Publications of 2009

Result of the query in the list of publications :

• 10 Articles
• 3 PhD Thesis and Habilitations
• 22 Conference articles
• 4 Technical and Research Reports
• 2 Collection articles or Books chapters

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10 Articles

1 - Comparative study on the performance of multi paramater SAR data for operational urban areas extraction. C. Corbane and N. Baghdadi and X. Descombes and M. Petit. IEEE-Geoscience and Remote Sensing Letters, 6(4): pages 728-732, October 2009. Keywords : Markov random field model, synthetic aperture radar, urban remote sensing. @ARTICLE{COR-09, author = {Corbane, C. and Baghdadi, N. and Descombes, X. and Petit, M.}, title = {Comparative study on the performance of multi paramater SAR data for operational urban areas extraction}, year = {2009}, month = {October}, journal = {IEEE-Geoscience and Remote Sensing Letters}, volume = {6}, number = {4}, pages = {728-732}, url = {http://dx.doi.org/10.1109/LGRS.2009.2024225}, keyword = {Markov random field model, synthetic aperture radar, urban remote sensing} } Abstract : The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m.

2 - Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. IEEE Trans. Image Processing, 18(10): pages 2303-2315, October 2009. Keywords : Change detection, Aerial images, Camera motion, MRF. @ARTICLE{benedekTIP09, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Image Processing}, volume = {18}, number = {10}, pages = {2303-2315}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5089480}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov Random Field model which integrates information from two different features, and ensures connected homogeneous regions in the segmented images. Validation is given on real aerial photos.

3 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model. C. Benedek and T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, October 2009. Keywords : mixed Markov models, Change detection, Aerial images, MAP estimation. Copyright : IEEE @ARTICLE{benedekTGRS09, author = {Benedek, C. and Szirányi, T.}, title = {Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {47}, number = {10}, pages = {3416-3430}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5257398&arnumber=5169964&count=26&index=11}, keyword = {mixed Markov models, Change detection, Aerial images, MAP estimation} } Abstract : In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth.

4 - Looking for shapes in two-dimensional, cluttered point clouds. A. Srivastava and I. H. Jermyn. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(9): pages 1616-1629, September 2009. Keywords : Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao. 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{SrivastavaJermyn09, author = {Srivastava, A. and Jermyn, I. H.}, title = {Looking for shapes in two-dimensional, cluttered point clouds}, year = {2009}, month = {September}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {31}, number = {9}, pages = {1616-1629}, url = {http://dx.doi.org/10.1109/TPAMI.2008.223}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/SrivastavaJermyn09.pdf}, keyword = {Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao} } Abstract : We study the problem of identifying shape classes in point clouds. These clouds contain sampled contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learnt from training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: (i) shape variations within classes, (ii) variability in sampling continuous curves, (iii) pose and scale variability, (iv) observation noise, and (v) points introduced by clutter. The variability in sampling closed curves into finite points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their squareroot forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of posterior probabilities of different classes and, hence, Bayesian classification.

5 - Blind deconvoltion for thin layered confocal imaging. P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Applied Optics, 48(22): pages 4437-4448, August 2009. Keywords : Blind Deconvolution, Confocal microscopy, Inverse Problems. Copyright : Optical Society of America @ARTICLE{ppankajakshan09b, author = {Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Blind deconvoltion for thin layered confocal imaging}, year = {2009}, month = {August}, journal = {Applied Optics}, volume = {48}, number = {22}, pages = {4437-4448}, pdf = {http://hal.inria.fr/docs/00/39/55/23/PDF/AppliedOpticsPaperTypesetting.pdf}, keyword = {Blind Deconvolution, Confocal microscopy, Inverse Problems} } Abstract : We propose an alternate minimization algorithm for estimating the point-spread function (PSF) of a confocal laser scanning microscope and the specimen fluorescence distribution. A three-dimensional separable Gaussian model is used to restrict the PSF solution space and a constraint on the specimen is used so as to favor the stabilization and convergence of the algorithm. The results obtained from the simulation show that the PSF can be estimated to a high degree of accuracy, and those on real data show better deconvolution as compared to a full theoretical PSF model.

6 - Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation. G. Scarpa and R. Gaetano and M. Haindl and J. Zerubia. IEEE Trans. on Image Processing, 18(8): pages 1830-1843, August 2009. Keywords : Hierarchical Image Models, Markov Process, Pattern Analysis. @ARTICLE{ScarpaTIP09, author = {Scarpa, G. and Gaetano, R. and Haindl, M. and Zerubia, J.}, title = { Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation}, year = {2009}, month = {August}, journal = {IEEE Trans. on Image Processing}, volume = {18}, number = {8}, pages = {1830-1843}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5161445&arnumber=4914796&count=21&index=11}, keyword = {Hierarchical Image Models, Markov Process, Pattern Analysis} } Abstract : In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.

7 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations. S. Descamps and X. Descombes and A. Béchet and J. Zerubia. Traitement du Signal, 26(2): pages 95-108, July 2009. Keywords : flamants roses. @ARTICLE{flamTS, author = {Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, title = {Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, year = {2009}, month = {July}, journal = {Traitement du Signal}, volume = {26}, number = {2}, pages = {95-108}, pdf = {http://documents.irevues.inist.fr/handle/2042/28809}, keyword = {flamants roses} } Résumé : Nous présentons dans cet article une nouvelle technique de détection de flamants roses sur des images aériennes. Nous considérons une approche stochastique fondée sur les processus ponctuels marqués, aussi appelés processus objets. Ici, les objets représentent les flamants, qui sont modélisés par des ellipses. La densité associée au processus ponctuel marqué d'ellipses est définie par rapport à une mesure de Poisson. Dans un cadre gibbsien, le problème se réduit à la minimisation d'une énergie, qui est constituée d'un terme de régularisation (densité a priori), qui introduit des contraintes sur les objets et leurs interactions; et un terme d'attache aux données, qui permet de localiser sur l'image les flamants à extraire. Nous échantillonnons le processus pour extraire la configuration d'objets minimisant l'énergie grâce à une nouvelle dynamique de Naissances et Morts multiples, amenant finalement à une estimation du nombre total de flamants présents sur l'image. Cette approche donne des comptes avec une bonne précision comparée aux comptes manuels. De plus, elle ne nécessite aucun traitement préalable ou intervention manuelle, ce qui réduit considérablement le temps d'obtention des comptes.

8 - A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Pattern Recognition, 42(5): pages 699-709, May 2009. Keywords : Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian. @ARTICLE{Horvath09, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction}, year = {2009}, month = {May}, journal = {Pattern Recognition}, volume = {42}, number = {5}, pages = {699-709}, url = {http://dx.doi.org/10.1016/j.patcog.2008.09.008}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Horvathetal09.pdf}, keyword = {Shape, Higher-order, Active contour, Gas of circles, Tree Crown Extraction, Bayesian} } Abstract : We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown number of circles of approximately the same radius. The model has applications to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed using higher-order active contours (HOACs) in order to include non-trivial prior knowledge about region shape without constraining topology. The main theoretical contribution is an analysis of the local minima of the HOAC energy that allows us to guarantee stable circles, fix one of the model parameters, and constrain the rest. We apply the model to tree crown extraction from aerial images of plantations. Numerical experiments both confirm the theoretical analysis and show the empirical importance of the prior shape information.

9 - Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum. X. Descombes and R. Minlos and E. Zhizhina. Journal of Mathematical Imaging and Vision, 33(3): pages 347-359, 2009. Keywords : birth and death process, Marked point process, Object extraction. Copyright : Springer @ARTICLE{DZM08, author = {Descombes, X. and Minlos, R. and Zhizhina, E.}, title = {Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum}, year = {2009}, journal = {Journal of Mathematical Imaging and Vision}, volume = {33}, number = {3}, pages = {347-359}, pdf = {http://dx.doi.org/10.1007/s10851-008-0117-y}, keyword = {birth and death process, Marked point process, Object extraction} } Abstract : We define a new birth and death dynamics dealing with configurations of disks in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in detecting a configuration of objects from a digital image. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data.

10 - Efficient schemes for total variation minimization under constraints in image processing. P. Weiss and L. Blanc-Féraud and G. Aubert. SIAM journal on Scientific Computing, 31(3): pages 2047-2080, 2009. Keywords : Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time. Copyright : Copyright Siam Society for Industrial and Applied @ARTICLE{SIAM_JSC_PWEISS, author = {Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {Efficient schemes for total variation minimization under constraints in image processing}, year = {2009}, journal = {SIAM journal on Scientific Computing}, volume = {31}, number = {3}, pages = {2047-2080}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, keyword = {Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time} }

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3 PhD Thesis and Habilitations

1 - Blind Deconvolution for Confocal Laser Scanning Microscopy. P. Pankajakshan. PhD Thesis, Universite de Nice Sophia Antipolis, December 2009. Keywords : Confocal Laser Scanning Microscopy, Blind Deconvolution, point spread function, Maximum likelihood estimation , total variation regularization. @PHDTHESIS{PankajakshanThesis09, author = {Pankajakshan, P.}, title = {Blind Deconvolution for Confocal Laser Scanning Microscopy}, year = {2009}, month = {December}, school = {Universite de Nice Sophia Antipolis}, url = {http://tel.archives-ouvertes.fr/tel-00474264/fr/}, keyword = {Confocal Laser Scanning Microscopy, Blind Deconvolution, point spread function, Maximum likelihood estimation , total variation regularization} } Résumé : La microscopie confocale à balayage laser, est une technique puissante pour étudier les spécimens biologiques en trois dimensions (3D) par sectionnement optique. Elle permet d’avoir des images de spécimen vivants à une résolution de l’ordre de quelques centaines de nanomètres. Bien que très utilisée, il persiste des incertitudes dans le procédé d’observation. Comme la réponse du système à une impulsion, ou fonction de flou (PSF), est dépendante à la fois du spécimen et des conditions d’acquisition, elle devrait être estimée à partir des images observées du spécimen. Ce problème est mal posé et sous déterminé. Pour obtenir une solution, il faut injecter des connaisances, c’est à dire, a priori dans le problème. Pour cela, nous adoptons une approche bayésienne. L’état de l’art des algorithmes concernant la déconvolution et la déconvolution aveugle est exposé dans le cadre d’un travail bayésien. Dans la première partie, nous constatons que la diffraction due à l’objectif et au bruit intrinsèque à l’acquisition, sont les distorsions principales qui affectent les images d’un spécimen. Une approche de minimisation alternée (AM), restaure les fréquences manquantes au-delà de la limite de diffraction, en utilisant une régularisation par la variation totale sur l’objet, et une contrainte de forme sur la PSF. En outre, des méthodes sont proposées pour assurer la positivité des intensités estimées, conserver le flux de l’objet, et bien estimer le paramètre de la régularisation. Quand il s’agit d’imager des spécimens épais, la phase de la fonction pupille, due aux aberrations sphériques (SA) ne peut être ignorée. Dans la seconde partie, il est montré qu’elle dépend de la difference à l’index de réfraction entre l’objet et le milieu d’immersion de l’objectif, et de la profondeur sous la lamelle. Les paramètres d’imagerie et la distribution de l’intensité originelle de l’objet sont calculés en modifiant l’algorithme AM. Due à la nature de la lumière incohérente en microscopie à fluorescence, il est possible d’estimer la phase à partir des intensités observées en utilisant un modèle d’optique géométrique. Ceci a été mis en évidence sur des données simulées. Cette méthode pourrait être étendue pour restituer des spécimens affectés par les aberrations sphériques. Comme la PSF varie dans l’espace, un modèle de convolution par morceau est proposé, et la PSF est approchée. Ainsi, en plus de l’objet, il suffit d’estimer un seul paramétre libre. Abstract : Confocal laser scanning microscopy is a powerful technique for studying biological specimens in three dimensions (3D) by optical sectioning. It permits to visualize images of live specimens non-invasively with a resolution of few hundred nanometers. Although ubiquitous, there are uncertainties in the observation process. As the system’s impulse response, or point-spread function (PSF), is dependent on both the specimen and imaging conditions, it should be estimated from the observed images in addition to the specimen. This problem is ill-posed, under-determined. To obtain a solution, it is necessary to insert some knowledge in the form of a priori and adopt a Bayesian approach. The state of the art deconvolution and blind deconvolution algorithms are reviewed within a Bayesian framework. In the first part, we recognize that the diffraction-limited nature of the objective lens and the intrinsic noise are the primary distortions that affect specimen images. An alternative minimization (AM) approach restores the lost frequencies beyond the diffraction limit by using total variation regularization on the object, and a spatial constraint on the PSF. Additionally, some methods are proposed to ensure positivity of estimated intensities, to conserve the object’s flux, and to well handle the regularization parameter. When imaging thick specimens, the phase of the pupil function due to spherical aberration (SA) cannot be ignored. It is shown to be dependent on the refractive index mismatch between the object and the objective immersion medium, and the depth under the cover slip. The imaging parameters and the object’s original intensity distribution are recovered by modifying the AM algorithm. Due to the incoherent nature of the light in fluorescence microscopy, it is possible to retrieve the phase from the observed intensities by using a model derived from geometrical optics. This was verified on the simulated data. This method could also be extended to restore specimens affected by SA. As the PSF is space varying, a piecewise convolution model is proposed, and the PSF approximated so that, apart from the specimen, it is sufficient to estimated only one free parameter.

2 - Shape recognition for image scene analysis. M. S. Kulikova. PhD Thesis, Universite de Nice - Sophia-Antipolis, December 2009. Keywords : tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior. @PHDTHESIS{mkulikova_phd09, author = {Kulikova, M. S.}, title = {Shape recognition for image scene analysis}, year = {2009}, month = {December}, school = {Universite de Nice - Sophia-Antipolis}, url = {http://tel.archives-ouvertes.fr/docs/00/48/20/19/PDF/phd_mkulikova_2009.pdf}, keyword = {tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior} } Résumé : Cette thèse est composée de deux parties principales. La première partie est dédiée au problème de la classification d’espèces d’arbres en utilisant des descripteurs de forme, en combainison ou non, avec ceux de radiométrie ou de texture. Nous montrons notamment que l’information sur la forme améliore la performance d’un classifieur. Pour ce faire, dans un premier temps, une étude des formes de couronnes d’arbres extraites à partir d’images aériennes, en infrarouge couleur, est eectuée en utilisant une méthodologie d’analyse de formes des courbes continues fermées dans un espace de formes, en utilisant la notion de chemin géodésique sous deux métriques dans des espaces appropriés : une métrique non-élastique en utilisant la reprèsentation par la fonction d’angle de la courbe, ainsi qu’une métrique élastique induite par une représentation par la racinecarée appelée q-fonction. Une étape préliminaire nécessaire à la classification est l’extraction des couronnes d’arbre. Dans une seconde partie, nous abordons donc le problème de l’extraction d’objets de forme complexe arbitraire, à partir d’images de télédétection à très haute résolution. Nous construisons un modèle fondé sur les processus ponctuels marqués. Son originalité tient dans sa prise en compte d’objets de forme arbitraire par rapport aux objets de forme paramétrique, e.g. ellipses ou rectangles. Les formes sélectionnées sont obtenues par la minimisation locale d’une énergie de type contours actifs avec diérents a priori sur la forme incorporé. Les objets de la configuration finale (optimale) sont ensuite sélectionnés parmi les candidats par une dynamique de naissances et morts multiples, couplée à un schéma de recuit simulé. L’approche est validée sur des images de zones forestières à très haute résolution fournies par l’Université d’Agriculture de Suède. Abstract : This thesis includes two main parts. In the first part we address the problem of tree crown classification into species using shape features, without, or in combination with, those of radiometry and texture, to demonstrate that shape information improves classification performance. For this purpose, we first study the shapes of tree crowns extracted from very high resolution colour aerial infra-red images. For our study, we choose a methodology based on the shape analysis of closed continuous curves on shape spaces using geodesic paths under the bending metric with the angle-function curve representation, and the elastic metric with the square root q-function representation. A necessary preliminary step to classification is extraction of the tree crowns. In the second part, we address thus the problem of extraction of multiple objects with complex, arbitrary shape from remote sensing images of very high resolution. We develop a model based on marked point processes. Its originality lies in its use of arbitrarily-shaped objects as opposed to parametric shape objects, e.g. ellipses or rectangles. The shapes considered are obtained by local minimisation of an active contour energy with weak and then strong shape prior knowledge included. The objects in the final (optimal) configuration are then selected from amongst these candidates by a multiple birth-and-death dynamics embedded in an annealing scheme. The approach is validated on very high resolution images of forest provided by the Swedish University of Agriculture.

3 - Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.. A. Baudour. PhD Thesis, Universite de Nice Sophia Antipolis, May 2009. Keywords : imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale. @PHDTHESIS{baudour2009, author = {Baudour, A.}, title = {Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.}, year = {2009}, month = {May}, school = {Universite de Nice Sophia Antipolis}, url = {https://hal.inria.fr/tel-00507520/}, keyword = {imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale} } Résumé : Cette thèse aborde le problème de la modélisation et de la détection des laments dans des images 3D. Nous avons développé des méthodes variationnelles pour quatre applications spéciques :  l'extraction de routes où nous avons introduit la notion de courbure totale pour conserver les réseaux réguliers en tolérant les discontinuités de direction.  la détection et la complétion de laments fortement bruités et présentant des occultation. Nous avons utilisé la magnétostatique et la théorie de Ginzburg-Landau pour représenter les laments comme ensemble de singularités d'un champ vectoriel.  la détection de laments dans des images biologiques acquises en microscopie confocale. On modélise les laments en tenant compte des spécicité de cette dernière. Les laments sont alors obtenus par une méthode de maximum à posteriori.  la détection de cible dans des séquences d'images infrarouges. Dans cette application, on cherche des trajectoires optimisant la diérence de luminosit é moyenne entre la trajectoire et son voisinage en tenant compte des capteurs utilisés. Par ailleurs, nous avons démontré des résultats théoriques portant sur la courbure totale et la convergence de la méthode d'Alouges associée aux systèmes de Ginzburg-Landau. Ce travail réunit à la fois modélisation, résulats théoriques et recherche d'algorithmes numériques performants permettant de traiter de réelles applications.

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22 Conference articles

1 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, December 2009. Keywords : Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics. @INPROCEEDINGS{Kulikova09a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects}, year = {2009}, month = {December}, booktitle = {Proc. IEEE SITIS}, publisher = {IEEE Computer Society}, address = {Marrakech, Maroc}, pdf = {http://hal.inria.fr/docs/00/43/63/20/PDF/PID1054029.pdf}, keyword = {Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics} } Abstract : We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns.

2 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009. Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images. @INPROCEEDINGS{benedekWacv09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, year = {2009}, month = {December}, booktitle = {IEEE Workshop on Applications of Computer Vision (WACV)}, pages = {100-105}, address = {Snowbird, Utah, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, keyword = {Marked point process, Change detection, Aerial images, Building extraction, Satellite images} } 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. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.

3 - Reconstruction 3D du bâti par la technique des ombres chinoises. P. Lukashevish and A. Kraushonak and X. Descombes and J.D. Durou and B. Zalessky and E. Zhizhina. In GRETSI Dijon, Dijon, France, November 2009. Keywords : 3D reconstruction. @INPROCEEDINGS{luka09, author = {Lukashevish, P. and Kraushonak, A. and Descombes, X. and Durou, J.D. and Zalessky, B. and Zhizhina, E.}, title = {Reconstruction 3D du bâti par la technique des ombres chinoises}, year = {2009}, month = {November}, booktitle = {GRETSI Dijon}, address = {Dijon, France}, url = {http://hal.inria.fr/inria-00399208/fr/}, keyword = {3D reconstruction} }

4 - Combining meshes and geometric primitives for accurate and semantic modeling. F. Lafarge and R. Keriven and M. Brédif. In Proc. British Machine Vision Conference (BMVC), London, U.K., November 2009. @INPROCEEDINGS{lafarge_bmvc09, author = {Lafarge, F. and Keriven, R. and Brédif, M.}, title = {Combining meshes and geometric primitives for accurate and semantic modeling}, year = {2009}, month = {November}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {London, U.K.}, url = {http://recherche.ign.fr/labos/matis/pdf/articles_conf/2009/bmvc_final_09.pdf}, keyword = {} }

5 - A markov random field model for extracting near-circular shapes. T. Blaskovics and Z. Kato and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Segmentation, Markov Random Fields, Shape prior. @INPROCEEDINGS{Blaskovics09, author = {Blaskovics, T. and Kato, Z. and Jermyn, I. H.}, title = {A markov random field model for extracting near-circular shapes}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5413472}, keyword = {Segmentation, Markov Random Fields, Shape prior} }

6 - Object extraction from high resolution SAR images using a birth and death dynamics. F. Arslan and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : High resolution SAR images, Object extraction, Marked point process, birth and death process. @INPROCEEDINGS{Fatih09, author = {Arslan, F. and Descombes, X. and Zerubia, J.}, title = {Object extraction from high resolution SAR images using a birth and death dynamics}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413907}, keyword = {High resolution SAR images, Object extraction, Marked point process, birth and death process} } Abstract : We present a new approach to extract predefined objects, such as trees and oil tanks for instance, from high resolution SAR images. We consider a stochastic approach based on an object process also called marked point process. The objects represent trees or oil tanks which are modeled by disks in the image. We first define a Gibbs density that takes into account both prior information and the data. The energy we define is composed of two terms, one is a prior, penalizing overlaps between objects, and the other is a data term, which measures the suitability of an object in the SAR image. The problem is then reduced to an energy minimization problem. We sample the process to extract the configuration of objects minimizing the energy by a fast birth-and-death dynamics, leading to the total number of objects (trees or oil tanks in our case). This approach is much faster than manual counts and does not need any preprocessing or supervision of a user.

7 - Multi-class SVM for forestry classification. N. Hajj Chehade and JG. Boureau and C. Vidal and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation. @INPROCEEDINGS{Nabil09, author = {Hajj Chehade, N. and Boureau, JG. and Vidal, C. and Zerubia, J.}, title = {Multi-class SVM for forestry classification}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413395}, keyword = {Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation} } Abstract : In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map.

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