Publications of 2005

Result of the query in the list of publications :

• 9 Articles
• 2 PhD Thesis and Habilitations
• 13 Conference articles
• 10 Technical and Research Reports
• 1 Collection article or Book chapter

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13 Conference articles

10 - A Marked Point Process Model for Tree Crown Extraction in Plantations. G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process. @INPROCEEDINGS{perrin_icip05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {A Marked Point Process Model for Tree Crown Extraction in Plantations}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, keyword = {Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} } Abstract : This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN.

11 - Shape Moments for Region-Based Active Contours. P. Horvath and A. Bhattacharya and I. H. Jermyn and J. Zerubia and Z. Kato. In Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Szeged, Hungary, May 2005. @INPROCEEDINGS{horvath_hacippr05, author = {Horvath, P. and Bhattacharya, A. and Jermyn, I. H. and Zerubia, J. and Kato, Z.}, title = {Shape Moments for Region-Based Active Contours}, year = {2005}, month = {May}, booktitle = {Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition}, address = {Szeged, Hungary}, url = {http://vision.vein.hu/HACIPPR/}, keyword = {} }

12 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform. G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, March 2005. @INPROCEEDINGS{pons_icassp2005, author = {Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, title = {A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform}, year = {2005}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Philadelphia, Pennsylvania, USA}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1415481}, keyword = {} } 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 and/or denoising methods have been proposed to reduce these degradations.Here we propose a wavelet denoising method, which turns out to be very effective for three-dimensional confocal images. To obtain a translation and rotation invariant algorithm, we have developped the 3D Complex Wavelet Transform introduced by N. Kingsbury. These wavelets allow moreover a better directional selectivity of the wavelet coefficients. We show on simulated and real biological data the good performances of this algorithm.

13 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. Physics in Signal and Image Processing, Toulouse, France, January 2005. Keywords : Bimodal, Statistics, Wavelet packet, Adaptive, Texture. @INPROCEEDINGS{cossu_psip05, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, year = {2005}, month = {January}, booktitle = {Proc. Physics in Signal and Image Processing}, address = {Toulouse, France}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, keyword = {Bimodal, Statistics, Wavelet packet, Adaptive, Texture} } Abstract : In recent work, it was noted that although the subband histograms for standard wavelet coefcients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and interestingly, in some subbands, bi- or multi-modal histograms. Motivated by this observation, we provide additional experimental conrmation of the existence of multimodal subbands, and provide a theoretical explanation for their occurrence. The results reveal the connection of such subbands with the characteristic structure in a texture, and thus confirm the importance of such subbands for image modelling and applications.

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10 Technical and Research Reports

1 - Higher-Order Active Contour Energies for Gap Closure. M. Rochery and I. H. Jermyn and J. Zerubia. Research Report 5717, INRIA, France, October 2005. Keywords : Road network, Continuity, Gap closure, Higher-order, Active contour, Shape. @TECHREPORT{RR_5717, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher-Order Active Contour Energies for Gap Closure}, year = {2005}, month = {October}, institution = {INRIA}, type = {Research Report}, number = {5717}, address = {France}, url = {http://hal.inria.fr/inria-00070300/fr/}, pdf = {https://hal.inria.fr/file/index/docid/70300/filename/RR-5717.pdf}, ps = {http://hal.inria.fr/docs/00/07/03/00/PS/RR-5717.ps}, keyword = {Road network, Continuity, Gap closure, Higher-order, Active contour, Shape} } Résumé : L'un des principaux problèmes lors de l'extraction de réseaux linéiques dans des images, et en particulier l'extraction de réseaux routiers dans des images de télédétection, est l'existence d'interruptions dans les données, causées, par exemple, par des occultations. Ces interruptions peuvent mener à des trous dans le réseau extrait qui n'existent pas dans le réseau réel. Dans ce rapport, nous décrivons une énergie de contour actif d'ordre supérieur qui, en plus de favoriser les régions composées de bras fins et connectés entre eux, inclut un terme d'a priori qui pénalise les configurations du réseau où des extremités proches et se faisant face apparaissent. L'apparition dans le réseau extrait de ces configurations est donc moins probable. Si des extremités proches et se faisant face apparaissent pendant l'évolution par descente de gradient utilisée pour minimiser l'énergie, le nouveau terme dans l'énergie crée une attraction entre ces extremités, qui se rapprochent donc l'une de l'autre et se rejoignent, fermant ainsi le trou entre elles. Pour minimiser l'énergie, nous développons des techniques spécifiques pour traiter les derivées d'ordre élevé qui apparaissent dans l'équation de descente de gradient. Nous présentons des résultats d'extraction automatique de réseaux routiers à partir d'images de télédétection, montrant ainsi la capacité du modèle à surmonter les interruptions. 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 report, we describe a higher-order active contour energy that in addition to favouring network-like regions composed of thin arms joining at junctions, also includes a prior term that penalizes network configurations containing `nearby opposing extremities', and thereby makes their appearance in the extracted network less likely. If nearby opposing extremities form during the gradient descent evolution used to minimize the energy, the new energy term causes the extremities to attract one another, and hence to move towards one another and join, thus 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.

2 - A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models.. M. Ortner and X. Descombes and J. Zerubia. Research Report 5712, INRIA, France, October 2005. Keywords : Marked point process, Buildings, RJMCMC. @TECHREPORT{ortner-RR05, 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 = {2005}, month = {October}, institution = {INRIA}, type = {Research Report}, number = {5712}, address = {France}, url = {https://hal.inria.fr/inria-00070305}, keyword = {Marked point process, Buildings, RJMCMC} } Résumé : Ce travail présente une approche par géométrie stochastique pour l'extraction de primitives dans les images. Ces structures sont modélisées sous forme de réalisations d'un processus ponctuel spatial marqué dont les points sont des formes géométriques. Cette approche permet d'incorporer un modèle a priori sur la répartition spatiale des structures d'intérêt. Plus spécifiquement, nous présentons un modèle fondé sur l'interaction d'un processus de rectangles avec un processus de segments. Le premier est dédié à la détection des zones homogènes dans l'image et le second à la détection des discontinuités significatives. Nous définissons l'énergie d'une configuration de façon à favoriser la connection entre les segments, l'alignement des rectangles et l'adéquation entre les deux types de primitives. L'estimation repose sur l'emploi d'une technique de recuit-simulé. Le modèle proposé est appliqué à l'analyse de Modèles Numériques d'Elevation. Nous présentons des résultats sur des données réelles fournies par l'Institut Géographique National (IGN). Nous montrons en particulier que l'approche est efficace sur des données de types très différents. Abstract : A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models. This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types.

3 - A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5687, INRIA, France, September 2005. Keywords : 3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM). @TECHREPORT{5687, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images}, year = {2005}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {5687}, address = {France}, url = {http://hal.inria.fr/inria-00070326/fr/}, pdf = {https://hal.inria.fr/file/index/docid/70326/filename/RR-5687.pdf}, ps = {http://hal.inria.fr/docs/00/07/03/26/PS/RR-5687.ps}, keyword = {3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM)} } Résumé : Dans ce rapport, nous développons un modèle paramétrique pour la reconstruction automatique de bâtiments en 3D fondé sur une approche bayésienne à partir de simulations PLEIADES. Les images satellitaires haute résolution représentent un nouveau type de données permettant de traiter les problèmes de reconstruction 3D de bâtiments. Leur résolution ``relativement basse'' et leur faible rapport signal sur bruit pour ce type de problèmes ne permet pas l'utilisation des méthodes standard développées dans le cas des images aériennes. Nous proposons une approche paramétrique utilisant des Modèles Numériques d'Elévation (MNE) et les empreintes de bâtiments associées modélisées par rectangles. La méthode proposée est fondée sur une approche bayésienne. Une technique de type de Monte Carlo par Chaînes de Markov est utilisée afin d'optimiser le modèle énergétique. Abstract : This report develops a parametric model for automatic 3D building reconstruction based on a Bayesian approach from PLEIADES simulations. High resolution satellite images are a new kind of data to deal with 3D building reconstruction problems. Their ``relatively low'' resolution and low signal noise ration do not allow to use standard methods developed for the aerial image case. We propose a parametric approach using Digital Elevation Models (DEM) and associated rectangular building footprints. The proposed method is based on a Bayesian approach. A Markov Chain Monte Carlo technique is used to optimize the energy model.

4 - Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Research Report 5697, INRIA, France, September 2005. @TECHREPORT{rrHimne, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework}, year = {2005}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {5697}, address = {France}, url = {http://hal.inria.fr/inria-00070318}, pdf = {http://hal.inria.fr/docs/00/07/03/18/PDF/RR-5697.pdf}, keyword = {} } Résumé : Ce rapport présente un algorithme d'extraction non supervisée de réseaux hydrographiques à partir d'images satellitaires exploitant la structure arborescante de tels réseaux. L'extraction du surfacique (branches de largeur supérieure à trois pixels) est réalisée par un algorithme efficace fondé sur une modélisation par champ de Markov. Ensuite, l'extraction du linéique se fait par un algorithme récursif fondé sur un modèle hiérarchique dans lequel les affluents d'un fleuve donné sont modélisés par un processus ponctuel marqué défini dans le voisinage de ce fleuve. L'optimisation de chaque processus ponctuel est réalisée par un recuit simulé utilisant un algorithme de Monte Carlo par chaîne de Markov à sauts réversibles. Nous obtenons de bons résultats en terme d'omissions et de surdétections sur une image radar de type ERS. Abstract : This report presents a two-step algorithm for unsupervised extraction of hydrographic networks from satellite images, that exploits the tree structures of such networks. First, the thick branches of the network are detected by an efficient algorithm based on a Markov random field. Second, the line branches are extracted using a recursive algorithm based on a hierarchical model of the hydrographic network, in which the tributaries of a given river are modeled by an object process (or a marked point process) defined within the neighborhood of this river. Optimization of each point process is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. We obtain encouraging results in terms of omissions and overdetections on a radar satellite image.

5 - A Polyline Process for Unsupervised Line Network Extraction in Remote Sensing. C. Lacoste and X. Descombes and J. Zerubia. Research Report 5698, INRIA, France, September 2005. @TECHREPORT{rrCaroline, author = {Lacoste, C. and Descombes, X. and Zerubia, J.}, title = {A Polyline Process for Unsupervised Line Network Extraction in Remote Sensing}, year = {2005}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {5698}, address = {France}, url = {http://hal.inria.fr/inria-00070317}, pdf = {http://hal.inria.fr/docs/00/07/03/17/PDF/RR-5698.pdf}, ps = {http://hal.inria.fr/docs/00/07/03/17/PS/RR-5698.ps}, keyword = {} } Résumé : Ce rapport présente un nouveau modèle issu de la géométrie stochastique pour l'extraction non supervisée de réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires ou aériennes. Le réseau linéique présent dans la scène observée est modélisé par un processus de lignes brisées, appelé CAROLINE. Le modèle a priori incorpore de fortes contraintes géométriques et topologiques au travers de potentiels sur la forme des lignes brisées et de potentiels d'interaction. Les propriétés radiométriques sont incorporées via la construction d'un terme d'attache aux données fondé sur des tests statistiques. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation. L'ajout de perturbations pertinentes permet une accélération de la convergence de l'algorithme. Des résultats expérimentaux obtenus sur des images satellitaires et aériennes sont présentés et comparés à ceux obtenus avec un précédent modèle fondé sur un processus de segments, appelé Quality Candy. Abstract : This report presents a new stochastic geometry model for unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates strong geometrical and topological constraints through potentials on the polyline shape and interaction potentials. Data properties are taken into account through a data term based on statistical tests. Optimization is done via a simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Experimental results are provided on aerial and satellite images and compared with the results obtained with a previous model, that is a segment process called Quality Candy.

6 - Optimization Techniques for Energy Minimization Problem in a Marked Point Process Application to Forestry. G. Perrin and X. Descombes and J. Zerubia. Research Report 5704, INRIA, France, September 2005. Keywords : Simulated Annealing, Marked point process, Stochastic geometry, Optimization. @TECHREPORT{rr_perrin_optim_05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Optimization Techniques for Energy Minimization Problem in a Marked Point Process Application to Forestry}, year = {2005}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {5704}, address = {France}, url = {https://hal.inria.fr/inria-00070312}, pdf = {https://hal.inria.fr/file/index/docid/70312/filename/RR-5704.pdf}, ps = {https://hal.inria.fr/docs/00/07/03/12/PS/RR-5704.ps}, keyword = {Simulated Annealing, Marked point process, Stochastic geometry, Optimization} } Résumé : Dans ce rapport de recherche, nous utilisons les processus ponctuels marqués afin d'extraire un nombre inconnu d'objets dans des images aériennes. Ces processus sont définis par une énergie, qui contient un terme a priori formalisant les interactions entre objets ainsi qu'un terme d'attache aux données. Nous cherchons à minimiser cette énergie, afin d'obtenir la meilleure configuration d'objets, à l'aide d'un recuit simulé qui s'inscrit dans l'algorithme d'échantillonnage MCMC à sauts réversibles. Nous comparons ici différents schémas de décroissance de température, et présentons certaines méthodes qui permettent d'améliorer la convergence de l'algorithme en un temps fini. Abstract : We use marked point processes to detect an unknown number of trees from high resolution aerial images. This approach turns to be an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects onto the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects. We compare in this paper different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels.

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