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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101 Articles

21 - A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images. P. Cariou and X. Descombes and E. Zhizhina. Problems of Information Transmission, 10(3): pages 247-256, 2010. Keywords : Marked point process, birth and death process, Road network extraction. @ARTICLE{cariou2010, author = {Cariou, P. and Descombes, X. and Zhizhina, E.}, title = {A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images}, year = {2010}, journal = {Problems of Information Transmission}, volume = {10}, number = {3}, pages = {247-256}, url = { http://www.jip.ru/2010/247-256-2010.pdf}, keyword = {Marked point process, birth and death process, Road network extraction} }

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

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

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

25 - Looking for shapes in two-dimensional, cluttered point clouds. A. Srivastava and I. H. Jermyn. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(9): pages 1616-1629, September 2009. Keywords : Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{SrivastavaJermyn09, author = {Srivastava, A. and Jermyn, I. H.}, title = {Looking for shapes in two-dimensional, cluttered point clouds}, year = {2009}, month = {September}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {31}, number = {9}, pages = {1616-1629}, url = {http://dx.doi.org/10.1109/TPAMI.2008.223}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/SrivastavaJermyn09.pdf}, keyword = {Shape, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao} } Abstract : We study the problem of identifying shape classes in point clouds. These clouds contain sampled contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learnt from training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: (i) shape variations within classes, (ii) variability in sampling continuous curves, (iii) pose and scale variability, (iv) observation noise, and (v) points introduced by clutter. The variability in sampling closed curves into finite points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their squareroot forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of posterior probabilities of different classes and, hence, Bayesian classification.

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

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

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

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

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

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