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245 Conference articles

151 - A Reversible Jump MCMC sampler for building detection in image processing. M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo Methods and Quasi-Monte Carlo Methods, series Special Se, Juan les Pins (France), June 2004. @INPROCEEDINGS{mcmcqmc, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {A Reversible Jump MCMC sampler for building detection in image processing}, year = {2004}, month = {June}, booktitle = {Monte Carlo Methods and Quasi-Monte Carlo Methods}, series = {Special Se}, address = {Juan les Pins (France)}, url = {http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, keyword = {} }

152 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, May 2004. @INPROCEEDINGS{lacoste04a, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, year = {2004}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Montreal, Quebec, Canada}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, keyword = {} }

153 - A $l^1$-unified variational framework for image restoration. J. Bect and L. Blanc-Féraud and G. Aubert and A. Chambolle. In Proc. European Conference on Computer Vision (ECCV), Vol. LNCS 3024, pages 1--13, Ed. T. Pajdla and J. Matas, Publ. Springer, Prague, Czech Republic, May 2004. @INPROCEEDINGS{eccv04, author = {Bect, J. and Blanc-Féraud, L. and Aubert, G. and Chambolle, A.}, title = {A $l^1$-unified variational framework for image restoration}, year = {2004}, month = {May}, booktitle = {Proc. European Conference on Computer Vision (ECCV)}, volume = {LNCS 3024}, pages = {1--13}, editor = {T. Pajdla and J. Matas}, publisher = {Springer}, address = {Prague, Czech Republic}, url = {http://link.springer.com/chapter/10.1007%2F978-3-540-24673-2_1}, keyword = {} }

154 - Deconvolution in confocal microscopy with total variation regularization. N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, May 2004. @INPROCEEDINGS{Dey04b, author = {Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Deconvolution in confocal microscopy with total variation regularization}, year = {2004}, month = {May}, booktitle = {Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, pages = {117--120}, url = {http://www3.jouy.inra.fr/miaj/public/imaste/ssiab2004/program/abw92/}, keyword = {} }

155 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004. Keywords : Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics. @INPROCEEDINGS{cossu04a, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, year = {2004}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Montreal, Canada}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, keyword = {Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics} } Abstract : Although subband histograms of the wavelet coefficients of natural images possess a characteristic leptokurtotic form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, leptokurtotic, and interestingly, in some subbands, multimodal histograms. These subbands are closely linked to the structure of the texture, and guarantee that the most probable image is not flat. Motivated by these observations, we propose a probabilistic model that takes them into account. Adaptive wavelet packet subbands are modelled as Gaussian, generalized Gaussian, or a constrained Gaussian mixture. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling.

156 - A deconvolution method for confocal microscopy with total variation regularization. N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, USA, April 2004. Keywords : 3D confocal microscopy, Poisson deconvolution, total variation regularization. @INPROCEEDINGS{Dey04a, author = {Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {A deconvolution method for confocal microscopy with total variation regularization}, year = {2004}, month = {April}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, address = {Arlington, USA}, pdf = {http://dx.doi.org/10.1109/ISBI.2004.1398765}, keyword = {3D confocal microscopy, Poisson deconvolution, total variation regularization} } Abstract : Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this method tends to amplify noise if used without regularizing constraint. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on total variation, whose smoothing avoids oscillations while preserving edges. We show on simulated images that this constraint improves the deconvolution result both visually and using quantitative measures.

157 - Marked Point Process in Image Analysis : from Context to Geometry. X. Descombes and F. Kruggel and C. Lacoste and M. Ortner and G. Perrin and J. Zerubia. In International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004. Keywords : RJMCMC, Object extraction, Marked point process, Stochastic geometry. @INPROCEEDINGS{geostoch04a, author = {Descombes, X. and Kruggel, F. and Lacoste, C. and Ortner, M. and Perrin, G. and Zerubia, J.}, title = {Marked Point Process in Image Analysis : from Context to Geometry}, year = {2004}, booktitle = {International Conference on Spatial Point Process Modelling and its Application (SPPA)}, address = {Castellon, Spain}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.ps.gz}, keyword = {RJMCMC, Object extraction, Marked point process, Stochastic geometry} } Abstract : We consider the marked point process framework as a natural extension of the Markov random field approach in image analysis. We consider a general model defined by its density allowing us to consider some geometrical constraints on objects and between objects in feature extraction problems. Some examples are derived for small brain lesions detection from MR Images, road network, tree crown and building extraction from remotely sensed images. The results obtained on real data show the relevance of the proposal approach.

158 - Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation. M. Ortner and X. Descombes and J. Zerubia. In Pixels et Cités, ENSG, Marne la Vallée, France, November 2003. @INPROCEEDINGS{mathiaspix, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation}, year = {2003}, month = {November}, booktitle = {Pixels et Cités}, address = {ENSG, Marne la Vallée, France}, pdf = {Articles/PixelsCites2003.pdf}, keyword = {} }

159 - Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation. M. Ortner and X. Descombes and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), Hammamet, Tunisia, October 2003. @INPROCEEDINGS{mathiastaima, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation}, year = {2003}, month = {October}, booktitle = {Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, address = {Hammamet, Tunisia}, url = {http://conferences.telecom-bretagne.eu/taima/_2003/}, keyword = {} }

160 - Wavelet-Based Superresolution in Astronomy. R. Willett and I. H. Jermyn and R. Nowak and J. Zerubia. In Proc. Astronomical Data Analysis Software and Systems, Strasbourg, France, October 2003. Keywords : Superresolution, Wavelets, Astronomy. @INPROCEEDINGS{Willett03, author = {Willett, R. and Jermyn, I. H. and Nowak, R. and Zerubia, J.}, title = {Wavelet-Based Superresolution in Astronomy}, year = {2003}, month = {October}, booktitle = {Proc. Astronomical Data Analysis Software and Systems}, address = {Strasbourg, France}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Willett03adass.pdf}, keyword = {Superresolution, Wavelets, Astronomy} } Abstract : High-resolution astronomical images can be reconstructed from several blurred and noisy low-resolution images using a computational process known as superresolution reconstruction. Superresolution reconstruction is closely related to image deconvolution, except that the low-resolution images are not registered and their relative translations and rotations must be estimated in the process. The novelty of our approach to the superresolution problem is the use of wavelets and related multiresolution methods within an expectation-maximization reconstruction process to improve the accuracy and visual quality of the reconstructed image. Simulations demonstrate the effectiveness of the proposed method, including its ability to distinguish between tightly grouped stars with a small set of observations.

161 - Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision, at ICCV, Nice, France, October 2003. Keywords : Higher-order, Active contour, Shape, Road network, Segmentation, Prior. @INPROCEEDINGS{Rochery03a, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery}, year = {2003}, month = {October}, booktitle = {Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision}, address = {at ICCV, Nice, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_vlsm03.pdf}, keyword = {Higher-order, Active contour, Shape, Road network, Segmentation, Prior} } Abstract : We present a novel method for the incorporation of shape information into active contour models, and apply it to the extraction of line networks (e.g. road, water) from satellite imagery. The method is based on a new class of contour energies. These energies are quadratic on the space of one-chains in the image, as opposed to classical energies, which are linear. They can be expressed as double integrals on the contour, and thus incorporate non-trivial interactions between different contour points. The new energies describe families of contours that share complex geometric properties, without making reference to any particular shape. Networks fall into such a family, and to model them we make a particular choice of quadratic energy whose minima are reticulated. 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. Promising experimental results are obtained using real images.

162 - Décomposition D'images: Application Aux Images RSO. J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003. @INPROCEEDINGS{jf_gretsi, author = {Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, title = {Décomposition D'images: Application Aux Images RSO}, year = {2003}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Paris, France}, url = {http://documents.irevues.inist.fr/handle/2042/13577}, keyword = {} }

163 - Wavelet-Based Level Set Evolution for Classification of Textured Images. J.F. Aujol and G. Aubert and L. Blanc-Féraud. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003. @INPROCEEDINGS{jf_icip, author = {Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, title = {Wavelet-Based Level Set Evolution for Classification of Textured Images}, year = {2003}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Barcelona, Spain}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1246863}, keyword = {} }

164 - Texture Analysis: An Adaptive Probabilistic Approach. K. Brady and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003. Keywords : Adaptive, Wavelet packet, Statistics, Texture. @INPROCEEDINGS{Brady03, author = {Brady, K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture Analysis: An Adaptive Probabilistic Approach}, year = {2003}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Barcelona, Spain}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03icip.pdf}, keyword = {Adaptive, Wavelet packet, Statistics, Texture} } Abstract : Two main issues arise when working in the area of texture segmentation: the need to describe the texture accurately by capturing its underlying structure, and the need to perform analyses on the boundaries of textures. Herein, we tackle these problems within a consistent probabilistic framework. Starting from a probability distribution on the space of infinite images, we generate a distribution on arbitrary finite regions by marginalization. For a Gaussian distribution, the computational requirement of diagonalization and the modelling requirement of adaptivity together lead naturally to adaptive wavelet packet models that capture the ‘significant amplitude features’ in the Fourier domain. Undecimated versions of the wavelet packet transform are used to diagonalize the Gaussian distribution efficiently, albeit approximately. We describe the implementation and application of this approach and present results obtained on several Brodatz texture mosaics.

165 - Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003. @INPROCEEDINGS{lacosteXDJZNB03, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet}, year = {2003}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Paris, France}, url = {http://documents.irevues.inist.fr/handle/2042/13529}, keyword = {} }

166 - Road Network Extraction in Remote Sensing by a Markov Object Process. C. Lacoste and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003. @INPROCEEDINGS{lacosteXDJZ03, author = {Lacoste, C. and Descombes, X. and Zerubia, J.}, title = {Road Network Extraction in Remote Sensing by a Markov Object Process}, year = {2003}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Barcelona, Spain}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1247420}, keyword = {} }

167 - Filtering interferometric phase images by anisotropic diffusion. C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, September 2003. @INPROCEEDINGS{ClacombGALBFPK, author = {Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, title = {Filtering interferometric phase images by anisotropic diffusion}, year = {2003}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Barcelona}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1247201}, keyword = {} }

168 - Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003. @INPROCEEDINGS{Rochery03, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection}, year = {2003}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Paris, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_gretsi03.pdf}, keyword = {} }

169 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images. K. Brady and I. H. Jermyn and J. Zerubia. In Proc. British Machine Vision Conference (BMVC), Norwich, U. K., September 2003. Keywords : probabilistic, Adaptive, wavelet, Texture. @INPROCEEDINGS{Brady03a, author = {Brady, K. and Jermyn, I. H. and Zerubia, J.}, title = {Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images}, year = {2003}, month = {September}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Norwich, U. K.}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03bmvc.pdf}, keyword = {probabilistic, Adaptive, wavelet, Texture} } Abstract : Remote sensing imagery plays an important role in many elds. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefcients. This technique has been applied to several remote sensing images, the results of which are presented.

170 - Natural image modeling using complex wavelets. A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. SPIE Conference on Wavelets, Vol. 5207, San Diego, August 2003. @INPROCEEDINGS{Jalobfjz, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Natural image modeling using complex wavelets}, year = {2003}, month = {August}, booktitle = {Proc. SPIE Conference on Wavelets}, volume = {5207}, address = {San Diego}, url = {http://spie.org/Publications/Proceedings/Paper/10.1117/12.507945}, keyword = {} }

171 - Discrete Wavelet transforms that have an adaptive low pass filter. G.C.K. Abhayaratne. In Proc. International Symposium on Signal Processing and its Applications (ISSPA), Paris, France, July 2003. @INPROCEEDINGS{gcka3, author = {Abhayaratne, G.C.K.}, title = {Discrete Wavelet transforms that have an adaptive low pass filter}, year = {2003}, month = {July}, booktitle = {Proc. International Symposium on Signal Processing and its Applications (ISSPA)}, address = {Paris, France}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1224920}, keyword = {} }

172 - Remotely sensed image segmentation using an object point process. S. Drot and H. Le Men and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, July 2003. @INPROCEEDINGS{drot, author = {Drot, S. and Le Men, H. and Descombes, X. and Zerubia, J.}, title = {Remotely sensed image segmentation using an object point process}, year = {2003}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1294331}, keyword = {} }

173 - Urban Scene Rendering using Object Description. F. Cerdat and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, July 2003. @INPROCEEDINGS{fcerdat, author = {Cerdat, F. and Descombes, X. and Zerubia, J.}, title = {Urban Scene Rendering using Object Description}, year = {2003}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1293679}, keyword = {} }

174 - Decomposing an Image: Application to SAR Images. J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. In Proc. Scale-Space, Vol. 2695, series Lecture No, June 2003. @INPROCEEDINGS{jf_scalespace, author = {Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, title = {Decomposing an Image: Application to SAR Images}, year = {2003}, month = {June}, booktitle = {Proc. Scale-Space}, volume = {2695}, series = {Lecture No}, url = {http://link.springer.com/chapter/10.1007%2F3-540-44935-3_21}, keyword = {} }

175 - Diffusion anisotrope et filtrage interférométrique. C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Congrès National d'Analyse Numérique, Montpellier, June 2003. @INPROCEEDINGS{Lacombe-Canum03, author = {Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, title = {Diffusion anisotrope et filtrage interférométrique}, year = {2003}, month = {June}, booktitle = {Congrès National d'Analyse Numérique}, address = {Montpellier}, url = {http://www.i3m.univ-montp2.fr/CANUM2003/}, pdf = {http://www.i3m.univ-montp2.fr/CANUM2003/COMMUNICATIONS/CONF_ORALES/caroline.lacombe.pdf}, keyword = {} }

176 - Filtrage adaptatif des interférogrammes par diffusion anisotrope. C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Gerardmer, May 2003. @INPROCEEDINGS{CLGALBFPK, author = {Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, title = {Filtrage adaptatif des interférogrammes par diffusion anisotrope}, year = {2003}, month = {May}, booktitle = {Proc. Journées des jeunes chercheurs en vision par ordinateur}, address = {Gerardmer}, ps = {http://www-math.unice.fr/publis/clacombe_orasis03.ps}, keyword = {} }

177 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval. H. Permuter and J.M. Francos and I. H. Jermyn. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, April 2003. Keywords : Texture, Gaussian mixture, Classification, Aerial images. @INPROCEEDINGS{Permuter03, author = {Permuter, H. and Francos, J.M. and Jermyn, I. H.}, title = {Gaussian Mixture Models of Texture and Colour for Image Database Retrieval}, year = {2003}, month = {April}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Hong Kong}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Permuter03icassp.pdf}, keyword = {Texture, Gaussian mixture, Classification, Aerial images} } Abstract : We introduce Gaussian mixture models of ‘structure’ and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.

178 - Classification d'images par approche variationnelle. L. Blanc-Féraud. In Workshop on Vision, Image, and Agriculture, Dijon, January 2003. Note : Invited talk. @INPROCEEDINGS{LaureBF, author = {Blanc-Féraud, L.}, title = {Classification d'images par approche variationnelle}, year = {2003}, month = {January}, booktitle = {Workshop on Vision, Image, and Agriculture}, address = {Dijon}, note = {Invited talk.}, keyword = {} }

179 - Building Extraction from Digital Elevation Model.. M. Ortner and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honk Kong, 2003. @INPROCEEDINGS{mathiasicassp, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Building Extraction from Digital Elevation Model.}, year = {2003}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Honk Kong}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1199477}, keyword = {} }

180 - Segmentation of Pathological Features in MRI Brain Datasets. F. Kruggel and C. Chalopin and X. Descombes and V. Kovalev and J.C. Rajapakse. In ICONIP, invited paper, Singapore, November 2002. @INPROCEEDINGS{kruggelXd, author = {Kruggel, F. and Chalopin, C. and Descombes, X. and Kovalev, V. and Rajapakse, J.C.}, title = {Segmentation of Pathological Features in MRI Brain Datasets}, year = {2002}, month = {November}, booktitle = {ICONIP, invited paper}, address = {Singapore}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1201981}, keyword = {} }

181 - Fusion of Radiometry and Textural Information for SIRC Image Classification. O. Viveros-Cancino and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002. @INPROCEEDINGS{oscarbaghdadi, author = {Viveros-Cancino, O. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Fusion of Radiometry and Textural Information for SIRC Image Classification}, year = {2002}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1038916}, keyword = {} }

182 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets. C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002. Keywords : Segmentation, Hidden Markov Model, Texture, Colour. @INPROCEEDINGS{ijking, author = {Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, title = {Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, year = {2002}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, keyword = {Segmentation, Hidden Markov Model, Texture, Colour} } Abstract : The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has emerged as an application area in which retrieval is attempted by trying to gain unsupervised access to the image semantics directly rather than via manual annotation. To this end, we present an unsupervised segmentation technique in which colour and texture models are learned from the image prior to segmentation, and whose output (including the models) may subsequently be used as a content descriptor in a CBIR system. These models are obtained in a multiresolution setting in which Hidden Markov Trees (HMT) are used to model the key statistical properties exhibited by complex wavelet and scaling function coefficients. The unsupervised Mean Shift Iteration (MSI) procedure is used to determine a number of image regions which are then used to train the models for each segmentation class.

183 - Psychovisual Evaluation of Image Segmentation Algorithms. C. Shaffrey and I. H. Jermyn and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002. @INPROCEEDINGS{kingij, author = {Shaffrey, C. and Jermyn, I. H. and Kingsbury, N.}, title = {Psychovisual Evaluation of Image Segmentation Algorithms}, year = {2002}, month = {September}, booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems}, address = {Ghent, Belgique}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002_final.pdf}, keyword = {} }

184 - Evaluation Methodologies for Image Retrieval Systems. I. H. Jermyn and C. Shaffrey and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002. @INPROCEEDINGS{shaffreyij, author = {Jermyn, I. H. and Shaffrey, C. and Kingsbury, N.}, title = {Evaluation Methodologies for Image Retrieval Systems}, year = {2002}, month = {September}, booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems}, address = {Ghent, Belgique}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002final.pdf}, keyword = {} }

185 - Satellite and aerial image deconvolution using an EM method with complex wavelets. A. Jalobeanu and R. Nowak and J. Zerubia and M. Figueiredo. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002. @INPROCEEDINGS{nowakjalojz, author = {Jalobeanu, A. and Nowak, R. and Zerubia, J. and Figueiredo, M.}, title = {Satellite and aerial image deconvolution using an EM method with complex wavelets}, year = {2002}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1038028}, keyword = {} }

186 - Image processing for high resolution satellite and aerial data. J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, September 2002. @INPROCEEDINGS{jzEusipco, author = {Zerubia, J.}, title = {Image processing for high resolution satellite and aerial data}, year = {2002}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7072252}, keyword = {} }

187 - Unsupervised segmentation of textured satellite and aerial images with Bayesian methods. S. Wilson and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, September 2002. @INPROCEEDINGS{wilsonjz, author = {Wilson, S. and Zerubia, J.}, title = {Unsupervised segmentation of textured satellite and aerial images with Bayesian methods}, year = {2002}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Toulouse, France}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7071914}, keyword = {} }

188 - A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas. G. Rellier and X. Descombes and J. Zerubia and F. Falzon. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{rellierXDfalzon, author = {Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, title = {A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1044850}, keyword = {} }

189 - Object Point Processes for Image Segmentation. S. Drot and X. Descombes and H. Le Men and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{drotXD, author = {Drot, S. and Descombes, X. and Le Men, H. and Zerubia, J.}, title = {Object Point Processes for Image Segmentation}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1048453}, keyword = {} }

190 - A variational approach to one dimensional phase unwrapping. C. Lacombe and P. Kornprobst and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, August 2002. @INPROCEEDINGS{lacombekronp, author = {Lacombe, C. and Kornprobst, P. and Aubert, G. and Blanc-Féraud, L.}, title = {A variational approach to one dimensional phase unwrapping}, year = {2002}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Québec, Canada}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1048426}, keyword = {} }

191 - Estimation of blur and noise parameters in remote sensing. A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Orlando, USA, May 2002. @INPROCEEDINGS{jallbfjz, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Estimation of blur and noise parameters in remote sensing}, year = {2002}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Orlando, USA}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5745429}, keyword = {} }

192 - Building extraction using a Markov point process. L. Garcin and X. Descombes and J. Zerubia and H. Le Men. In Proc. IEEE International Conference on Image Processing (ICIP), papier invité, Thessalonique, Grèce, October 2001. @INPROCEEDINGS{xd01d, author = {Garcin, L. and Descombes, X. and Zerubia, J. and Le Men, H.}, title = {Building extraction using a Markov point process}, year = {2001}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {papier invité, Thessalonique, Grèce}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=958555}, keyword = {} }

193 - Image deconvolution using Hidden Markov Tree modeling of complex wavelet packets. A. Jalobeanu and N. Kingsbury and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Thessalonique, Grèce, October 2001. @INPROCEEDINGS{aj01b, author = {Jalobeanu, A. and Kingsbury, N. and Zerubia, J.}, title = {Image deconvolution using Hidden Markov Tree modeling of complex wavelet packets}, year = {2001}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Thessalonique, Grèce}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=958988}, keyword = {} }

194 - Two variational models for multispectral image classification. C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Sophia Antipolis, France, September 2001. @INPROCEEDINGS{lbf01a, author = {Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, title = {Two variational models for multispectral image classification}, year = {2001}, month = {September}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {Sophia Antipolis, France}, url = {http://link.springer.com/chapter/10.1007%2F3-540-44745-8_23}, keyword = {} }

195 - Classification d'image satellitaire superspectrale en zone rurale et périurbaine. O. Pony and U. Polverini and L. Gautret and J. Zerubia and X. Descombes. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001. @INPROCEEDINGS{xd01f, author = {Pony, O. and Polverini, U. and Gautret, L. and Zerubia, J. and Descombes, X.}, title = {Classification d'image satellitaire superspectrale en zone rurale et périurbaine}, year = {2001}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Toulouse, France}, url = {http://documents.irevues.inist.fr/handle/2042/13236}, keyword = {} }

196 - Un modèle markovien gaussien pour l'analyse de texture hyperspectrale en milieu urbain. G. Rellier and X. Descombes and J. Zerubia and F. Falzon. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001. @INPROCEEDINGS{xd01g, author = {Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, title = {Un modèle markovien gaussien pour l'analyse de texture hyperspectrale en milieu urbain}, year = {2001}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Toulouse, France}, url = {http://documents.irevues.inist.fr/handle/2042/13293}, keyword = {} }

197 - Recuit simulé pour le shape from shading. X. Descombes and J.D. Durou and L. Petit. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001. @INPROCEEDINGS{xd01h, author = {Descombes, X. and Durou, J.D. and Petit, L.}, title = {Recuit simulé pour le shape from shading}, year = {2001}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Toulouse, France}, url = {http://documents.irevues.inist.fr/handle/2042/13322}, keyword = {} }

198 - Estimation de paramètres instrumentaux en imagerie satellitaire. A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001. @INPROCEEDINGS{aj01d, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Estimation de paramètres instrumentaux en imagerie satellitaire}, year = {2001}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Toulouse, France}, url = {http://documents.irevues.inist.fr/handle/2042/13231}, keyword = {} }

199 - Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model. X. Descombes and M.N.M. van Lieshout and R. Stoica and J. Zerubia. In IEEE Workshop on Statistical Signal Processing, papier invité, Singapour, August 2001. @INPROCEEDINGS{xd01e, author = {Descombes, X. and van Lieshout, M.N.M. and Stoica, R. and Zerubia, J.}, title = {Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model}, year = {2001}, month = {August}, booktitle = {IEEE Workshop on Statistical Signal Processing}, address = {papier invité, Singapour}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=955212}, keyword = {} }

200 - Region extraction from multiple images. H. Ishikawa and I. H. Jermyn. In Proc. IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, July 2001. Keywords : Stereo, Motion, global, optimum, Graph, Cycle. @INPROCEEDINGS{IJ01a, author = {Ishikawa, H. and Jermyn, I. H.}, title = {Region extraction from multiple images}, year = {2001}, month = {July}, booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)}, address = {Vancouver, Canada}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jermyn01iccv.pdf}, keyword = {Stereo, Motion, global, optimum, Graph, Cycle} } Abstract : We present a method for region identification in multiple images. A set of regions in different images and the correspondences on their boundaries can be thought of as a boundary in the multi-dimensional space formed by the product of the individual image domains. We minimize an energy functional on the space of such boundaries, thereby identifying simultaneously both the optimal regions in each image and the optimal correspondences on their boundaries. We use a ratio form for the energy functional, thus enabling the global minimization of the energy functional using a polynomial time graph algorithm, among other desirable properties. We choose a simple form for this energy that favours boundaries that lie on high intensity gradients in each image, while encouraging correspondences between boundaries in different images that match intensity values. The latter tendency is weighted by a novel heuristic energy that encourages the boundaries to lie on disparity or optical flow discontinuities, although no dense optical flow or disparity map is computed.

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