Publications of 2009

Result of the query in the list of publications :

• 10 Articles
• 3 PhD Thesis and Habilitations
• 22 Conference articles
• 4 Technical and Research Reports
• 2 Collection articles or Books chapters

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3 PhD Thesis and Habilitations

1 - Blind Deconvolution for Confocal Laser Scanning Microscopy. P. Pankajakshan. PhD Thesis, Universite de Nice Sophia Antipolis, December 2009. Keywords : Confocal Laser Scanning Microscopy, Blind Deconvolution, point spread function, Maximum likelihood estimation , total variation regularization. @PHDTHESIS{PankajakshanThesis09, author = {Pankajakshan, P.}, title = {Blind Deconvolution for Confocal Laser Scanning Microscopy}, year = {2009}, month = {December}, school = {Universite de Nice Sophia Antipolis}, url = {http://tel.archives-ouvertes.fr/tel-00474264/fr/}, keyword = {Confocal Laser Scanning Microscopy, Blind Deconvolution, point spread function, Maximum likelihood estimation , total variation regularization} } Résumé : La microscopie confocale à balayage laser, est une technique puissante pour étudier les spécimens biologiques en trois dimensions (3D) par sectionnement optique. Elle permet d’avoir des images de spécimen vivants à une résolution de l’ordre de quelques centaines de nanomètres. Bien que très utilisée, il persiste des incertitudes dans le procédé d’observation. Comme la réponse du système à une impulsion, ou fonction de flou (PSF), est dépendante à la fois du spécimen et des conditions d’acquisition, elle devrait être estimée à partir des images observées du spécimen. Ce problème est mal posé et sous déterminé. Pour obtenir une solution, il faut injecter des connaisances, c’est à dire, a priori dans le problème. Pour cela, nous adoptons une approche bayésienne. L’état de l’art des algorithmes concernant la déconvolution et la déconvolution aveugle est exposé dans le cadre d’un travail bayésien. Dans la première partie, nous constatons que la diffraction due à l’objectif et au bruit intrinsèque à l’acquisition, sont les distorsions principales qui affectent les images d’un spécimen. Une approche de minimisation alternée (AM), restaure les fréquences manquantes au-delà de la limite de diffraction, en utilisant une régularisation par la variation totale sur l’objet, et une contrainte de forme sur la PSF. En outre, des méthodes sont proposées pour assurer la positivité des intensités estimées, conserver le flux de l’objet, et bien estimer le paramètre de la régularisation. Quand il s’agit d’imager des spécimens épais, la phase de la fonction pupille, due aux aberrations sphériques (SA) ne peut être ignorée. Dans la seconde partie, il est montré qu’elle dépend de la difference à l’index de réfraction entre l’objet et le milieu d’immersion de l’objectif, et de la profondeur sous la lamelle. Les paramètres d’imagerie et la distribution de l’intensité originelle de l’objet sont calculés en modifiant l’algorithme AM. Due à la nature de la lumière incohérente en microscopie à fluorescence, il est possible d’estimer la phase à partir des intensités observées en utilisant un modèle d’optique géométrique. Ceci a été mis en évidence sur des données simulées. Cette méthode pourrait être étendue pour restituer des spécimens affectés par les aberrations sphériques. Comme la PSF varie dans l’espace, un modèle de convolution par morceau est proposé, et la PSF est approchée. Ainsi, en plus de l’objet, il suffit d’estimer un seul paramétre libre. Abstract : Confocal laser scanning microscopy is a powerful technique for studying biological specimens in three dimensions (3D) by optical sectioning. It permits to visualize images of live specimens non-invasively with a resolution of few hundred nanometers. Although ubiquitous, there are uncertainties in the observation process. As the system’s impulse response, or point-spread function (PSF), is dependent on both the specimen and imaging conditions, it should be estimated from the observed images in addition to the specimen. This problem is ill-posed, under-determined. To obtain a solution, it is necessary to insert some knowledge in the form of a priori and adopt a Bayesian approach. The state of the art deconvolution and blind deconvolution algorithms are reviewed within a Bayesian framework. In the first part, we recognize that the diffraction-limited nature of the objective lens and the intrinsic noise are the primary distortions that affect specimen images. An alternative minimization (AM) approach restores the lost frequencies beyond the diffraction limit by using total variation regularization on the object, and a spatial constraint on the PSF. Additionally, some methods are proposed to ensure positivity of estimated intensities, to conserve the object’s flux, and to well handle the regularization parameter. When imaging thick specimens, the phase of the pupil function due to spherical aberration (SA) cannot be ignored. It is shown to be dependent on the refractive index mismatch between the object and the objective immersion medium, and the depth under the cover slip. The imaging parameters and the object’s original intensity distribution are recovered by modifying the AM algorithm. Due to the incoherent nature of the light in fluorescence microscopy, it is possible to retrieve the phase from the observed intensities by using a model derived from geometrical optics. This was verified on the simulated data. This method could also be extended to restore specimens affected by SA. As the PSF is space varying, a piecewise convolution model is proposed, and the PSF approximated so that, apart from the specimen, it is sufficient to estimated only one free parameter.

2 - Shape recognition for image scene analysis. M. S. Kulikova. PhD Thesis, Universite de Nice - Sophia-Antipolis, December 2009. Keywords : tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior. @PHDTHESIS{mkulikova_phd09, author = {Kulikova, M. S.}, title = {Shape recognition for image scene analysis}, year = {2009}, month = {December}, school = {Universite de Nice - Sophia-Antipolis}, url = {http://tel.archives-ouvertes.fr/docs/00/48/20/19/PDF/phd_mkulikova_2009.pdf}, keyword = {tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior} } Résumé : Cette thèse est composée de deux parties principales. La première partie est dédiée au problème de la classification d’espèces d’arbres en utilisant des descripteurs de forme, en combainison ou non, avec ceux de radiométrie ou de texture. Nous montrons notamment que l’information sur la forme améliore la performance d’un classifieur. Pour ce faire, dans un premier temps, une étude des formes de couronnes d’arbres extraites à partir d’images aériennes, en infrarouge couleur, est eectuée en utilisant une méthodologie d’analyse de formes des courbes continues fermées dans un espace de formes, en utilisant la notion de chemin géodésique sous deux métriques dans des espaces appropriés : une métrique non-élastique en utilisant la reprèsentation par la fonction d’angle de la courbe, ainsi qu’une métrique élastique induite par une représentation par la racinecarée appelée q-fonction. Une étape préliminaire nécessaire à la classification est l’extraction des couronnes d’arbre. Dans une seconde partie, nous abordons donc le problème de l’extraction d’objets de forme complexe arbitraire, à partir d’images de télédétection à très haute résolution. Nous construisons un modèle fondé sur les processus ponctuels marqués. Son originalité tient dans sa prise en compte d’objets de forme arbitraire par rapport aux objets de forme paramétrique, e.g. ellipses ou rectangles. Les formes sélectionnées sont obtenues par la minimisation locale d’une énergie de type contours actifs avec diérents a priori sur la forme incorporé. Les objets de la configuration finale (optimale) sont ensuite sélectionnés parmi les candidats par une dynamique de naissances et morts multiples, couplée à un schéma de recuit simulé. L’approche est validée sur des images de zones forestières à très haute résolution fournies par l’Université d’Agriculture de Suède. Abstract : This thesis includes two main parts. In the first part we address the problem of tree crown classification into species using shape features, without, or in combination with, those of radiometry and texture, to demonstrate that shape information improves classification performance. For this purpose, we first study the shapes of tree crowns extracted from very high resolution colour aerial infra-red images. For our study, we choose a methodology based on the shape analysis of closed continuous curves on shape spaces using geodesic paths under the bending metric with the angle-function curve representation, and the elastic metric with the square root q-function representation. A necessary preliminary step to classification is extraction of the tree crowns. In the second part, we address thus the problem of extraction of multiple objects with complex, arbitrary shape from remote sensing images of very high resolution. We develop a model based on marked point processes. Its originality lies in its use of arbitrarily-shaped objects as opposed to parametric shape objects, e.g. ellipses or rectangles. The shapes considered are obtained by local minimisation of an active contour energy with weak and then strong shape prior knowledge included. The objects in the final (optimal) configuration are then selected from amongst these candidates by a multiple birth-and-death dynamics embedded in an annealing scheme. The approach is validated on very high resolution images of forest provided by the Swedish University of Agriculture.

3 - Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.. A. Baudour. PhD Thesis, Universite de Nice Sophia Antipolis, May 2009. Keywords : imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale. @PHDTHESIS{baudour2009, author = {Baudour, A.}, title = {Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.}, year = {2009}, month = {May}, school = {Universite de Nice Sophia Antipolis}, url = {https://hal.inria.fr/tel-00507520/}, keyword = {imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale} } Résumé : Cette thèse aborde le problème de la modélisation et de la détection des laments dans des images 3D. Nous avons développé des méthodes variationnelles pour quatre applications spéciques :  l'extraction de routes où nous avons introduit la notion de courbure totale pour conserver les réseaux réguliers en tolérant les discontinuités de direction.  la détection et la complétion de laments fortement bruités et présentant des occultation. Nous avons utilisé la magnétostatique et la théorie de Ginzburg-Landau pour représenter les laments comme ensemble de singularités d'un champ vectoriel.  la détection de laments dans des images biologiques acquises en microscopie confocale. On modélise les laments en tenant compte des spécicité de cette dernière. Les laments sont alors obtenus par une méthode de maximum à posteriori.  la détection de cible dans des séquences d'images infrarouges. Dans cette application, on cherche des trajectoires optimisant la diérence de luminosit é moyenne entre la trajectoire et son voisinage en tenant compte des capteurs utilisés. Par ailleurs, nous avons démontré des résultats théoriques portant sur la courbure totale et la convergence de la méthode d'Alouges associée aux systèmes de Ginzburg-Landau. Ce travail réunit à la fois modélisation, résulats théoriques et recherche d'algorithmes numériques performants permettant de traiter de réelles applications.

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22 Conference articles

1 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, December 2009. Keywords : Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics. @INPROCEEDINGS{Kulikova09a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects}, year = {2009}, month = {December}, booktitle = {Proc. IEEE SITIS}, publisher = {IEEE Computer Society}, address = {Marrakech, Maroc}, pdf = {http://hal.inria.fr/docs/00/43/63/20/PDF/PID1054029.pdf}, keyword = {Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics} } Abstract : We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns.

2 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009. Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images. @INPROCEEDINGS{benedekWacv09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, year = {2009}, month = {December}, booktitle = {IEEE Workshop on Applications of Computer Vision (WACV)}, pages = {100-105}, address = {Snowbird, Utah, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, keyword = {Marked point process, Change detection, Aerial images, Building extraction, Satellite images} } Abstract : In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.

3 - Reconstruction 3D du bâti par la technique des ombres chinoises. P. Lukashevish and A. Kraushonak and X. Descombes and J.D. Durou and B. Zalessky and E. Zhizhina. In GRETSI Dijon, Dijon, France, November 2009. Keywords : 3D reconstruction. @INPROCEEDINGS{luka09, author = {Lukashevish, P. and Kraushonak, A. and Descombes, X. and Durou, J.D. and Zalessky, B. and Zhizhina, E.}, title = {Reconstruction 3D du bâti par la technique des ombres chinoises}, year = {2009}, month = {November}, booktitle = {GRETSI Dijon}, address = {Dijon, France}, url = {http://hal.inria.fr/inria-00399208/fr/}, keyword = {3D reconstruction} }

4 - Combining meshes and geometric primitives for accurate and semantic modeling. F. Lafarge and R. Keriven and M. Brédif. In Proc. British Machine Vision Conference (BMVC), London, U.K., November 2009. @INPROCEEDINGS{lafarge_bmvc09, author = {Lafarge, F. and Keriven, R. and Brédif, M.}, title = {Combining meshes and geometric primitives for accurate and semantic modeling}, year = {2009}, month = {November}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {London, U.K.}, url = {http://recherche.ign.fr/labos/matis/pdf/articles_conf/2009/bmvc_final_09.pdf}, keyword = {} }

5 - A markov random field model for extracting near-circular shapes. T. Blaskovics and Z. Kato and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Segmentation, Markov Random Fields, Shape prior. @INPROCEEDINGS{Blaskovics09, author = {Blaskovics, T. and Kato, Z. and Jermyn, I. H.}, title = {A markov random field model for extracting near-circular shapes}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5413472}, keyword = {Segmentation, Markov Random Fields, Shape prior} }

6 - Object extraction from high resolution SAR images using a birth and death dynamics. F. Arslan and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : High resolution SAR images, Object extraction, Marked point process, birth and death process. @INPROCEEDINGS{Fatih09, author = {Arslan, F. and Descombes, X. and Zerubia, J.}, title = {Object extraction from high resolution SAR images using a birth and death dynamics}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413907}, keyword = {High resolution SAR images, Object extraction, Marked point process, birth and death process} } Abstract : We present a new approach to extract predefined objects, such as trees and oil tanks for instance, from high resolution SAR images. We consider a stochastic approach based on an object process also called marked point process. The objects represent trees or oil tanks which are modeled by disks in the image. We first define a Gibbs density that takes into account both prior information and the data. The energy we define is composed of two terms, one is a prior, penalizing overlaps between objects, and the other is a data term, which measures the suitability of an object in the SAR image. The problem is then reduced to an energy minimization problem. We sample the process to extract the configuration of objects minimizing the energy by a fast birth-and-death dynamics, leading to the total number of objects (trees or oil tanks in our case). This approach is much faster than manual counts and does not need any preprocessing or supervision of a user.

7 - Multi-class SVM for forestry classification. N. Hajj Chehade and JG. Boureau and C. Vidal and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation. @INPROCEEDINGS{Nabil09, author = {Hajj Chehade, N. and Boureau, JG. and Vidal, C. and Zerubia, J.}, title = {Multi-class SVM for forestry classification}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413395}, keyword = {Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation} } Abstract : In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map.

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