Les Publications

Résultat de la recherche dans la liste des publications :

• 101 Articles
• 31 Thèses de Doctorat et Habilitations
• 245 Articles de conférence
• 90 Rapports de recherche et Rapports techniques
• 14 Articles de collection ou Chapitres de livres
• 5 Livres

nouvelle recherche | bibtex

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101 Articles

41 - 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} }

42 - 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.

43 - 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

44 - 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.

45 - 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.

46 - 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.

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

48 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation. G. Moser 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.

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

50 - Automatic building 3D reconstruction from DEMs. F. Lafarge 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.

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

52 - 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 = {} }

53 - 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} }

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

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

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

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

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

59 - 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} }

60 - Applications of Gibbs fields methods to image processing problems. X. Descombes et E. Zhizhina. Problems of Information Transmission, 40(3): pages 108--125, septembre 2004. Note : in Russian @ARTICLE{DES04br, author = {Descombes, X. and Zhizhina, E.}, title = {Applications of Gibbs fields methods to image processing problems}, year = {2004}, month = {septembre}, journal = {Problems of Information Transmission}, volume = {40}, number = {3}, pages = {108--125}, note = {in Russian}, pdf = {http://www.mathnet.ru/php/getFT.phtml?jrnid=ppi&paperid=146&what=fullt&option_lang=rus}, keyword = {} }

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