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245 Articles de conférence

21 - A novel algorithm for occlusions and perspective effects using a 3d object process. A. Gamal Eldin et X. Descombes et J. Zerubia. Dans ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing), Prague, Czech Republic, mai 2011. Mots-clés : Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting. @INPROCEEDINGS{ICASSP_2011, author = {Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, title = {A novel algorithm for occlusions and perspective effects using a 3d object process}, year = {2011}, month = {mai}, booktitle = {ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing)}, address = {Prague, Czech Republic}, url = {http://hal.inria.fr/inria-00592449/fr/}, keyword = {Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting} } Abstract : In this paper, we introduce a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying in the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. These configurations are projected onto the image plane. The configurations are modified until convergence using the multiple birth and death optimization algorithm and by measuring the similarity between the projected image of the configuration and the real image. During optimization, the proposed configuration is modeled by a mixed graph which represents all dependencies between the objects, including interaction between neighbor objects and parent-child dependency for occluded objects. We tested our model on synthetic image, and real images.

22 - A new variational method for preserving point-like and curve-like singularities in 2d images. D. Graziani et L. Blanc-Féraud et G. Aubert. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, mai 2011. Mots-clés : Convex optimization, nesterov scheme, laplacian operator. @INPROCEEDINGS{ICASSP_Graziani11, author = {Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, title = {A new variational method for preserving point-like and curve-like singularities in 2d images}, year = {2011}, month = {mai}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Prague, Czech Republic}, url = {http://hal.inria.fr/inria-00592603/fr/}, keyword = {Convex optimization, nesterov scheme, laplacian operator} } Abstract : We propose a new variational method to restore point-like and curve-like singularities in 2-D images. As points and open curves are fine structures, they are difficult to restore by means of first order derivative operators computed in the noisy image. In this paper we propose to use the Laplacian operator of the observed intensity, since it becomes singular at points and curves. Then we propose to restore these singularities by introducing suitable regularization involving the l-1-norm of the Laplacian operator. Results are shown on synthetic an real data.

23 - Wavefront sensing for aberration modeling in fluorescence MACROscopy. P. Pankajakshan et A. Dieterlen et G. Engler et Z. Kam et L. Blanc-Féraud et J. Zerubia et J.C. Olivo-Marin. Dans Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, avril 2011. Mots-clés : fluorescence MACROscopy , phase retrieval, field aberration. @INPROCEEDINGS{PanjakshanISBI2011, author = {Pankajakshan, P. and Dieterlen, A. and Engler, G. and Kam, Z. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Wavefront sensing for aberration modeling in fluorescence MACROscopy}, year = {2011}, month = {avril}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, address = {Chicago, USA}, url = {http://hal.inria.fr/inria-00563988/en/}, keyword = {fluorescence MACROscopy , phase retrieval, field aberration} } Abstract : In this paper, we present an approach to calculate the wavefront in the back pupil plane of an objective in a fluorescent MACROscope. We use the three-dimensional image of a fluorescent bead because it contains potential pupil information in the ‘far’ out-of-focus planes for sensing the wavefront at the back focal plane of the objective. Wavefront sensing by phase retrieval technique is needed for several reasons. Firstly, the point-spread function of the imaging system can be calculated from the estimated pupil phase and used for image restoration. Secondly, the aberrations in the optics of the objective can be determined by studying this phase. Finally, the estimated wavefront can be used to correct the aberrated optical path with- out a wavefront sensor. In this paper, we estimate the wavefront of a MACROscope optical system by using Bayesian inferencing and derive the Gerchberg-Saxton algorithm as a special case.

24 - Brain tumor vascular network segmentation from micro-tomography. X. Descombes et F. Plouraboue et El Boustani Habdelhkim et Fonta Caroline et |LeDuc Geraldine et Serduc Raphael et Weitkamp Timm. Dans Internation Symposium of Biomedical Imaging (ISBI), Chicago, USA, avril 2011. Mots-clés : Segmentation, Markov random field, Tomography, Brain, vascular network. Copyright : IEEE @INPROCEEDINGS{isbi11, author = {Descombes, X. and Plouraboue, F. and Boustani Habdelhkim, El and Caroline, Fonta and Geraldine, |LeDuc and Raphael, Serduc and Timm, Weitkamp}, title = {Brain tumor vascular network segmentation from micro-tomography}, year = {2011}, month = {avril}, booktitle = {Internation Symposium of Biomedical Imaging (ISBI)}, address = {Chicago, USA}, url = {http://dx.doi.org/10.1109/ISBI.2011.5872596}, keyword = {Segmentation, Markov random field, Tomography, Brain, vascular network} } Abstract : Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus characterized by their respective distribution of watershed region size interpreted as local vascular territories.

25 - Multiple Birth and Cut Algorithm for Point Process Optimization. A. Gamal Eldin et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS), Kuala Lumpur, Malaysia, décembre 2010. Mots-clés : Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Processus ponctuels marques. @INPROCEEDINGS{MBC_MPP_SITIS10, author = {Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, title = {Multiple Birth and Cut Algorithm for Point Process Optimization}, year = {2010}, month = {décembre}, booktitle = {Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS)}, address = {Kuala Lumpur, Malaysia}, url = {http://hal.archives-ouvertes.fr/inria-00516305/fr/}, keyword = {Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Processus ponctuels marques} } Abstract : In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm.

26 - A theoretical and numerical study of a phase field higher-order active contour model of directed networks. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, novembre 2010. Mots-clés : Champ de Phase, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing. Copyright : Springer-Verlag GmbH Berlin Heidelberg @INPROCEEDINGS{Elghoul10b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A theoretical and numerical study of a phase field higher-order active contour model of directed networks}, year = {2010}, month = {novembre}, booktitle = {The Tenth Asian Conference on Computer Vision (ACCV)}, address = {Queenstown, New Zealand}, pdf = {http://hal.archives-ouvertes.fr/inria-00522443/fr/}, keyword = {Champ de Phase, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing} } Abstract : We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest.We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images.

27 - Point-spread function model for fluorescence MACROscopy imaging. P. Pankajakshan et Z. Kam et A. Dieterlen et G. Engler et L. Blanc-Féraud et J. Zerubia et J.C. Olivo-Marin. Dans Asilomar Conference on Signals, Systems and Computers, pages 1364-136, Pacific Grove, CA, USA , novembre 2010. Mots-clés : fluorescence MACROscopy , point-spread function, pupil function, vignetting . @INPROCEEDINGS{PanjakshanASILOMAR2010, author = {Pankajakshan, P. and Kam, Z. and Dieterlen, A. and Engler, G. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Point-spread function model for fluorescence MACROscopy imaging}, year = {2010}, month = {novembre}, booktitle = {Asilomar Conference on Signals, Systems and Computers}, pages = {1364-136}, address = {Pacific Grove, CA, USA }, url = {http://hal.inria.fr/inria-00555940/}, keyword = {fluorescence MACROscopy , point-spread function, pupil function, vignetting } } Abstract : In this paper, we model the point-spread function (PSF) of a fluorescence MACROscope with a field aberration. The MACROscope is an imaging arrangement that is designed to directly study small and large specimen preparations without physically sectioning them. However, due to the different optical components of the MACROscope, it cannot achieve the condition of lateral spatial invariance for all magnifications. For example, under low zoom settings, this field aberration becomes prominent, the PSF varies in the lateral field, and is proportional to the distance from the center of the field. On the other hand, for larger zooms, these aberrations become gradually absent. A computational approach to correct this aberration often relies on an accurate knowledge of the PSF. The PSF can be defined either theoretically using a scalar diffraction model or empirically by acquiring a three-dimensional image of a fluorescent bead that approximates a point source. The experimental PSF is difficult to obtain and can change with slight deviations from the physical conditions. In this paper, we model the PSF using the scalar diffraction approach, and the pupil function is modeled by chopping it. By comparing our modeled PSF with an experimentally obtained PSF, we validate our hypothesis that the spatial variance is caused by two limiting optical apertures brought together on different conjugate planes.

28 - Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images. S. Ben Hadj et F. Chatelain et X. Descombes et J. Zerubia. Dans Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, septembre 2010. Mots-clés : Shape extraction, Processus ponctuels marques, RJMCMC, Recuit Simule, EM Stochastique (SEM). @INPROCEEDINGS{sbenhadj10a, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images}, year = {2010}, month = {septembre}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, url = {http://hal.archives-ouvertes.fr/docs/00/52/63/45/PDF/ISPRS_SBH_FC_XD_JZ_Final2.pdf}, keyword = {Shape extraction, Processus ponctuels marques, RJMCMC, Recuit Simule, EM Stochastique (SEM)} }

29 - Tree crown detection in high resolution optical and LiDAR images of tropical forest. J. Zhou et C. Proisy et X. Descombes et I. Hedhli et N. Barbier et J. Zerubia et J.-P. Gastellu-Etchegorry et P. Couteron. Dans Proc. SPIE Symposium on Remote Sensing, Toulouse, France, septembre 2010. Mots-clés : Tropical forest, tree detection, Marked point process. @INPROCEEDINGS{Zhou10, author = {Zhou, J. and Proisy, C. and Descombes, X. and Hedhli, I. and Barbier, N. and Zerubia, J. and Gastellu-Etchegorry, J.-P. and Couteron, P.}, title = {Tree crown detection in high resolution optical and LiDAR images of tropical forest}, year = {2010}, month = {septembre}, booktitle = {Proc. SPIE Symposium on Remote Sensing}, address = {Toulouse, France}, url = {http://dx.doi.org/10.1117/12.865068}, keyword = {Tropical forest, tree detection, Marked point process} }

30 - Multi-spectral Image Analysis for Skin Pigmentation Classification. S. Prigent et X. Descombes et D. Zugaj et P. Martel et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, septembre 2010. Mots-clés : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction. @INPROCEEDINGS{sp02, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, title = {Multi-spectral Image Analysis for Skin Pigmentation Classification}, year = {2010}, month = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Hong-Kong, China}, pdf = {http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, keyword = {skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} } Abstract : In this paper, we compare two different approaches for semi-automatic detection of skin hyper-pigmentation on multi-spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.

31 - Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, septembre 2010. Mots-clés : Champ de Phase, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing. Copyright : ISPRS @INPROCEEDINGS{Elghoul10a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model}, year = {2010}, month = {septembre}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, pdf = {https://hal.inria.fr/inria-00491017}, keyword = {Champ de Phase, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing} } Abstract : We propose a new algorithm for network segmentation from VHR remote sensing images. The algorithm performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images.

32 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. A. Voisin et G. Moser et V. Krylov et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, septembre 2010. Mots-clés : Images SAR, Supervised classification, Zones urbaines, Textural features, Copulas, Markov Random Fields. Copyright : SPIE @INPROCEEDINGS{7830-23, author = {Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, title = {Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, year = {2010}, month = {septembre}, booktitle = {Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, volume = {7830}, address = {Toulouse, France}, url = {http://hal.archives-ouvertes.fr/inria-00516333/en}, pdf = {http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, keyword = {Images SAR, Supervised classification, Zones urbaines, Textural features, Copulas, Markov Random Fields} } Abstract : This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers.

33 - Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles. C. Benedek et X. Descombes et J. Zerubia. Dans Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, août 2010. Mots-clés : Processus ponctuels marques, multiple birth-and-death dynamics, Building extraction. @INPROCEEDINGS{benedekICPR10, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, year = {2010}, month = {août}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Istanbul, Turkey}, pdf = {http://hal.archives-ouvertes.fr/inria-00481019/en/}, keyword = {Processus ponctuels marques, multiple birth-and-death dynamics, Building extraction} } Abstract : In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques.

34 - Graph-based Analysis of Textured Images for Hierarchical Segmentation. R. Gaetano et G. Scarpa et T. Sziranyi. Dans Proc. British Machine Vision Conference (BMVC), Aberystwyth, UK, août 2010. @INPROCEEDINGS{Gaetano2010, author = {Gaetano, R. and Scarpa, G. and Sziranyi, T.}, title = {Graph-based Analysis of Textured Images for Hierarchical Segmentation}, year = {2010}, month = {août}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Aberystwyth, UK}, url = {http://hal.archives-ouvertes.fr/inria-00506596}, keyword = {} } Abstract : The Texture Fragmentation and Reconstruction (TFR) algorithm has beenrecently introduced to address the problem of image segmentationby textural properties, based on a suitable image description toolknown as the Hierarchical Multiple Markov Chain (H-MMC) model. TFRprovides a hierarchical set of nested segmentation maps by firstidentifying the elementary image patterns, and then merging themsequentially to identify complete textures at different scales ofobservation.In this work, we propose a major modification to the TFR by resortingto a graph based description of the image content and a graph clusteringtechnique for the enhancement and extraction of image patterns. Aprocedure based on mathematical morphology will be introduced thatallows for the construction of a color-wise image representationby means of multiple graph structures, along with a simple clusteringtechnique aimed at cutting the graphs and correspondingly segmentgroups of connected components with a similar spatial context.The performance assessment, realized both on synthetic compositionsof real-world textures and images from the remote sensing domain,confirm the effectiveness and potential of the proposed method.

35 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, juillet 2010. Mots-clés : multichannel SAR, Classification, probability density function estimation, Markov random field, copula. Copyright : AIP @INPROCEEDINGS{krylovMaxEnt10, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields}, year = {2010}, month = {juillet}, booktitle = {Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)}, volume = {1305}, pages = {319-326}, address = {Chamonix, France}, url = {http://hal.archives-ouvertes.fr/inria-00495557/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/49/55/57/PDF/krylov_MaxEnt2010.pdf}, keyword = {multichannel SAR, Classification, probability density function estimation, Markov random field, copula} } Abstract : The last decades have witnessed an intensive development and a significant increase of interest to remote sensing, and, in particular, to synthetic aperture radar (SAR) imagery. In this paper we develop a supervised classification approach for medium and high resolution multichannel SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and the Markov random field approach to Bayesian image classification. The finite mixture modeling is done via a recently proposed SAR-specific dictionary-based stochastic expectation maximization approach to class-conditional amplitude probability density function estimation, which is applied separately to all the SAR channels. For modeling the class-conditional joint distributions of multichannel data the statistical concept of copulas is employed, and a dictionary-based copula selection method is proposed. Finally, the Markov random field approach enables to take into account the contextual information and to gain robustness against the inherent noise-like phenomenon of SAR known as speckle. The designed method is an extension and a generalization to multichannel SAR of a recently developed single-channel and Dual-pol SAR image classification technique. The accuracy of the developed multichannel SAR classification approach is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.

36 - Hybrid Multi-view Reconstruction by Jump-Diffusion. F. Lafarge et R. Keriven et M. Brédif et H. Vu. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), San Franscico, U.S., juin 2010. @INPROCEEDINGS{lafarge_cvpr10, author = {Lafarge, F. and Keriven, R. and Brédif, M. and Vu, H.}, title = {Hybrid Multi-view Reconstruction by Jump-Diffusion}, year = {2010}, month = {juin}, booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, address = {San Franscico, U.S.}, pdf = {http://certis.enpc.fr/publications/papers/CVPR10a.pdf}, keyword = {} }

37 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification. S. Prigent et X. Descombes et D. Zugaj et J. Zerubia. Dans Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, juin 2010. Mots-clés : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation. @INPROCEEDINGS{sp01, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, title = {Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, year = {2010}, month = {juin}, booktitle = {Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, address = {Reykjavik, Iceland}, pdf = {http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, keyword = {Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} } Abstract : Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.

38 - Hidden fuzzy Markov chain model with K discrete classes. A. Gamal Eldin et Fabien Salzenstein et Christophe Collet. Dans Information Sciences Signal Processing and their Applications (ISSPA), mai 2010. Mots-clés : hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density. @INPROCEEDINGS{fuzzy_segmentation10, author = {Gamal Eldin, A. and Salzenstein, Fabien and Collet, Christophe}, title = {Hidden fuzzy Markov chain model with K discrete classes}, year = {2010}, month = {mai}, booktitle = {Information Sciences Signal Processing and their Applications (ISSPA)}, url = {http://hal.inria.fr/hal-00616372}, keyword = {hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density} } Abstract : This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data.

39 - Detection and tracking of threats in aerial infrared images by a minimal path approach. G. Aubert et A. Baudour et L. Blanc-Féraud et L. Guillot et Y. Le Guilloux. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, Texas, USA, mars 2010. @INPROCEEDINGS{ICASSP10, author = {Aubert, G. and Baudour, A. and Blanc-Féraud, L. and Guillot, L. and Le Guilloux, Y.}, title = {Detection and tracking of threats in aerial infrared images by a minimal path approach}, year = {2010}, month = {mars}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Dallas, Texas, USA}, pdf = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5495518}, keyword = {} }

40 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours. M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. Dans Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, janvier 2010. Mots-clés : Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, birth-and-death dynamics. Copyright : Copyright 2010 by SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE Electronic Imaging 2010 Conference in San Jose, USA, and is made available as an electronic reprint 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{Kulikova10a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours}, year = {2010}, month = {janvier}, booktitle = {Proc. IS&T/SPIE Electronic Imaging}, address = {San Jose, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/46/54/72/PDF/Kulikova_SPIE2010.pdf}, keyword = {Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, birth-and-death dynamics} } Abstract : We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. From an alternative point of view, the approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined on the configuration space of an arbitrary number of objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data.

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