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90 Technical and Research Reports

1 - Classification of very high resolution SAR images of urban areas. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Rapport de recherche 7758, INRIA, October 2011. Keywords : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet. @TECHREPORT{RR-7758, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of very high resolution SAR images of urban areas}, year = {2011}, month = {October}, institution = {INRIA}, type = {Rapport de recherche}, number = {7758}, url = {http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, keyword = {Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} } Résumé : Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en vue d’améliorer la classification finale. Abstract : In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results.

2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Research Report 7666, INRIA, July 2011. Keywords : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (SAR), Classification. Copyright : INRIA/ARIANA @TECHREPORT{RR-7666, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation}, year = {2011}, month = {July}, institution = {INRIA}, type = {Research Report}, number = {7666}, url = {http://hal.archives-ouvertes.fr/inria-00605274/en/}, keyword = {Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (SAR), Classification} } Résumé : L'estimation de paramètres de fonctions de densité de probabilité est une étape majeure dans le domaine du traitement statistique du signal et des images. Dans ce rapport, nous étudions les propriétés et les limites de l'estimation de paramètres par la méthode des cumulants logarithmiques (MoLC), qui est une alternative à la fois au maximum de vraisemblance (MV) classique et à la méthode des moments. Nous dérivons la condition générale suffisante de consistance forte de l'estimation par la méthode MoLC, qui représente une propriété asymptotique importante de tout estimateur statistique. Grâce à cela, nous démontrons la consistance forte de l'estimation par la méthode MoLC pour une sélection de familles de distributions particulièrement adaptées (mais non restreintes) au traitement d'images acquises par radar à synthèse d'ouverture (RSO). Nous dérivons ensuite les conditions analytiques d'applicabilité de la méthode MoLC à des échantillons générés qui suivent les lois des différentes familles de distribution de notre sélection. Enfin, nous testons la méthode MoLC sur des données synthétiques et réelles afin de comparer les différentes propriétés inhérentes aux différents types d'images, l'applicabilité de la méthode et les effets d'un nombre restreint d'échantillons. Nous avons, en particulier, considéré les distributions gamma généralisée et K. Comme exemple d'application, nous avons réalisé des classifications supervisées d'images médicales à ultrason ainsi que d'images de télédétection acquises par des capteurs RSO. Les résultats obtenus montrent que la méthode MoLC est une bonne alternative à la méthode des moments, bien qu'elle contienne certaines limitations. Elle est particulièrement utile lorsqu'une approche directe par MV n'est pas possible. Abstract : Parameter estimation of probability density functions is one of the major steps in the mainframe of statistical image and signal processing. In this report we explore the properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition of strong consistency of MoLC estimates which represents an important asymptotic property of any statistical estimator. With its help we demonstrate the strong consistency of MoLC estimates for a selection of widely used distribution families originating (but not restricted to) synthetic aperture radar (SAR) image processing. We then derive the analytical conditions of applicability of MoLC to samples generated from several distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K family of distributions. Supervised image classification experiments are considered for medical ultrasound and remote sensing SAR imagery. The obtained results suggest MoLC to be a feasible yet not universally applicable alternative to MoM that can be considered when the direct ML approach turns out to be unfeasible.

3 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model. K. Kayabol and J. Zerubia. Research Report 7700, INRIA, July 2011. Keywords : High resolution SAR, Classification, Texture. @TECHREPORT{Kayabol11, author = {Kayabol, K. and Zerubia, J.}, title = {Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, year = {2011}, month = {July}, institution = {INRIA}, type = {Research Report}, number = {7700}, url = {http://hal.archives-ouvertes.fr/hal-00612491/fr/}, keyword = {High resolution SAR, Classification, Texture} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.

4 - Restoration mehod for spatially variant blurred images. S. Ben Hadj and L. Blanc-Féraud. Research Report 7654, INRIA, June 2011. Keywords : Deconvolution, energy minimization, spatially-variant PSF, Total variation. @TECHREPORT{RR_SBH_11, author = {Ben Hadj, S. and Blanc-Féraud, L.}, title = {Restoration mehod for spatially variant blurred images}, year = {2011}, month = {June}, institution = {INRIA}, type = {Research Report}, number = {7654}, url = { http://hal.inria.fr/inria-00602650/fr/}, keyword = {Deconvolution, energy minimization, spatially-variant PSF, Total variation} }

5 - Complex wavelet regularization for 3D confocal microscopy deconvolution. M. Carlavan and L. Blanc-Féraud. Research Report 7366, INRIA, August 2010. Keywords : 3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique. @TECHREPORT{RR-7366, author = {Carlavan, M. and Blanc-Féraud, L.}, title = {Complex wavelet regularization for 3D confocal microscopy deconvolution}, year = {2010}, month = {August}, institution = {INRIA}, type = {Research Report}, number = {7366}, url = {http://hal.inria.fr/inria-00509447/fr/}, keyword = {3D confocal microscopy, Deconvolution, complex wavelet regularization, discrepancy principle, Alternating Direction technique} } Abstract : Confocal microscopy is an increasingly popular technique for 3D imaging of biological specimens which gives images with a very good resolution (several tenths of micrometers), even though degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, some of them being regularized on a Total Variation prior, which gives good results in image restoration but does not allow to retrieve the thin details (including the textures) of the specimens. We rst propose here to use instead a wavelet prior based on the Dual-Tree Complex Wavelet transform to retrieve the thin details of the object. As the regularizing prior eciency also depends on the choice of its regularizing parameter, we secondly propose a method to select the regularizing parameter following a discrepancy principle for Poisson noise. Finally, in order to implement the proposed deconvolution method, we introduce an algorithm based on the Alternating Direction technique which allows to avoid inherent stability problems of the Richardson-Lucy multiplicative algorithm which is widely used in 3D image restoration. We show some results on real and synthetic data, and compare these results to the ones obtained with the Total Variation and the Curvelets priors. We also give preliminary results on a modication of the wavelet transform allowing to deal with the anisotropic sampling of 3D confocal images.

6 - Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution . S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Rapport de recherche 7350, INRIA, July 2010. Keywords : Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction. @TECHREPORT{RR-7350, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution }, year = {2010}, month = {July}, institution = {INRIA}, type = {Rapport de recherche}, number = {7350}, url = {http://hal.archives-ouvertes.fr/inria-00508431/fr/}, keyword = {Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction} }

7 - Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach. C. Benedek and X. Descombes and J. Zerubia. Research Report 7143, INRIA, Sophia Antipolis, December 2009. Keywords : Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics. @TECHREPORT{benedekRR_09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, year = {2009}, month = {December}, institution = {INRIA}, type = {Research Report}, number = {7143}, address = {Sophia Antipolis}, url = {http://hal.archives-ouvertes.fr/inria-00426615}, keyword = {Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics} } Résumé : Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. Abstract : In this report 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.

8 - Space non-invariant point-spread function and its estimation in fluorescence microscopy. P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. Research Report 7157, INRIA, December 2009. Keywords : Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy. @TECHREPORT{ppankajakshan09c, author = {Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, title = {Space non-invariant point-spread function and its estimation in fluorescence microscopy}, year = {2009}, month = {December}, institution = {INRIA}, type = {Research Report}, number = {7157}, url = {http://hal.archives-ouvertes.fr/inria-00438719/en/}, keyword = {Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy} } Résumé : Dans ce rapport de recherche, nous rappelons brièvement comment la nature limitée de diffraction de l'objectif d'un microscope optique, et le bruit intrinsèque peuvent affecter la résolution d'une image observée. Un algorithme de déconvolution aveugle a été proposé en vue de restaurer les fréquences manquants au delà de la limite de diffraction. Cependant, sous d'autres conditions, l'approximation du systéme imageur l'imagerie sans aberration n'est plus valide et donc les aberrations de la phase du front d'onde émergeant d'un médium ne sont plus ignorées. Dans la deuxième partie de ce rapport de recherche, nous montrons que la distribution d'intensité originelle et la localisation d'un objet peuvent être retrouvées uniquement en obtenant de la phase du front d'onde réfracté, à partir d'images d'intensité observées. Nous démontrons cela par obtention de la fonction de ou a partir d'une microsphère imagée. Le bruit et l'influence de la taille de la microsphère peuvent être diminués et parfois complètement supprimes des images observées en utilisant un estimateur maximum a posteriori. Néanmoins, a cause de l'incohérence du système d'acquisition, une récupération de phase a partir d'intensités observées n'est possible que si la restauration de la phase est contrainte. Nous avons utilisé l'optique géométrique pour modéliser la phase du front d'onde réfracté, et nous avons teste l'algorithme sur des images simulées. Abstract : In this research report, we recall briefly how the diffraction-limited nature of an optical microscope's objective, and the intrinsic noise can affect the observed images' resolution. A blind deconvolution algorithm can restore the lost frequencies beyond the diffraction limit. However, under other imaging conditions, the approximation of aberration-free imaging, is not applicable, and the phase aberrations of the emerging wavefront from a specimen immersion medium cannot be ignored any more. We show that an object's location and its original intensity distribution can be recovered by retrieving the refracted wavefront's phase from the observed intensity images. We demonstrate this by retrieving the point-spread function from an imaged microsphere. The noise and the influence of the microsphere size can be mitigated and sometimes completely removed from the observed images by using a maximum a posteriori estimate. However, due to the incoherent nature of the acquisition system, phase retrieval from the observed intensities will be possible only if the phase is constrained. We have used geometrical optics to model the phase of the refracted wavefront, and tested the algorithm on some simulated images.

9 - High resolution SAR-image classification. V. Krylov and J. Zerubia. Research Report 7108, INRIA, November 2009. Keywords : SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula. Copyright : INRIA/ARIANA, 2009 @TECHREPORT{RR-7108, author = {Krylov, V. and Zerubia, J.}, title = {High resolution SAR-image classification}, year = {2009}, month = {November}, institution = {INRIA}, type = {Research Report}, number = {7108}, url = {http://hal.archives-ouvertes.fr/inria-00433036/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/44/81/40/PDF/RR-7108.pdf}, keyword = {SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula} } Résumé : Dans ce rapport, nous proposons une nouvelle approche pour la classification des images de type Radar à Synthèse d’Ouverture (RSO) haute résolution. Cette approche combine la méthode des champs Markoviens (MRF) pour la classification bayésienne et un modèle de mélange fini pour l’estimation des densités de probabilité. Ce modèle de mélange fini est realisé grace à une approche fondée sur une espérance-maximisation stochastique, à partir d'un dictionnaire, pour l’estimation des densités de probabilité d’amplitude. Cette approche semi-automatique est étendue au cas important des images RSO avec plusieurs polarisations, en utilisant des copulas pour modéliser les distributions jointes. Des résultats expérimentaux, sur plusieurs images RSO réelles (Dual-Pol TerraSAR-X et Single-Pol COSMO-SkyMed), pour la classification de zones humides, sont présentés pour montrer l’efficacité de l’algorithme proposé. Abstract : In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed.

10 - A formal Gamma-convergence approach for the detection of points in 2-D images. D. Graziani and L. Blanc-Féraud and G. Aubert. Research Report 7038, INRIA, May 2009. Note : to appear Siam Journal of Imaging Science Keywords : points detection, curvature-depending functionals, divergence-measure fields, Gamma-convergence, biological 2-D images. @TECHREPORT{GRAZIANI_GAMMA_POINTS, author = {Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, title = {A formal Gamma-convergence approach for the detection of points in 2-D images}, year = {2009}, month = {May}, institution = {INRIA}, type = {Research Report}, number = {7038}, note = {to appear Siam Journal of Imaging Science}, url = {https://hal.inria.fr/inria-00418526}, keyword = {points detection, curvature-depending functionals, divergence-measure fields, Gamma-convergence, biological 2-D images} }

11 - Modeling the statistics of high resolution SAR images. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Research Report 6722, INRIA, November 2008. Keywords : Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM). Copyright : INRIA/ARIANA, 2008 @TECHREPORT{krylovDSEM08, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Modeling the statistics of high resolution SAR images}, year = {2008}, month = {November}, institution = {INRIA}, type = {Research Report}, number = {6722}, url = {http://hal.archives-ouvertes.fr/inria-00342681/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/35/76/27/PDF/RR-6722.pdf}, keyword = {Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM)} } Abstract : In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images.

12 - Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept. P. Pankajakshan and L. Blanc-Féraud and B. Zhang and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Research Report 6493, INRIA, April 2008. Keywords : Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation. Copyright : ARIANA/INRIA @TECHREPORT{ppankajakshan08b, author = {Pankajakshan, P. and Blanc-Féraud, L. and Zhang, B. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept}, year = {2008}, month = {April}, institution = {INRIA}, type = {Research Report}, number = {6493}, url = {https://hal.inria.fr/inria-00269265}, pdf = {http://hal.inria.fr/docs/00/27/02/92/PDF/report.pdf}, keyword = {Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation} } Résumé : Nous proposons une méthode de restauration itérative d’images de fluorescence CLSM et d’estimation paramétrique de la fonction de flou (PSF) du système d’acquisition. Le CLSM est un microscope qui balaye un échantillon en 3D et utilise une sténopée pour rejeter la lumière en dehors du point de focalisation. Néanmoins, la qualité des images souffre de deux limitations physiques. La première est due à la diffraction due au système optique et la seconde est due à la quantité réduite de lumière détectée par le tube photo-multiplicateur (PMT). Ces limitations induisent respectivement un flou et du bruit de comptage de photons. Les images peuvent alors bénéficier d’un post-traitement de restauration fondé sur la déconvolution. Le problème à traiter est l’estimation simultanée de la distribution 3D de l’échantillon des sources fluorescentes et de la PSF du microscope (i.e. de déconvolution aveugle). En utilisant un modèle de processus physique d’acquisition d’images microscopiques (CLSM), on réduit le nombre de paramètres libres décrivant la PSF et on introduit des contraintes. On introduit aussi des connaissances a priori sur l’échantillon ce qui permet de stabiliser le processus d’estimation et de favoriser la convergence. Des expériences sur des données synthétiques montrent que la PSF peut être estimée avec précision. Des expériences sur des données réelles montrent de bons resultats de déconvolution en comparaison avec le modèle théorique de la PSF du microscope. Abstract : We propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscope (CLSM) images with parametric estimation of the acquisition system’s Point Spread Function (PSF). The CLSM is an optical fluorescence microscope that scans a specimen in 3D and uses a pinhole to reject most of the out-of-focus light. However, the quality of the image suffers from two primary physical limitations. The first is due to the diffraction-limited nature of the optical system and the second is due to the reduced amount of light detected by the photomultiplier tube (PMT). These limitations cause blur and photon counting noise respectively. The images can hence benefit from post-processing restoration methods based on deconvolution. An efficient method for parametric blind image deconvolution involves the simultaneous estimation of the specimen 3D distribution of fluorescent sources and the microscope PSF. By using a model for the microscope image acquisition physical process, we reduce the number of free parameters describing the PSF and introduce constraints. The parameters of the PSF may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We also introduce a priori knowledge of the specimen that permits stabilization of the estimation process and favorizes the convergence. Experiments on simulated data show that the PSF could be estimatedwith a higher degree of accuracy and those done on real data show very good deconvolution results in comparison to the theoretical microscope PSF model.

13 - On the illumination invariance of the level lines under directed light. Application to change detection. P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. Research Report 6612, INRIA, 2008. Keywords : Level Lines, illumination invariance, topographic map, Change detection, remote sensing, Urban areas. Copyright : @TECHREPORT{RR-6612, author = {Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, title = {On the illumination invariance of the level lines under directed light. Application to change detection}, year = {2008}, institution = {INRIA}, type = {Research Report}, number = {6612}, url = {https://hal.archives-ouvertes.fr/inria-00310383}, pdf = {http://hal.inria.fr/docs/00/31/03/83/PDF/RR-6612.pdf}, keyword = {Level Lines, illumination invariance, topographic map, Change detection, remote sensing, Urban areas} } Abstract : We analyze the illumination invariance of the level lines of an image. We show that if the scene surface has Lambertian reflectance and the light is directed, then a necessary condition for the level lines to be illumination invariant is that the 3D scene be developable and that its albedo satisfies some geometrical constraints. We then show that the level lines are ``almost'' invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. In a second part, this allows us to devise a very fast algorithm that detects changes between pairs of remotely sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of the algorithm both on synthetic OpenGL scenes and real Quickbird images. We compare the efficiency of the proposed algorithm with other classical approaches and show that it is superior both in practice and in theory.

14 - Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier. M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. Research Report 6732, INRIA, 2008. Keywords : l1 norm, nesterov scheme, total variation minimization, wavelet. Copyright : @TECHREPORT{RR-6732, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, title = {Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier}, year = {2008}, institution = {INRIA}, type = {Research Report}, number = {6732}, url = {http://hal.archives-ouvertes.fr/inria-00340975/fr/}, pdf = {http://hal.inria.fr/docs/00/34/09/75/PDF/RR-6732.pdf}, keyword = {l1 norm, nesterov scheme, total variation minimization, wavelet} }

15 - Support Vector Machines for burnt area discrimination. O. Zammit and X. Descombes and J. Zerubia. Research Report 6343, INRIA, November 2007. Keywords : Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification. @TECHREPORT{zammit_RR_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Support Vector Machines for burnt area discrimination}, year = {2007}, month = {November}, institution = {INRIA}, type = {Research Report}, number = {6343}, url = {http://hal.inria.fr/inria-00185101/fr/}, pdf = {http://hal.inria.fr/inria-00185101/fr/}, keyword = {Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification} } Résumé : Ce rapport aborde le problème de l'évaluation des dégâts après un feux de forêt. La détection est effectuée à partir d'une seule image satellite (SPOT 5) acquise après le feu. Afin de détecter les zones brûlées, nous utilisons une approche récente de classification nommée SVM (Séparateurs à Vaste Marge). Cette méthode est comparée aux algorithmes de classification plus conventionnels comme les K-moyennes ou les K-plus proches voisins, qui sont régulièrement utilisés en traitement d'image. Nous proposons également une méthode de classification non supervisée combinant les K-moyennes et les SVM. Les résultats fournis par les différentes techniques sont comparés à des vérités de terrain sur diverses zones brûlées. Abstract : This report addresses the problem of burnt area discrimination using remote sensing images. The detection is based on a single post-fire image acquired by SPOT 5 satellite. To delineate the burnt areas, we use a recent classification method called Support Vectors Machines (SVM). This approach is compared to more conventional classifiers such as K-means or K-nearest neighbours which are widely used in image processing. We also proposed a new automatic classification approach combining K-means and SVM. The results given by the different methods are finally compared to ground truths on various burnt areas

16 - 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. Research Report 6328, INRIA, October 2007. Keywords : Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses. @TECHREPORT{Descamps-Descombes, 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 = {2007}, month = {October}, institution = {INRIA}, type = {Research Report}, number = {6328}, url = {http://hal.inria.fr/inria-00180811}, pdf = {http://hal.inria.fr/docs/00/18/08/93/PDF/RR-Desc-Desc-Bech-Zeru.pdf}, keyword = {Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses} }

17 - An adaptive simulated annealing cooling schedule for object detection in images. M. Ortner and X. Descombes and J. Zerubia. Research Report 6336, INRIA, October 2007. Keywords : Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule. @TECHREPORT{Ortner-Descombes, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {An adaptive simulated annealing cooling schedule for object detection in images}, year = {2007}, month = {October}, institution = {INRIA}, type = {Research Report}, number = {6336}, url = {https://hal.inria.fr/inria-00181764}, pdf = {https://hal.inria.fr/inria-00181764}, keyword = {Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule} }

18 - Efficient schemes for total variation minimization under constraints in image processing. P. Weiss and L. Blanc-Féraud and G. Aubert. Research Report 6260, INRIA, July 2007. Keywords : l1 norm, total variation minimization, duality lp norms, gradient and subgradient descent, nesterov scheme, texture + geometry decomposition. @TECHREPORT{RR-6260, author = {Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {Efficient schemes for total variation minimization under constraints in image processing}, year = {2007}, month = {July}, institution = {INRIA}, type = {Research Report}, number = {6260}, url = {http://hal.inria.fr/inria-00166096/fr/}, pdf = {http://hal.inria.fr/docs/00/26/16/35/PDF/RR-6260.pdf}, ps = {http://hal.inria.fr/docs/00/26/16/35/PS/RR-6260.ps}, keyword = {l1 norm, total variation minimization, duality lp norms, gradient and subgradient descent, nesterov scheme, texture + geometry decomposition} } Résumé : Ce papier présente de nouveaux algorithmes pour minimiser la variation totale, et plus généralement des normes l^1, sous des contraintes convexes. Ces algorithmes proviennent d'une avancée récente en optimisation convexe proposée par Yurii Nesterov. Suivant la régularité de l'attache aux données, nous résolvons soit un problème primal, soit un problème dual. Premièrement, nous montrons que les schémas standard de premier ordre permettent d'obtenir des solutions de précision epsilon en O(frac1epsilon^2) itérations au pire des cas. Pour une contrainte convexe quelconque, nous proposons un schéma qui permet d'obtenir une solution de précision epsilon en O(frac1epsilon) itérations. Pour une contrainte fortement convexe, nous résolvons un problème dual avec un schéma qui demande O(frac1sqrtepsilon) itérations pour obtenir une solution de précision epsilon. Suivant la contrainte, nous gagnons donc un à deux ordres dans la rapidité de convergence par rapport à des approches standard. Finalement, nous faisons quelques expériences numériques qui confirment les résultats théoriques sur de nombreux problèmes. Abstract : This paper presents new algorithms to minimize total variation and more generally l^1-norms under a general convex constraint. The algorithms are based on a recent advance in convex optimization proposed by Yurii Nesterov citeNESTEROV. Depending on the regularity of the data fidelity term, we solve either a primal problem, either a dual problem. First we show that standard first order schemes allow to get solutions of precision epsilon in O(frac1epsilon^2) iterations at worst. For a general convex constraint, we propose a scheme that allows to obtain a solution of precision epsilon in O(frac1epsilon) iterations. For a strongly convex constraint, we solve a dual problem with a scheme that requires O(frac1sqrtepsilon) iterations to get a solution of precision epsilon. Thus, depending on the regularity of the data term, we gain from one to two orders of magnitude in the convergence rates with respect to standard schemes. Finally we perform some numerical experiments which confirm the theoretical results on various problems.

19 - A Three-layer MRF model for Object Motion Detection in Airborne Images. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. Research Report 6208, INRIA, June 2007. Keywords : Aerial images, Change detection, Camera motion, MRF. @TECHREPORT{benedek_INRIARR07, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {A Three-layer MRF model for Object Motion Detection in Airborne Images}, year = {2007}, month = {June}, institution = {INRIA}, type = {Research Report}, number = {6208}, url = {https://hal.inria.fr/inria-00150805}, pdf = {https://hal.inria.fr/inria-00150805}, keyword = {Aerial images, Change detection, Camera motion, MRF} }

20 - Object extraction using a stochastic birth-and-death dynamics in continuum. X. Descombes and R. Minlos and E. Zhizhina. Research Report 6135, INRIA, 2007. Keywords : birth and death process, Stochastic modeling, Wavelets. @TECHREPORT{RR-6135, author = {Descombes, X. and Minlos, R. and Zhizhina, E.}, title = {Object extraction using a stochastic birth-and-death dynamics in continuum}, year = {2007}, institution = {INRIA}, type = {Research Report}, number = {6135}, url = {https://hal.inria.fr/inria-00133726}, pdf = {http://hal.inria.fr/inria-00133726}, keyword = {birth and death process, Stochastic modeling, Wavelets} } Abstract : We define a new birth and death dynamics dealing with configurations of discs 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 extracting objects. 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.

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