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245 Conference articles

51 - A phase field higher-order active contour model of directed networks. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV, Kyoto, Japan, September 2009. Keywords : Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks. Copyright : ©2009 IEEE. @INPROCEEDINGS{ElGhoul09b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A phase field higher-order active contour model of directed networks}, year = {2009}, month = {September}, booktitle = {2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV}, address = {Kyoto, Japan}, url = {https://hal.inria.fr/inria-00409910}, pdf = {http://hal.inria.fr/docs/00/40/99/10/PDF/nordia09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks} } Abstract : The segmentation of directed networks is an important problem in many domains, e.g. medical imaging (vascular networks) and remote sensing (river networks). Directed networks carry a unidirectional flow in each branch, which leads to characteristic geometric properties. In this paper, we present a nonlocal phase field model of directed networks. In addition to a scalar field representing a region by its smoothed characteristic function and interacting nonlocally so as to favour network configurations, the model contains a vector field representing the ‘flow’ through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the region; and to have zero divergence. This prolongs network branches; controls width variation along a branch; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. In conjunction with a new interaction function, it also allows a broad range of stable branch widths. We analyse the energy to constrain the parameters, and show geometric experiments confirming the above behaviour. We also show a segmentation result on a synthetic river image.

52 - Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes. M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009. Keywords : Deconvolution, nesterov scheme, Wavelets, l1 norm. @INPROCEEDINGS{GRETSICarlavan09, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, title = {Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes}, year = {2009}, month = {September}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Dijon, France}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, keyword = {Deconvolution, nesterov scheme, Wavelets, l1 norm} } Résumé : De nombreuses méthodes de restauration d'images consistent à minimiser une énergie convexe. Nous nous focalisons sur l'utilisation de ces méthodes et considérons la minimisation de deux critères contenant une norme l1 des coefficients en ondelettes. La plupart des travaux publiés récemment proposent un critère à minimiser dans le domaine des coefficients en ondelettes, utilisant ainsi un a priori de parcimonie. Nous proposons un algorithme rapide et des résultats de déconvolution par minimisation d'un critère dans le domaine image, avec un a priori de régularité exprimé dans le domaine image utilisant une décomposition redondante sur une trame. L'algorithme et le modèle proposés semblent originaux pour ce problème en traitement d'images et sont performants en terme de temps de calculs et de qualité de restauration. Nous montrons des comparaisons entre les deux types d' a priori. Abstract : Many image restoration techniques are based on convex energy minimization. We focus on the use of these techniques and consider the minimization of two criteria holding a l1-norm of wavelet coefficients. Most of the recent research works are based on the minimization of a criterion in the wavelet coefficients domain, namely as a sparse prior. We propose a fast algorithm and deconvolution results obtained by minimizing a criterion in the image domain using a redundant decomposition on a frame. The algorithm and model proposed are unusual for this problem and very efficient in term of computing time and quality of restoration results. We show comparisons between the two different priors.

53 - Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes. C. Chaux and L. Blanc-Féraud. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009. @INPROCEEDINGS{ChauxGRETSI09, author = {Chaux, C. and Blanc-Féraud, L.}, title = {Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes}, year = {2009}, month = {September}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Dijon, France}, url = {http://hdl.handle.net/2042/28911}, keyword = {} } Résumé : Nous nous intéressons à l'estimation des paramètres de régularisation pour la restauration d'image floue et bruitée. Dans l'approche variationnelle, la restauration consiste à minimiser un critère convexe composé d'un terme de rappel aux données (quadratique) et d'un terme de régularisation (norme I1) opérant dans le domaine ondelettes. Nous proposons une méthode d'estimation des paramètres de régularisation (hyperparamètres, un par sous-bande) par maximum de vraisemblance, à partir de la seule image observée. La difficulté de l'estimation en données incomplètes est de pouvoir échantillonner des lois sur des champs de variables aléatoires dont les interactions entre voisins sont étendues, du fait de l'opérateur linéaire de flou. Nous proposons une méthode qui permet de calculer ces échantillons par MCMC (échantillonnage de Gibbs et Metropolis-Hastings). Pour l'estimation, nous utilisons une méthode de gradient. Les résultats de simulation obtenus montrent la faisabilité de la méthode et ses bonnes performances en terme d'estimation.

54 - Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009. Keywords : Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing. Copyright : EURASIP @INPROCEEDINGS{ElGhoul09a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images}, year = {2009}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Glasgow, Scotland}, url = {http://hal.inria.fr/inria-00390446/fr/}, pdf = {http://hal.inria.fr/docs/00/39/04/46/PDF/eusipco09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC ‘inflection point’ model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images.

55 - Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster). F. Chatelain and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Bonn, Germany, August 2009. @INPROCEEDINGS{ChatelainEMMCVPR09, author = {Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster)}, year = {2009}, month = {August}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {Bonn, Germany}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-03641-5_17}, keyword = {} }

56 - A proximal method for inverse problems in image processing. P. Weiss and L. Blanc-Féraud. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009. Keywords : Extragradient method, proximal method, Image decomposition, Meyer's model, convergence rate. @INPROCEEDINGS{PWEISS_Eusipco, author = {Weiss, P. and Blanc-Féraud, L.}, title = {A proximal method for inverse problems in image processing}, year = {2009}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Glasgow, Scotland}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, keyword = {Extragradient method, proximal method, Image decomposition, Meyer's model, convergence rate} } Abstract : In this paper, we present a new algorithm to solve some inverse problems coming from the field of image processing. The models we study consist in minimizing a regularizing, convex criterion under a convex and compact set. The main idea of our scheme consists in solving the underlying variational inequality with a proximal method rather than the initial convex problem. Using recent results of A. Nemirovski [13], we show that the scheme converges at least as O(1/k) (where k is the iteration counter). This is in some sense an optimal rate of convergence. Finally, we compare this approach to some others on a problem of image cartoon+texture decomposition.

57 - Complex wavelet regularization for solving inverse problems in remote sensing. M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009. Keywords : Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet. @INPROCEEDINGS{IGARSSCarlavan09, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, title = {Complex wavelet regularization for solving inverse problems in remote sensing}, year = {2009}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, address = {Cape Town, South Africa}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/IGARSS09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/IGARSS09.pdf}, keyword = {Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet} } Abstract : Many problems in remote sensing can be modeled as the minimization of the sum of a data term and a prior term. We propose to use a new complex wavelet based prior and an efficient scheme to solve these problems. We show some results on a problem of image reconstruction with noise, irregular sampling and blur. We also show a comparison between two widely used priors in image processing: sparsity and regularity priors.

58 - Conditional mixed-state model for structural change analysis from very high resolution optical images. B. Belmudez and V. Prinet and J.F. Yao and P. Bouthemy and X. Descombes. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009. Keywords : Change detection, mixed Markov models. @INPROCEEDINGS{bel09, author = {Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, title = {Conditional mixed-state model for structural change analysis from very high resolution optical images}, year = {2009}, month = {July}, booktitle = {IGARSS}, address = {Cape Town, South Africa}, url = {http://hal.archives-ouvertes.fr/inria-00398062/}, keyword = {Change detection, mixed Markov models} } Abstract : The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results.

59 - Point-spread function retrieval for fluorescence microscopy. P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, June 2009. Keywords : fluorescence microscopy, point spread function, EM algorithm, Deconvolution. Copyright : Copyright 2009 IEEE. Published in the 2009 International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), scheduled for June 28 - July 1, 2009 in Boston, Massachusetts, U.S.A. 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. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966. @INPROCEEDINGS{ppankajakshan09a, author = {Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, title = {Point-spread function retrieval for fluorescence microscopy}, year = {2009}, month = {June}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, organization = {IEEE}, address = {Boston, USA}, pdf = {http://hal.inria.fr/docs/00/39/55/34/PDF/pankajakshan.pdf}, keyword = {fluorescence microscopy, point spread function, EM algorithm, Deconvolution} } Abstract : In this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluorescent microspheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data.

60 - A new variational method to detect points in biological images. D. Graziani and L. Blanc-Féraud and G. Aubert. In ISBI'09, Org. IEEE International Symposium on Biomedical Imaging, Boston, USA, June 2009. Keywords : Biological images, points detection, Gamma-convergence. @INPROCEEDINGS{GRAZIANI_ISBI2009, author = {Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, title = {A new variational method to detect points in biological images}, year = {2009}, month = {June}, booktitle = {ISBI'09}, organization = {IEEE International Symposium on Biomedical Imaging}, address = {Boston, USA}, url = {http://dx.doi.org/10.1109/ISBI.2009.5193301}, keyword = {Biological images, points detection, Gamma-convergence} } Abstract : We propose a new variational method to isolate points in biological images. As points are fine structures they are difficult to detect by derivative operators computed in the noisy image. In this paper we propose to compute a vector field from the observed intensity so that its divergence explodes at points. As the image could contains spots but also noise and curves where the divergence also blows up, we propose to capture spots by introducing suitable energy whose minimizers are given by the points we want to detect. In order to provide numerical experiments we approximate this energy by means of a sequence of more treatable functionals by a Gamma-convergence approach. Results are shown on synthetic and biological images.

61 - Fast Realization of Digital Elevation Model . X. Descombes and A. Kraushonak and P. Lukashevish and B. Zalessky. In PRIP , pages 156-160, Minsk, Belarus, May 2009. @INPROCEEDINGS{KRA-09, author = {Descombes, X. and Kraushonak, A. and Lukashevish, P. and Zalessky, B.}, title = {Fast Realization of Digital Elevation Model }, year = {2009}, month = {May}, booktitle = {PRIP }, pages = {156-160}, address = {Minsk, Belarus}, url = {http://www.iapr.org/members/newsletter/Newsletter09-03/index_files/Page420.htm}, pdf = {https://hal.inria.fr/inria-00423678/document}, keyword = {} }

62 - Smoothing techniques for convex problems. Applications in image processing. P. Weiss and M. Carlavan and L. Blanc-Féraud and J. Zerubia. In Proc. SAMPTA (international conference on Sampling Theory and Applications), Marseille, France, May 2009. Keywords : nesterov scheme, convergence rate, Dual smoothing. @INPROCEEDINGS{PWEISS_SAMPTA09, author = {Weiss, P. and Carlavan, M. and Blanc-Féraud, L. and Zerubia, J.}, title = {Smoothing techniques for convex problems. Applications in image processing}, year = {2009}, month = {May}, booktitle = {Proc. SAMPTA (international conference on Sampling Theory and Applications)}, address = {Marseille, France}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Sampta09.pdf}, keyword = {nesterov scheme, convergence rate, Dual smoothing} } Abstract : In this paper, we present two algorithms to solve some inverse problems coming from the field of image processing. The problems we study are convex and can be expressed simply as sums of lp-norms of affine transforms of the image. We propose 2 different techniques. They are - to the best of our knowledge - new in the domain of image processing and one of them is new in the domain of mathematical programming. Both methods converge to the set of minimizers. Additionally, we show that they converge at least as O(1/N) (where N is the iteration counter) which is in some sense an ``optimal'' rate of convergence. Finally, we compare these approaches to some others on a toy problem of image super-resolution with impulse noise.

63 - Dictionary-based probability density function estimation for high-resolution SAR data. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009), Vol. 7246, pages 72460S, San Jose, USA, January 2009. Keywords : SAR image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM). Copyright : SPIE @INPROCEEDINGS{KrylovSPIE09, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Dictionary-based probability density function estimation for high-resolution SAR data}, year = {2009}, month = {January}, booktitle = {Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009)}, volume = {7246}, pages = {72460S}, address = {San Jose, USA}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=812524}, pdf = {http://hal.archives-ouvertes.fr/inria-00361384/en/}, keyword = {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 the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) 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). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution 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 accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.

64 - Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In International Conference on Pattern Recognition (ICPR), Tampa, Florida, December 2008. Keywords : Phase diagram, Higher-order actif contours, Shape, river extraction. @INPROCEEDINGS{ElGhoul08b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images}, year = {2008}, month = {December}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, Florida}, url = {https://hal.inria.fr/inria-00316619}, pdf = {http://hal.inria.fr/docs/00/31/66/19/PDF/icpr08aymenelghoul.pdf}, keyword = {Phase diagram, Higher-order actif contours, Shape, river extraction} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a ‘gas of circles’. In this paper, we present a stability analysis of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy. We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images.

65 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. C. Benedek and T. Szirányi. In Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008. Keywords : Aerial images, Change detection, mixed Markov models. @INPROCEEDINGS{benedekICPR08, author = {Benedek, C. and Szirányi, T.}, title = {A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences}, year = {2008}, month = {December}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, pdf = {http://hal.inria.fr/docs/00/35/91/16/PDF/benedekICPR08.pdf}, keyword = {Aerial images, Change detection, mixed Markov models} } Abstract : In the paper we propose a novel multi-layer Mixed Markov model for detecting relevant changes in registered aerial images taken with significant time differences. The introduced approach combines global intensity statistics with local correlation and contrast features. A global energy optimization process simultaneously ensures optimal local feature selection and smooth, observation-consistent classification. Validation is given on real aerial photos.

66 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas. A. Fournier and P. Weiss and L. Blanc-Féraud and G. Aubert. In International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008. Keywords : Change detection, Level Lines, remote sensing. 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. @INPROCEEDINGS{l_lines_icpr08, author = {Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, year = {2008}, month = {December}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, keyword = {Change detection, Level Lines, remote sensing} }

67 - Texture representation by geometric objects using a jump-diffusion process. F. Lafarge and G. Gimel'farb. In Proc. British Machine Vision Conference (BMVC), Leeds, U.K., November 2008. @INPROCEEDINGS{lafarge_bmvc08, author = {Lafarge, F. and Gimel'farb, G.}, title = {Texture representation by geometric objects using a jump-diffusion process}, year = {2008}, month = {November}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Leeds, U.K.}, url = {http://www.comp.leeds.ac.uk/bmvc2008/proceedings/papers/86.pdf}, keyword = {} }

68 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. European Conference on Computer Vision (ECCV), Marseille, France, October 2008. Keywords : Dense urban area, Phase Field, Road network, Variational methods, Very high resolution. Copyright : @INPROCEEDINGS{Peng08c, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = {An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images}, year = {2008}, month = {October}, booktitle = {Proc. European Conference on Computer Vision (ECCV)}, address = {Marseille, France}, pdf = {http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_38}, keyword = {Dense urban area, Phase Field, Road network, Variational methods, Very high resolution} } Abstract : This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular.

69 - A Geometric Primitive Extraction Process for Remote Sensing Problems.. F. Lafarge and G. Gimel'farb and X. Descombes. In Proc. Advanced Concepts for Intelligent Vision Systems, pages 518-529, Juan-les-Pins, France, October 2008. Copyright : @INPROCEEDINGS{LGF2008, author = {Lafarge, F. and Gimel'farb, G. and Descombes, X.}, title = {A Geometric Primitive Extraction Process for Remote Sensing Problems.}, year = {2008}, month = {October}, booktitle = {ACIVS}, pages = {518-529}, address = {Juan-les-Pins, France}, pdf = {http://www.springerlink.com/content/b228321527177226/}, keyword = {} }

70 - Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas. O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition and Image Analysis (PRIA), Nizhny Novgorod, Russia, September 2008. Keywords : Classification, Segmentation, Support Vector Machines, Burnt areas, Forest fires, Satellite images. Copyright : @INPROCEEDINGS{zammit_pria_08, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas}, year = {2008}, month = {September}, booktitle = {Proc. International Conference on Pattern Recognition and Image Analysis (PRIA)}, address = {Nizhny Novgorod, Russia}, pdf = {http://hal.inria.fr/inria-00316297/fr/}, keyword = {Classification, Segmentation, Support Vector Machines, Burnt areas, Forest fires, Satellite images} }

71 - Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping. O. Zammit and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008. Note : to appear. Keywords : Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, Clustering. Copyright : @INPROCEEDINGS{zammit_eusipco_08, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping}, year = {2008}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Lausanne, Switzerland}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7080254}, keyword = {Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, Clustering} }

72 - Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering. R. Gaetano and G. Scarpa and G. Poggi and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008. Keywords : Segmentation, Markov Random Fields, Mean Shift, Land Classification. @INPROCEEDINGS{Gaetano2008, author = {Gaetano, R. and Scarpa, G. and Poggi, G. and Zerubia, J.}, title = {Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering}, year = {2008}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Lausanne, Switzerland}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7080521}, keyword = {Segmentation, Markov Random Fields, Mean Shift, Land Classification} } Abstract : Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method.

73 - A new computationally efficient stochastic approach for building reconstruction from satellite data. F. Lafarge and M. Durupt and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In XXI ISPRS Congress, Part A, Beijing, China, July 2008. Note : Copyright ISPRS Keywords : 3D reconstruction, Building, satellite data, stochastic approach, jump process. @INPROCEEDINGS{lafarge_isprs08, author = {Lafarge, F. and Durupt, M. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {A new computationally efficient stochastic approach for building reconstruction from satellite data}, year = {2008}, month = {July}, booktitle = {XXI ISPRS Congress, Part A}, address = {Beijing, China}, note = {Copyright ISPRS}, url = {http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/40.pdf}, keyword = {3D reconstruction, Building, satellite data, stochastic approach, jump process} }

74 - Indexing of mid-resolution satellite images with structural attributes. A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In The International Society for Photogrammetry and Remote Sensing, Beijing, China, July 2008. Keywords : Landscape, Segmentation, Features, Extraction, Classification, Modelling. @INPROCEEDINGS{Bhattacharya08, author = {Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, title = {Indexing of mid-resolution satellite images with structural attributes}, year = {2008}, month = {July}, booktitle = {The International Society for Photogrammetry and Remote Sensing}, address = {Beijing, China}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Bhattacharya08isprs.pdf}, keyword = {Landscape, Segmentation, Features, Extraction, Classification, Modelling} } Abstract : Indexing and retrieval of satellite images relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Entities in an image may be strongly correlated with each other and can therefore be used to characterize geographical environments on the Earth’s surface. The properties of road networks vary considerably from one geographical environment to another. The networks pertaining in a satellite image can therefore be used to classify and retrieve such environments. In the work presented in this paper we have defined 7 such classes. These classes can be categorized as follows: 2 urban classes consisting of “Urban USA” and “Urban Europe”; 3 rural classes consisting of “Villages”, “Mountains” and “Fields”; an “Airports” class and a “Common” class (this can be considered as a rejection class). These classes were then classified with the aid of geometrical and topological features computed from the road networks occurring in them. In our work we have used two extraction methods simultaneously on an image to extract the road networks pertaining in it. A set of 16 network features were computed from one extraction method and were categorized into 6 groups as follows: 6 measures of ‘density’, 4 measures of ‘curviness’, 2 measures of ‘homogeneity’, 1 measure of ‘length’, 2 measures of ‘distribution’ and 1 measure of ‘entropy’. Due to certain limitations of these extraction methods there was a relative failure of network extraction in certain urban regions con- taining narrow and dense road structures. This loss of information was circumvented by segmenting the urban regions and computing a second set of geometrical and topological features from them. A set of 4 urban region features were computed and were categorized into 3 groups as follows: 2 measures of ‘density’, 1 measure of ‘labels’ and 1 measure of ‘compactness’. The 500 images (each of size 512x512 pixels) forming our database were selected from SPOT5 scenes with 5m resolution. From each image a set of geometrical and topological features were computed from the road networks and urban regions. These features were then used to classify the pre-defined geographical classes. Feature selection was done to avoid the burden of feature dimensionality and increase the classification performance. A set of 20 features was selected from 36 features by Fisher Linear Discriminant (FLD) analysis which gave the least classification error with an one-vs-rest linear Support Vector Machine (SVM). The impact of spatial resolution and size of images on the feature set have been explored in this work. We took a closer look at the effect of spatial resolution and size of images on the discriminative power of the feature set to classify the images belonging to the pre-defined geographical classes. Tests were performed with feature selection by FLD and one-vs-rest linear SVM classification on a database with images of 10m resolution. Another test was performed with feature selection by FLD and one-vs-rest linear SVM classification on a database with 5m resolution images (each of size 256x256 pixels). With the above mentioned approaches, we developed a novel method to classify large satellite images acquired by SPOT5 satellite (5m resolution) with patches of images each of size 512x512 pixels extracted from them. There has been a large amount of work dedicated to the classification of large satellite images at pixel level rather than considering image patches of different sizes. Classification of image patches of different sizes from a large satellite image is a novel idea in the sense that the patches considered contain significant coverage of a particular type of geographical environment. Road networks and urban region features were computed from these image patches extracted from the large image. A one-vs-rest Gaussian kernel SVM classification method was used to classify this large image. The classification results show that the image patches were labeled with the class having the maximum geographical coverage of the area associated in the large image. The large image was mapped into a “region matrix”, where each element of the matrix corresponds to a geographical class. This is a ‘hard’ classification and no inference can be drawn about the classification confidence. In certain cases, this produces some anomalies, as a single patch may contain two or more different geographical coverages. In order to have an estimate of these partial coverages, the output of the SVM was mapped into probabilities. These probability measures were then studied to have a closer look at the classification accuracies. The results confirm that our method is able to classify a large image into various geographical classes with a mean error of less than 10%. Future studies can use operators to detect not only man-made structures like roads and urban areas, but also natural entities like rivers, forests, etc. In this work we have restricted ourselves to a single resolution, but our methodology can be adapted to consider images of higher resolutions from QuickBird and the future Pleiade satellite. At a better resolution it may be possible to extract different structures like buildings, gardens, cross-roads, etc. This in turn will allow us to incorporate more classes to appropriately classify any geographical environment. At an image resolution of 1m, we may imagine to have sub-classes of an existing class, e.g., classes like urban Europe and urban USA can de divided into downtown, residential and industrial classes.

75 - Extraction of main and secondary roads in VHR images using a higher-order phase field model. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, July 2008. Keywords : Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods. Copyright : ISPRS @INPROCEEDINGS{Peng08a, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = {Extraction of main and secondary roads in VHR images using a higher-order phase field model}, year = {2008}, month = {July}, booktitle = {Proc. XXI ISPRS Congress, Part A}, pages = {215-22}, address = {Beijing, China}, pdf = {http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/33.pdf}, keyword = {Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods} } Abstract : This paper addresses the issue of extracting main and secondary road networks in dense urban areas from very high resolution (VHR, ~0.61m) satellite images. The difficulty with secondary roads lies in the low discriminative power of the grey-level distributions of road regions and the background, and the greater effect of occlusions and other noise on narrower roads. To tackle this problem, we use a previously developed higher-order active contour (HOAC) phase field model and augment it with an additional non-linear nonlocal term. The additional term allows separate control of road width and road curvature; thus more precise prior knowledge can be incorporated, and better road prolongation can be achieved for the same width. Promising results on QuickBird panchromatic images at reduced resolutions and comparisons with other models demonstrate the role and the efficiency of our new model.

76 - Building reconstruction from a single DEM. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, U.S., June 2008. @INPROCEEDINGS{lafarge_cvpr08, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Building reconstruction from a single DEM}, year = {2008}, month = {June}, booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, address = {Anchorage, Alaska, U.S.}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_lafarge_cvpr08.pdf}, keyword = {} }

77 - Blind deconvolution for diffraction-limited fluorescence microscopy. P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pages 740-743, Paris, France, May 2008. Keywords : Confocal microscopy, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization. Copyright : This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. @INPROCEEDINGS{ppankajakshan08a, author = {Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Blind deconvolution for diffraction-limited fluorescence microscopy}, year = {2008}, month = {May}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, pages = {740-743}, address = {Paris, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_ppankajakshan08a.pdf}, keyword = {Confocal microscopy, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization} } Abstract : Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system’s Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good deconvolution results in comparison to theoretical microscope PSF models.

78 - Automatic 3D modeling of urban scenes from satellite images. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. SPACEAPPLI, Toulouse, France, April 2008. @INPROCEEDINGS{lafarge_spaceappli08, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Automatic 3D modeling of urban scenes from satellite images}, year = {2008}, month = {April}, booktitle = {Proc. SPACEAPPLI}, address = {Toulouse, France}, url = {http://www.toulousespaceshow.eu/tss08/spaceappli08/index.htm}, keyword = {} }

79 - Compression artifacts reduction using variational methods: algorithms and experimental study. P. Weiss and L. Blanc-Féraud and T. Andre and M. Antonini. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008. Keywords : compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000. Copyright : @INPROCEEDINGS{ICASSP_WEISS, author = {Weiss, P. and Blanc-Féraud, L. and Andre, T. and Antonini, M.}, title = {Compression artifacts reduction using variational methods: algorithms and experimental study}, year = {2008}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Las Vegas, USA}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icassp2008.pdf}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_ICASSP_WEISS.pdf}, keyword = {compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000} }

80 - AUTOMATIC FLAMINGO DETECTION USING A MULTIPLE BIRTH AND DEATH PROCESS. S. Descamps and X. Descombes and A. Béchet and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008. Copyright : copyright IEEE 2008 @INPROCEEDINGS{descamps08, author = {Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, title = {AUTOMATIC FLAMINGO DETECTION USING A MULTIPLE BIRTH AND DEATH PROCESS}, year = {2008}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Las Vegas, USA}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_descamps08.pdf}, keyword = {} }

81 - SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING. E. Bughin and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008. Keywords : Irregular sampling, Variational methods, Fourier analysis, Satellite imaging. Copyright : @INPROCEEDINGS{Bughin08, author = {Bughin, E. and Blanc-Féraud, L. and Zerubia, J.}, title = {SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING}, year = {2008}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Las Vegas, USA}, url = {http://hal.inria.fr/docs/00/27/89/19/PDF/bughinICASSP08.pdf}, keyword = {Irregular sampling, Variational methods, Fourier analysis, Satellite imaging} }

82 - Mixing Geometric and Radiometric Features for Change Classification. A. Fournier and X. Descombes and J. Zerubia. In Proc. SPIE Symposium on Electronic Imaging, San Jose, USA, January 2008. Keywords : Change detection, directional Statistics, polygonal approximation, Classification. Copyright : Copyright 2008 SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE 20th Annual Symposium on Electronic Imaging and is made available as an electronic reprint (preprint) with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. @INPROCEEDINGS{fournier_spie08, author = {Fournier, A. and Descombes, X. and Zerubia, J.}, title = {Mixing Geometric and Radiometric Features for Change Classification}, year = {2008}, month = {January}, booktitle = {Proc. SPIE Symposium on Electronic Imaging}, address = {San Jose, USA}, url = {http://hal.inria.fr/inria-00269853/fr/}, keyword = {Change detection, directional Statistics, polygonal approximation, Classification} } Abstract : Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

83 - Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Amiens, France, January 2008. Keywords : Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection. @INPROCEEDINGS{ElGhoul08, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue}, year = {2008}, month = {January}, booktitle = {16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, address = {Amiens, France}, url = {https://hal.inria.fr/inria-00319575}, pdf = {http://hal.inria.fr/docs/00/31/95/75/PDF/rfia08aymenelghoul.pdf}, keyword = {Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection} } Résumé : Dans cet article, nous présentons l’analyse de stabilité du modèle des “contours actifs d’ordre supérieur” (CAOS), pour l’extraction des réseaux routiers présents dans des images de télédétection. Le modèle énergétique des CAOS à minimiser présente des comportements différents en fonction des valeurs des paramètres du modèle. Il s’est avéré que deux structures géométriques sont favorisées par ce modèle : des structures linéiques et circulaires. Nous nous intéressons ici à la détermination du diagramme de phase, qui définit les gammes des valeurs des paramètres du modèle des CAOS, permettant d’obtenir des structures linéiques. Abstract : In this paper, we present a stability analysis of a “higher-order active contour” (HOAC) model for road network extraction from remotely sensed images. The HOAC energy presents several different behaviours depending on the model parameter values. Two types of geometric structure are favoured, namely line networks and circles. In this work, we derive the phase diagram giving the parameter ranges of the HOAC model that allow stable linear structures.

84 - Forest Fire Detection based on Gaussian field analysis. F. Lafarge and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007. Note : Copyright EURASIP Keywords : Gaussian Field, DT-caracteristic, Forest fires. @INPROCEEDINGS{lafarge_eusipco07, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Forest Fire Detection based on Gaussian field analysis}, year = {2007}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Poznan, Poland}, note = {Copyright EURASIP}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_eusipco07.pdf}, keyword = {Gaussian Field, DT-caracteristic, Forest fires} }

85 - 3D city modeling based on Hidden Markov Model. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), San Antonio, U.S., September 2007. Note : Copyright IEEE Keywords : 3D reconstruction, Building, Hidden Markov Model. @INPROCEEDINGS{lafarge_icip07, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {3D city modeling based on Hidden Markov Model}, year = {2007}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {San Antonio, U.S.}, note = {Copyright IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4379207}, keyword = {3D reconstruction, Building, Hidden Markov Model} }

86 - A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction. P. Horvath and I. H. Jermyn. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007. Keywords : Phase Field, Tree Crown Extraction. @INPROCEEDINGS{Horvath07d, author = {Horvath, P. and Jermyn, I. H.}, title = {A `Gas of Circles' Phase Field Model and its Application to Tree Crown Extraction}, year = {2007}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Poznan, Poland}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07d.pdf}, keyword = {Phase Field, Tree Crown Extraction} } Abstract : The problem of extracting the region in the image domain corresponding to an a priori unknown number of circular objects occurs in several domains. We propose a new model of a `gas of circles', the ensemble of regions in the image domain composed of circles of a given radius. The model uses the phase field reformulation of higher-order active contours (HOACs). Phase fields possess several advantages over contour and level set approaches to region modelling, in particular for HOAC models. The reformulation allows us to benefit from these advantages without losing the strengths of the HOAC framework. Combined with a suitable likelihood energy, and applied to the tree crown extraction problem, the new model shows markedly improved performance, both in quality of results and in computation time, which is two orders of magnitude less than the HOAC level set implementation.

87 - A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia and B. Hu. In Proc. British Machine Vision Conference (BMVC), Warwick, UK, September 2007. Keywords : Road network, Very high resolution, Higher-order, Active contour, Shape, Prior. @INPROCEEDINGS{Peng07a, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J. and Hu, B.}, title = {A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images}, year = {2007}, month = {September}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Warwick, UK}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Peng07a.pdf}, keyword = {Road network, Very high resolution, Higher-order, Active contour, Shape, Prior} } Abstract : We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach.

88 - Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées. O. Zammit and X. Descombes and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Troyes, France, September 2007. Keywords : Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base. @INPROCEEDINGS{zammit_gretsi_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées}, year = {2007}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Troyes, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_zammit_gretsi_07.pdf}, keyword = {Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base} }

89 - Rectangular Road Marking Detection with Marked Point Processes. O. Tournaire and N. Paparoditis and F. Lafarge. In ISPRS Conference Photogrammetric Image Analysis (PIA), Vol. 36, pages 149--154, Org. IAPRS, Munich, Germany, September 2007. @INPROCEEDINGS{tournaire_pia07, author = {Tournaire, O. and Paparoditis, N. and Lafarge, F.}, title = {Rectangular Road Marking Detection with Marked Point Processes}, year = {2007}, month = {September}, booktitle = {ISPRS Conference Photogrammetric Image Analysis (PIA)}, volume = {36}, pages = {149--154}, organization = {IAPRS}, address = {Munich, Germany}, pdf = {http://www-sop.inria.fr/ariana/Publis/2007-tournaire-pia.pdf}, keyword = {} }

90 - A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vol. 6, pages 141--144, San Antonio, Texas, USA, September 2007. Keywords : Change detection, Aerial images, Camera motion, MRF. Copyright : Copyright IEEE @INPROCEEDINGS{benedek_ICIP07, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs}, year = {2007}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, volume = {6}, pages = {141--144}, address = {San Antonio, Texas, USA}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4379541&isnumber=4379494&punumber=4378863&k2dockey=4379541@ieeecnfs&query=%28benedek+%3Cin%3E+metadata%29+%3Cand%3E+%284379494+%3Cin%3E+isnumber%29&pos=0}, pdf = {http://web.eee.sztaki.hu/~bcsaba/Publications/Pdf/benedek_icip2007.pdf}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse matching instead of a precise image registration. The challenge of the proposed model is to eliminate the registration errors, noise and the parallax artifacts caused by the static objects having considerable height (buildings, trees, walls etc.) from the difference image. We describe the background membership of a given image point through two different features, and introduce a novel three-layerMarkov Random Field (MRF) model to ensure connected homogenous regions in the segmented image.

91 - Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes. P. Weiss and L. Blanc-Féraud and G. Aubert. In Proc. Symposium on Signal and Image Processing (GRETSI), Troyes, France, September 2007. Keywords : l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme. @INPROCEEDINGS{Pierre Weiss, author = {Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes}, year = {2007}, month = {September}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Troyes, France}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Gretsi_WeissBlancFeraudAubert_2010.PDF}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Pierre Weiss.pdf}, keyword = {l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme} }

92 - A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction. P. Horvath. In Proc. Advanced Concepts for Intelligent Vision Systems, Delft, Netherlands, August 2007. Keywords : Higher-order, Tree Crown Extraction, Colour. @INPROCEEDINGS{Horvath07c, author = {Horvath, P.}, title = {A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction}, year = {2007}, month = {August}, booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems}, address = {Delft, Netherlands}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07c.pdf}, keyword = {Higher-order, Tree Crown Extraction, Colour} } Abstract : Forestry management makes great use of statistics concerning the individual trees making up a forest, but the acquisition of this information is expensive. Image processing can potentially both reduce this cost and improve the statistics. The key problem is the delineation of tree crowns in aerial images. The automatic solution of this problem requires considerable prior information to be built into the image and region models. Our previous work has focused on including shape information in the region model; in this paper we examine the image model. The aerial images involved have three bands. We study the statistics of these bands, and construct both multispectral and single band image models. We combine these with a higher-order active contour model of a `gas of circles' in order to include prior shape information about the region occupied by the tree crowns in the image domain. We compare the results produced by these models on real aerial images and conclude that multiple bands improves the quality of the segmentation. The model has many other potential applications, e.g. to nano-technology, microbiology, physics, and medical imaging.

93 - A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images. P. Horvath and I. H. Jermyn. In Proc. International Conference on Computer Analysis of Images and Patterns (CAIP), Vienna, Austria, August 2007. Keywords : Phase Field, Tree Crown Extraction. @INPROCEEDINGS{Horvath07b, author = {Horvath, P. and Jermyn, I. H.}, title = {A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images}, year = {2007}, month = {August}, booktitle = {Proc. International Conference on Computer Analysis of Images and Patterns (CAIP)}, address = {Vienna, Austria}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07b.pdf}, keyword = {Phase Field, Tree Crown Extraction} } Abstract : We describe a model for tree crown extraction from aerial images, a problem of great practical importance for the forestry industry. The novelty lies in the prior model of the region occupied by tree crowns in the image, which is a phase field version of the higher-order active contour inflection point `gas of circles' model. The model combines the strengths of the inflection point model with those of the phase field framework: it removes the `phantom circles' produced by the original `gas of circles' model, while executing two orders of magnitude faster than the contour-based inflection point model. The model has many other areas of application e.g., to imagery in nanotechnology, biology, and physics.

94 - Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves. S. Joshi and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Ezhou, China, August 2007. Keywords : Shape, Reparameterization, Metric, Geodesic. Copyright : The original publication is available at www.springerlink.com. @INPROCEEDINGS{Joshi07b, author = {Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, title = {Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves}, year = {2007}, month = {August}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {Ezhou, China}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07b.pdf}, keyword = {Shape, Reparameterization, Metric, Geodesic} } Abstract : This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas - elastic shape metric and path-straightening methods - for finding geodesics in shape spaces of curves. The elastic metric allows for optimal matching of features between curves while path-straightening ensures that the algorithm results in geodesic paths. This paper extends this framework by removing two important shape preserving transformations: rotations and re-parameterizations, by forming quotient spaces and constructing geodesics on these quotient spaces. These ideas are demonstrated using experiments involving 2D and 3D curves.

95 - Parametric blind deconvolution for confocal laser scanning microscopy. P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, August 2007. Keywords : Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation. Copyright : ©2007 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. @INPROCEEDINGS{Pankajakshan07a, author = {Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, title = {Parametric blind deconvolution for confocal laser scanning microscopy}, year = {2007}, month = {August}, booktitle = {Proc. 29th International Conference of IEEE EMBS (EMBC-07)}, pages = {6531-6534}, pdf = {http://ieeexplore.ieee.org/iel5/4352184/4352185/04353856.pdf?tp=&isnumber=&arnumber=4353856}, keyword = {Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation} } Abstract : In this paper, we propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscopic (CLSM) images and 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 images suffers from two basic physical limitations. The diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier cause blur and photon counting noise respectively. These 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 observed data. A priori model of the specimen is further applied to stabilize the alternate minimization algorithm and to converge to the solutions.

96 - Assessment of different classification algorithms for burnt land discrimination. O. Zammit and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 3000-3003, Barcelone, Spain, July 2007. Keywords : Satellite images, Burnt areas, Support Vector Machines, Forest fires, Classification. Copyright : IEEE @INPROCEEDINGS{zammit_igarss_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Assessment of different classification algorithms for burnt land discrimination}, year = {2007}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, pages = {3000-3003}, address = {Barcelone, Spain}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4423476}, keyword = {Satellite images, Burnt areas, Support Vector Machines, Forest fires, Classification} }

97 - A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images. G. Scarpa and M. Haindl and J. Zerubia. In Scandinavian Conference on Image Analysis, Vol. 4522/2007, pages 303-312, series LNCS 4522, Ed. Springer Berlin / Heidelberg, Aalborg, Denmark, June 2007. @INPROCEEDINGS{scarpa_scia_07, author = {Scarpa, G. and Haindl, M. and Zerubia, J.}, title = {A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images}, year = {2007}, month = {June}, booktitle = {Scandinavian Conference on Image Analysis}, volume = {4522/2007}, pages = {303-312}, series = {LNCS 4522}, editor = {Springer Berlin / Heidelberg}, address = {Aalborg, Denmark}, url = {http://link.springer.com/chapter/10.1007%2F978-3-540-73040-8_31}, keyword = {} }

98 - A Novel Representation for Riemannian Analysis of Elastic Curves in R^n. S. Joshi and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007. Keywords : Shape, Metric, Geodesic, Prior. @INPROCEEDINGS{Joshi07a, author = {Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, title = {A Novel Representation for Riemannian Analysis of Elastic Curves in R^n}, year = {2007}, month = {June}, booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, address = {Minneapolis, USA}, url = {http://dx.doi.org/10.1109/CVPR.2007.383185}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07a.pdf}, keyword = {Shape, Metric, Geodesic, Prior} } Abstract : We propose an efficient representation for studying shapes of closed curves in R^n. This paper combines the strengths of two important ideas---elastic shape metric and path-straightening methods---and results in a very fast algorithm for finding geodesics in shape spaces. The elastic metric allows for optimal matching of features between the two curves while path-straightening ensures that the algorithm results in geodesic paths. For the novel representation proposed here, the elastic metric becomes the simple L^2 metric, in contrast to the past usage where more complex forms were used. We present the step-by-step algorithms for computing geodesics and demonstrate them with 2-D as well as 3-D examples.

99 - Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface. A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. International Workshop on Content-Based Multimedia Indexing, Bordeaux, France, June 2007. Keywords : Indexation, Road network, Semantic, Retrieval, Feature statistics. @INPROCEEDINGS{Bhattacharya07a, author = {Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, title = {Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface}, year = {2007}, month = {June}, booktitle = {Proc. International Workshop on Content-Based Multimedia Indexing}, address = {Bordeaux, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07a.pdf}, keyword = {Indexation, Road network, Semantic, Retrieval, Feature statistics} } Abstract : Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database.

100 - Riemannian Analysis of Probability Density Functions with Applications in Vision. S. Joshi and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007. Keywords : Probability density function, Metric, Geodesic, Reparameterization. @INPROCEEDINGS{Joshi07, author = {Joshi, S. and Srivastava, A. and Jermyn, I. H.}, title = {Riemannian Analysis of Probability Density Functions with Applications in Vision}, year = {2007}, month = {June}, booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, address = {Minneapolis, USA}, url = {http://dx.doi.org/10.1109/CVPR.2007.383188 }, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07.pdf}, keyword = {Probability density function, Metric, Geodesic, Reparameterization} } Abstract : Applications in computer vision involve statistically analyzing an important class of constrained, non- negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a em spherical version of the Fisher-Rao metric that provides closed form expressions for geodesics and distances, and allows an efficient computation of statistics. We compare this metric with some previously used metrics and present an application in planar shape classification.

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