The Publications

Result of the query in the list of publications :

• 101 Articles
• 31 PhD Thesis and Habilitations
• 245 Conference articles
• 90 Technical and Research Reports
• 14 Collection articles or Books chapters
• 5 Books

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245 Conference articles

49 - Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection. F. Chatelain and X. Descombes and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, November 2009. @INPROCEEDINGS{cha09a, author = {Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection}, year = {2009}, month = {November}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Dijon, France}, url = {http://hal.inria.fr/inria-00399258/fr/}, keyword = {} }

50 - Lidar Waveform Modeling using a Marked Point Process. C. Mallet and F. Lafarge and F. Bretar and U. Soergel and C. Heipke. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : 3D point cloud, Lidar, Marked point process, RJMCMC. @INPROCEEDINGS{mallet_icip09, author = {Mallet, C. and Lafarge, F. and Bretar, F. and Soergel, U. and Heipke, C.}, title = {Lidar Waveform Modeling using a Marked Point Process}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413380}, keyword = {3D point cloud, Lidar, Marked point process, RJMCMC} } Abstract : Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas.

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.

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