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

9 - Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images. S. Prigent and D. Zugaj and X. Descombes and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. @INPROCEEDINGS{prigent11a, author = {Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, title = {Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, pdf = {http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, keyword = {} } Abstract : Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy.

10 - Two constrained formulations for deblurring Poisson noisy images. M. Carlavan and L. Blanc-Féraud. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : Poisson deconvolution, discrepancy principle, constrained convex optimization. @INPROCEEDINGS{ICIP2011_Carlavan, author = {Carlavan, M. and Blanc-Féraud, L.}, title = {Two constrained formulations for deblurring Poisson noisy images}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00591035/fr/}, keyword = {Poisson deconvolution, discrepancy principle, constrained convex optimization} } Abstract : Deblurring noisy Poisson images has recently been subject of an increasingly amount of works in many areas such as astronomy or biological imaging. Several methods have promoted explicit prior on the solution to regularize the ill-posed inverse problem and to improve the quality of the image. In each of these methods, a regularizing parameter is introduced to control the weight of the prior. Unfortunately, this regularizing parameter has to be manually set such that it gives the best qualitative results. To tackle this issue, we present in this paper two constrained formulations for the Poisson deconvolution problem, derived from recent advances in regularizing parameter estimation for Poisson noise. We first show how to improve the accuracy of these estimators and how to link these estimators to constrained formulations. We then propose an algorithm to solve the resulting optimization problems and detail how to perform the projections on the constraints. Results on real and synthetic data are presented.

11 - A fast multiple birth and cut algorithm using belief propagation. A. Gamal Eldin and X. Descombes and Charpiat G. and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation. @INPROCEEDINGS{MBC_ICIP11, author = {Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, title = {A fast multiple birth and cut algorithm using belief propagation}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592446/fr/}, keyword = {Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation} } Abstract : In this paper, we present a faster version of the newly proposed Multiple Birth and Cut (MBC) algorithm. MBC is an optimization method applied to the energy minimization of an object based model, defined by a marked point process. We show that, by proposing good candidates in the birth step of this algorithm, the speed of convergence is increased. The algorithm starts by generating a dense configuration in a special organization, the best candidates are selected using the belief propagation algorithm. Next, this candidate configuration is combined with the current configuration using binary graph cuts as presented in the original version of the MBC algorithm. We tested the performance of our algorithm on the particular problem of counting flamingos in a colony, and show that it is much faster with the modified birth step.

12 - Formulation contrainte pour la déconvolution de bruit de Poisson. M. Carlavan and L. Blanc-Féraud. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011. Keywords : 3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise. @INPROCEEDINGS{CarlavanGRETSI11, author = {Carlavan, M. and Blanc-Féraud, L.}, title = {Formulation contrainte pour la déconvolution de bruit de Poisson}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux, France}, url = {http://hal.inria.fr/inria-00602015/fr/}, keyword = {3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise} } Résumé : Nous considérons le problème de la restauration d’image floue et bruitée par du bruit de Poisson. De nombreux travaux ont proposé de traiter ce problème comme la minimisation d’une énergie convexe composée d’un terme d’attache aux données et d’un terme de régularisation choisi selon l’a priori dont on dispose sur l’image à restaurer. Un des problèmes récurrents dans ce type d’approche est le choix du paramètre de régularisation qui contrôle le compromis entre l’attache aux données et la régularisation. Une approche est de choisir ce paramètre de régularisation en procédant à plusieurs minimisations pour plusieurs valeurs du paramètre et en ne gardant que celle qui donne une image restaurée vérifiant un certain critère (qu’il soit qualitatif ou quantitatif). Cette technique est évidemment très couteuse lorsque les données traitées sont de grande dimension, comme c’est le cas en microscopie 3D par exemple. Nous proposons ici de formuler le problème de restauration d’image floue et bruitée par du bruit de Poisson comme un problème contraint sur l’antilog de la vraisemblance poissonienne et proposons une estimation de la borne à partir des travaux de Bertero et al. sur le principe de discrepancy pour l’estimation du paramètre de régularisation en présence de bruit de Poisson. Nous montrons des résultats sur des images synthétiques et réelles et comparons avec l'écriture non-contrainte utilisant une approximation gaussienne du bruit de Poisson pour l’estimation du paramètre de régularisation. Abstract : We focus here on the restoration of blurred and Poisson noisy images. Several methods solve this problem by minimizing a convex cost function composed of a data term and a regularizing term chosen from the prior that one have on the image. One of the recurrent problems of this approach is how to choose the regularizing paramater which controls the weight of the regularization term in front of the data term. One method consists in solving the minimization problem for several values of this parameter and by keeping the value which gives an image verifying a quality criterion (either qualitative or quantitative). This technique is obviously time consuming when one deal with high dimensional data such as in 3D microscopy imaging. We propose to formulate the blurred and Poisson noisy images restoration problem as a constrained problem on the antilog of the Poisson likelihood and propose an estimation of the bound from the works of Bertero et al. on the discrepancy principle for the estimation of the regularizing parameter for Poisson noise. We show results on synthetic and real data and we compare these results to the one obtained with the unconstrained formulation using the Gaussian approximation of the Poisson noise for the estimation of the regularizing parameter.

13 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities. K. Kayabol and A. Voisin and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, September 2011. Keywords : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm. @INPROCEEDINGS{inria-00592252, author = {Kayabol, K. and Voisin, A. and Zerubia, J.}, title = {SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, pages = {173-176}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592252/en/}, keyword = {High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

14 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution. A. Voisin and V. Krylov and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, September 2011. Keywords : SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models. @INPROCEEDINGS{VoisinGretsi2011, author = {Voisin, A. and Krylov, V. and Zerubia, J.}, title = {Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux}, url = {http://hal.inria.fr/inria-00623003/fr/}, keyword = {SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models} } Résumé : Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle à partir de champs de Markov hiérarchiques. Abstract : This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields

15 - Restauration d'image dégradée par un flou spatialement variant. S. Ben Hadj and L. Blanc-Féraud. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011. @INPROCEEDINGS{SaimaGretsi11, author = {Ben Hadj, S. and Blanc-Féraud, L.}, title = {Restauration d'image dégradée par un flou spatialement variant}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux, France}, url = {http://hal.inria.fr/inria-00625519/fr/}, keyword = {} } Résumé : La plupart des techniques de restauration d’images disponibles supposent que le flou est spatialement invariant. Néanmoins, différents phénomènes physiques liés aux propriétés de l’optique font que les dégradations peuvent être différentes selon les régions de l’image. Dans ce travail, nous considérons un modèle de PSF invariant par zone avec des transitions régulières entre les zones afin de prendre en compte la variation du flou dans l’image. Nous développons pour ce modèle, une méthode de déconvolution adaptée, par minimisation d’un critère avec une régularisation par variation totale. Nous nous appuyions sur une méthode rapide de minimisation par décomposition de domaine qui a été récemment développée par Fornasier et al., 2009. Nous obtenons ainsi un algorithme où la minimisation du critère est effectuée en parallèle sur les différentes zones de l’image, tout en prenant en compte les estimées dans les zones voisines des sous-images considérées, de sorte que la solution finale soit le minimum du critère où le flou est variant spatialement. Abstract : In most of the existing image restoration techniques, the blur is assumed to be spatially invariant. However, different physical phenomena related to the optic’s properties makes that degradations may change according to the image’s areas. In this work, we consider a piecewise-varying PSF model with smooth transitions between areas in order to take into account blur variation in the image. We develop for this model, a convenient deconvolution method by minimizing a criterion with a total variation regularization. We rely on a fast minimization method using a domain decomposition method that was recently developed by Fornasier et al. 2009. We thus obtain an algorithm where the criterion minimization is performed in a parallel way on different areas of the image, taking into account the estimates of neighboring areas of the considered sub-image, so that the final solution is space-varying deconvolved.

16 - Extraction et caractérisation de régions saines et pathologiques à partir de micro-tomographie RX du système vasculaire cérébral. X. Descombes and A. Gamal Eldin and F. Plouraboue and C. Fonta and S. Serduc and G. Le Duc and T. Weitkamp. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011. @INPROCEEDINGS{XavierGRETSI11, author = {Descombes, X. and Gamal Eldin, A. and Plouraboue, F. and Fonta, C. and Serduc, S. and Le Duc, G. and Weitkamp, T.}, title = {Extraction et caractérisation de régions saines et pathologiques à partir de micro-tomographie RX du système vasculaire cérébral}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux, France}, url = {http://hal.inria.fr/inria-00625525/fr/}, keyword = {} } Abstract : In this paper, we consider X-ray micro-tomography representing the brain vascular network. We define the local vascular territories as the regions obtained after a watershed algorithm applied on the distance map. The obtained graph is then regularized by a Markov random field approach. The optimization is performed using a graph cut algorithm. We show that the resulting segmentation exhibits three classes corresponding to normal tissue, tumour and an intermediate region.

17 - Reconstruction 3D du bâti à partir d'une seule image par naissances et morts multiples. J.D. Durou and X. Descombes and P. Lukashevish and A. Kraushonak. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, September 2011. @INPROCEEDINGS{DurouGretsi11, author = {Durou, J.D. and Descombes, X. and Lukashevish, P. and Kraushonak, A.}, title = {Reconstruction 3D du bâti à partir d'une seule image par naissances et morts multiples}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux, France}, url = {http://hal.inria.fr/inria-00625527/fr/}, keyword = {} } Résumé : Dans cet article, nous nous écartons de l’approche classique qui considère la reconstruction 3D comme un problème inverse et la résout en mettant en correspondance deux images d’une paire stéréoscopique. Au contraire, nous montrons qu’il est plus simple de résoudre le problème direct. Pour ce faire, nous proposons aléatoirement des configurations de bâtiments pour ne conserver que les plus pertinentes par un algorithme de type naissances et morts multiples. Nous montrons notamment que cette approche ne nécessite pas un temps de calcul prohibitif, grâce à la puissance de calcul d’OpenGL qui s’appuie sur la carte graphique. Les premiers résultats obtenus montrent la pertinence de l’approche adoptée. En particulier, elle permet de résoudre des ambiguïtés pour lesquelles l’inversion du problème serait quasiment impossible. Abstract : In this paper, contrary to the classical approach addressing the 3D reconstruction problem as an inverse problem and solving it by matching two images from a stereoscopic pair, we show that we can solve the direct problem in a simpler way. To do so, we randomly propose configurations of buildings while keeping only the most relevant ones, using a multiple births and deaths algorithm. Notably, we show that this approach does not imply a prohibitory computation time, thanks to the freeware OpenGL which exploits the graphic card. The first results show that the proposed approach is relevant. In particular, it allows solving ambiguities for which inverting the problem is almost impossible.

18 - Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar. Y. Verdié and F. Lafarge and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : 3D-Modeling, shape analysis, Mesh processing. @INPROCEEDINGS{VerdieICIP11, author = {Verdié, Y. and Lafarge, F. and Zerubia, J.}, title = {Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00605623/fr/}, keyword = {3D-Modeling, shape analysis, Mesh processing} } Abstract : We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness

19 - Morphological road segmentation in urban areas from high resolution satellite images. R. Gaetano and J. Zerubia and G. Scarpa and G. Poggi. In International Conference on Digital Signal Processing, Corfu, Greece, July 2011. Keywords : Segmentation, Classification, skeletonization , pattern recognition, shape analysis. @INPROCEEDINGS{GaetanoDSP, author = {Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, title = {Morphological road segmentation in urban areas from high resolution satellite images}, year = {2011}, month = {July}, booktitle = {International Conference on Digital Signal Processing}, address = {Corfu, Greece}, url = {http://hal.inria.fr/inria-00618222/fr/}, keyword = {Segmentation, Classification, skeletonization , pattern recognition, shape analysis} } Abstract : High resolution satellite images provided by the last generation sensors significantly increased the potential of almost all the image information mining (IIM) applications related to earth observation. This is especially true for the extraction of road information, task of primary interest for many remote sensing applications, which scope is more and more extended to complex urban scenarios thanks to the availability of highly detailed images. This context is particularly challenging due to such factors as the variability of road visual appearence and the occlusions from entities like trees, cars and shadows. On the other hand, the peculiar geometry and morphology of man-made structures, particularly relevant in urban areas, is enhanced in high resolution images, making this kind of information especially useful for road detection. In this work, we provide a new insight on the use of morphological image analysis for road extraction in complex urban scenarios, and propose a technique for road segmentation that only relies on this domain. The keypoint of the technique is the use of skeletons as powerful descriptors for road objects: the proposed method is based on an ad-hoc skeletonization procedure that enhances the linear structure of road segments, and extracts road objects by first detecting their skeletons and then associating each of them with a region of the image. Experimental results are presented on two different high resolution satellite images of urban areas.

20 - Regularizing parameter estimation for Poisson noisy image restoration. M. Carlavan and L. Blanc-Féraud. In International ICST Workshop on New Computational Methods for Inverse Problems, Paris, France, May 2011. Keywords : Parameter estimation, discrepancy principle, Poisson noise. @INPROCEEDINGS{NCMIP11, author = {Carlavan, M. and Blanc-Féraud, L.}, title = {Regularizing parameter estimation for Poisson noisy image restoration}, year = {2011}, month = {May}, booktitle = {International ICST Workshop on New Computational Methods for Inverse Problems}, address = {Paris, France}, url = {http://hal.inria.fr/inria-00590906/fr/}, keyword = {Parameter estimation, discrepancy principle, Poisson noise} } Abstract : Deblurring images corrupted by Poisson noise is a challeng- ing process which has devoted much research in many ap- plications such as astronomical or biological imaging. This problem, among others, is an ill-posed problem which can be regularized by adding knowledge on the solution. Several methods have therefore promoted explicit prior on the im- age, coming along with a regularizing parameter to moder- ate the weight of this prior. Unfortunately, in the domain of Poisson deconvolution, only a few number of methods have been proposed to select this regularizing parameter which is most of the time set manually such that it gives the best visual results. In this paper, we focus on the use of l1 -norm prior and present two methods to select the regularizing pa- rameter. We show some comparisons on synthetic data using classical image fidelity measures.

21 - A novel algorithm for occlusions and perspective effects using a 3d object process. A. Gamal Eldin and X. Descombes and J. Zerubia. In ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing), Prague, Czech Republic, May 2011. Keywords : 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 = {May}, 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 and L. Blanc-Féraud and G. Aubert. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 2011. Keywords : 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 = {May}, 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 and A. Dieterlen and G. Engler and Z. Kam and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, April 2011. Keywords : 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 = {April}, 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 and F. Plouraboue and El Boustani Habdelhkim and Fonta Caroline and |LeDuc Geraldine and Serduc Raphael and Weitkamp Timm. In Internation Symposium of Biomedical Imaging (ISBI), Chicago, USA, April 2011. Keywords : 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 = {April}, 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 and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS), Kuala Lumpur, Malaysia, December 2010. Keywords : Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process. @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 = {December}, 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, Marked point process} } 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 and I. H. Jermyn and J. Zerubia. In The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, November 2010. Keywords : Phase Field, 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 = {November}, booktitle = {The Tenth Asian Conference on Computer Vision (ACCV)}, address = {Queenstown, New Zealand}, pdf = {http://hal.archives-ouvertes.fr/inria-00522443/fr/}, keyword = {Phase Field, 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 and Z. Kam and A. Dieterlen and G. Engler and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Asilomar Conference on Signals, Systems and Computers, pages 1364-136, Pacific Grove, CA, USA , November 2010. Keywords : 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 = {November}, 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 and F. Chatelain and X. Descombes and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010. Keywords : Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (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 = {September}, 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, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM)} }

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