 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.

22 - Gaussian approximations of fluorescence microscope point-spread function models. B. Zhang et J. Zerubia et J.C. Olivo-Marin. Applied Optics, 46(10): pages 1819-1829, avril 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 = {avril}, 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.

23 - Building Outline Extraction from Digital Elevation Models using Marked Point Processes. M. Ortner et X. Descombes et J. Zerubia. International Journal of Computer Vision, 72(2): pages 107-132, avril 2007. Mots-clés : RJMCMC, Batiments, Geometrie stochastique, Processus ponctuels marques, Modele numerique d'elevation (MNE). @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 = {avril}, 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, Batiments, Geometrie stochastique, Processus ponctuels marques, Modele numerique d'elevation (MNE)} }

24 - Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques. F. Lafarge et X. Descombes et J. Zerubia et S. Mathieu. Traitement du Signal, 24(1), 2007. Note : copyright Traitement du Signal Mots-clés : Champs Gaussiens, Évenement rare, DT-caracteristic, Pic d'intensite. @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 = {Champs Gaussiens, Évenement rare, DT-caracteristic, Pic d'intensite} }

25 - Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images. A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et 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.

26 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model. A. Achim et E.E. Kuruoglu et J. Zerubia. IEEE Trans. on Image Processing, 15(9): pages 2686-2693, septembre 2006. Mots-clés : Images SAR. @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 = {septembre}, journal = {IEEE Trans. on Image Processing}, volume = {15}, number = {9}, pages = {2686-2693}, pdf = {http://dx.doi.org/10.1109/TIP.2006.877362}, keyword = {Images SAR} } 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

27 - Higher Order Active Contours. M. Rochery et I. H. Jermyn et J. Zerubia. International Journal of Computer Vision, 69(1): pages 27--42, août 2006. Mots-clés : Contour actif, Forme, Ordre superieur, A priori, Reseaux routiers. @ARTICLE{mr_ijcv_06, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher Order Active Contours}, year = {2006}, month = {août}, 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 = {Contour actif, Forme, Ordre superieur, A priori, Reseaux routiers} } 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.

28 - SAR amplitude probability density function estimation based on a generalized Gaussian model. G. Moser et J. Zerubia et S.B. Serpico. IEEE Trans. on Image Processing, 15(6): pages 1429-1442, juin 2006. Mots-clés : Images SAR, Gaussiennes generalisees, Transformee de Mellin. 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 = {juin}, 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 = {Images SAR, Gaussiennes generalisees, Transformee de Mellin} } 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.

29 - Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution. N. Dey et L. Blanc-Féraud et C. Zimmer et Z. Kam et P. Roux et J.C. Olivo-Marin et J. Zerubia. Microscopy Research Technique, 69: pages 260-266, avril 2006. Mots-clés : Microscopie confocale, Methodes variationnelles, Variation totale, 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 = {avril}, journal = {Microscopy Research Technique}, volume = {69}, pages = {260-266}, url = {http://dx.doi.org/10.1002/jemt.20294}, keyword = {Microscopie confocale, Methodes variationnelles, Variation totale, 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.

30 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation. G. Moser et J. Zerubia et S.B. Serpico. IEEE Trans. Geoscience and Remote Sensing, 44(1): pages 188-200, janvier 2006. Mots-clés : Images SAR, EM Stochastique (SEM), Dictionnaire. 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 = {janvier}, 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 = {Images SAR, EM Stochastique (SEM), Dictionnaire} } 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.

31 - Automatic building 3D reconstruction from DEMs. F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 184: pages 48--53, 2006. Mots-clés : 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.

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

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

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

35 - Modelling SAR Images with a Generalization of the Rayleigh Distribution. E.E. Kuruoglu et J. Zerubia. IEEE Trans. Image Processing, 13(4): pages 527 - 533, avril 2004. @ARTICLE{Kuruoglu03, author = {Kuruoglu, E.E. and Zerubia, J.}, title = {Modelling SAR Images with a Generalization of the Rayleigh Distribution}, year = {2004}, month = {avril}, journal = {IEEE Trans. Image Processing}, volume = {13}, number = {4}, pages = {527 - 533}, pdf = {http://ieeexplore.ieee.org/iel5/83/28667/01284389.pdf?tp=&arnumber=1284389&isnumber=28667}, keyword = {} }

36 - A Gibbs point process for road extraction in remotely sensed images. R. Stoica et X. Descombes et J. Zerubia. International Journal of Computer Vision, 57(2): pages 121--136, 2004. @ARTICLE{STO04a, author = {Stoica, R. and Descombes, X. and Zerubia, J.}, title = {A Gibbs point process for road extraction in remotely sensed images}, year = {2004}, journal = {International Journal of Computer Vision}, volume = {57}, number = {2}, pages = {121--136}, url = {http://www.springerlink.com/content/kr262t6084464n30/}, keyword = {} }

37 - An adaptive Gaussian model for satellite image deblurring. A. Jalobeanu et L. Blanc-Féraud et J. Zerubia. IEEE Trans. Image Processing, 13(4), 2004. @ARTICLE{JAL04a, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {An adaptive Gaussian model for satellite image deblurring}, year = {2004}, journal = {IEEE Trans. Image Processing}, volume = {13}, number = {4}, pdf = {http://ieeexplore.ieee.org/iel5/83/28667/01284396.pdf?tp=&arnumber=1284396&isnumber=28667}, keyword = {} }

38 - Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification. G. Rellier et X. Descombes et F. Falzon et J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 42(7): pages 1543--1551, 2004. @ARTICLE{DES04c, author = {Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, title = {Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification}, year = {2004}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {42}, number = {7}, pages = {1543--1551}, pdf = {http://ieeexplore.ieee.org/iel5/36/29162/01315838.pdf?tp=&arnumber=1315838&isnumber=29162}, keyword = {} }

39 - Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux. M. Ortner et X. Descombes et J. Zerubia. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 173-174: pages 83--92, 2004. @ARTICLE{ORT04a, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux}, year = {2004}, journal = {Bulletin de la Société Française de Photogrammétrie et de Télédétection}, volume = {173-174}, pages = {83--92}, keyword = {} }

40 - A nonlinear entropic variational model for image filtering. A. Ben Hamza et H. Krim et J. Zerubia. EURASIP Journal on Applied Signal Processing, 16: pages 2408--2422, 2004. @ARTICLE{JZHK04, author = {Ben Hamza, A. and Krim, H. and Zerubia, J.}, title = {A nonlinear entropic variational model for image filtering}, year = {2004}, journal = {EURASIP Journal on Applied Signal Processing}, volume = {16}, pages = {2408--2422}, url = {https://hal.inria.fr/hal-00784485/}, keyword = {} }

41 - Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet. C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 13--22, 2003. @ARTICLE{lacostesfpt, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet}, year = {2003}, journal = {Bulletin de la Société Française de Photogrammétrie et de Télédétection}, volume = {170}, pages = {13--22}, url = {http://www.researchgate.net/profile/Nicolas_Baghdadi/publication/236882132_Extraction_automatique_des_rseaux_liniques__partir_dimages_satellitaires_et_ariennes_par_processus_Markov_objets/links/00463519e05ebd9e83000000.pdf?disableCoverPage=true}, keyword = {} }

42 - Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection. G. Rellier et X. Descombes et F. Falzon et J. Zerubia. Traitement du Signal, 20(1): pages 25--42, 2003. @ARTICLE{rellierXDFFJZ, author = {Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, title = {Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection}, year = {2003}, journal = {Traitement du Signal}, volume = {20}, number = {1}, pages = {25--42}, url = {http://documents.irevues.inist.fr/handle/2042/2216}, keyword = {} }

43 - Satellite image deblurring using complex wavelet packets. A. Jalobeanu et L. Blanc-Féraud et J. Zerubia. International Journal of Computer Vision, 51(3): pages 205--217, 2003. @ARTICLE{JalobeaLBFJZ, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Satellite image deblurring using complex wavelet packets}, year = {2003}, journal = {International Journal of Computer Vision}, volume = {51}, number = {3}, pages = {205--217}, pdf = {http://link.springer.com/article/10.1023/A%3A1021801918603}, keyword = {} }

44 - Skewed alpha-stable distributions for modelling textures. E.E. Kuruoglu et J. Zerubia. Pattern Recognition Letters, 24(1-3): pages 339--348, 2003. @ARTICLE{Kuruoglu03a, author = {Kuruoglu, E.E. and Zerubia, J.}, title = {Skewed alpha-stable distributions for modelling textures}, year = {2003}, journal = {Pattern Recognition Letters}, volume = {24}, number = {1-3}, pages = {339--348}, url = {http://www.sciencedirect.com/science/article/pii/S0167865502002477}, keyword = {} }

45 - Marked Point Processes in Image Analysis. X. Descombes et J. Zerubia. IEEE Signal Processing Magazine, 19(5): pages 77-84, septembre 2002. @ARTICLE{XDJZ, author = {Descombes, X. and Zerubia, J.}, title = {Marked Point Processes in Image Analysis}, year = {2002}, month = {septembre}, journal = {IEEE Signal Processing Magazine}, volume = {19}, number = {5}, pages = {77-84}, pdf = {http://ieeexplore.ieee.org/iel5/79/22084/01028354.pdf?tp=&arnumber=1028354&isnumber=22084}, keyword = {} }

46 - Extension of phase correlation to subpixel registration. H. Foroosh et J. Zerubia et M. Berthod. IEEE Trans. on Image Processing, 11(3): pages 188 - 200, mars 2002. @ARTICLE{forooshjzmb, author = {Foroosh, H. and Zerubia, J. and Berthod, M.}, title = {Extension of phase correlation to subpixel registration}, year = {2002}, month = {mars}, journal = {IEEE Trans. on Image Processing}, volume = {11}, number = {3}, pages = {188 - 200}, pdf = {http://ieeexplore.ieee.org/iel5/83/21305/00988953.pdf?tp=&arnumber=988953&isnumber=21305}, keyword = {} }

47 - Local registration and deformation of a road cartographic database on a SPOT Satellite Image. G. Rellier et X. Descombes et J. Zerubia. Pattern Recognition, 35(10), 2002. @ARTICLE{rellierXDJZ, author = {Rellier, G. and Descombes, X. and Zerubia, J.}, title = {Local registration and deformation of a road cartographic database on a SPOT Satellite Image}, year = {2002}, journal = {Pattern Recognition}, volume = {35}, number = {10}, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001807}, keyword = {} }

48 - Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method. A. Jalobeanu et L. Blanc-Féraud et J. Zerubia. Pattern Recognition, 35(2): pages 341--352, 2002. @ARTICLE{jalo02h, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, year = {2002}, journal = {Pattern Recognition}, volume = {35}, number = {2}, pages = {341--352}, url = {http://www.sciencedirect.com/science/article/pii/S0031320300001783}, keyword = {} }

49 - A RJMCMC algorithm for object processes in image processing. X. Descombes et R. Stoica et L. Garcin et J. Zerubia. Monte Carlo Methods and Applications, 7(1-2): pages 149-156, 2001. @ARTICLE{xd01c, author = {Descombes, X. and Stoica, R. and Garcin, L. and Zerubia, J.}, title = {A RJMCMC algorithm for object processes in image processing}, year = {2001}, journal = {Monte Carlo Methods and Applications}, volume = {7}, number = {1-2}, pages = {149-156}, url = {http://www.degruyter.com/view/j/mcma.2001.7.issue-1-2/mcma.2001.7.1-2.149/mcma.2001.7.1-2.149.xml}, keyword = {} }

50 - Image segmentation using Markov random field model in fully parallel cellular network architectures. T. Szirányi et J. Zerubia et L. Czúni et D. Geldreich et Z. Kato. Real Time Imaging, 6(3): pages 195-211, juin 2000. @ARTICLE{jz00y, author = {Szirányi, T. and Zerubia, J. and Czúni, L. and Geldreich, D. and Kato, Z.}, title = {Image segmentation using Markov random field model in fully parallel cellular network architectures}, year = {2000}, month = {juin}, journal = {Real Time Imaging}, volume = {6}, number = {3}, pages = {195-211}, pdf = {http://dx.doi.org/10.1006/rtim.1998.0159}, keyword = {} } Abstract : Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips. As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 μs. In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures. In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel.

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