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Publications of 2009
Result of the query in the list of publications :
22 Conference articles |
18 - Point-spread function retrieval for fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, June 2009.
Keywords : fluorescence microscopy, point spread function, EM algorithm, Deconvolution.
Copyright : Copyright 2009 IEEE. Published in the 2009 International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), scheduled for June 28 - July 1, 2009 in Boston, Massachusetts, U.S.A. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.
@INPROCEEDINGS{ppankajakshan09a,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Point-spread function retrieval for fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| publisher |
= |
{IEEE}, |
| organization |
= |
{IEEE}, |
| address |
= |
{Boston, USA}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/39/55/34/PDF/pankajakshan.pdf}, |
| keyword |
= |
{fluorescence microscopy, point spread function, EM algorithm, Deconvolution} |
| } |
Abstract :
In this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluorescent microspheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data. |
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19 - A new variational method to detect points in biological images.
D. Graziani and L. Blanc-Féraud and G. Aubert. In ISBI'09, Org. IEEE International Symposium on Biomedical Imaging, Boston, USA, June 2009.
Keywords : Biological images, points detection, Gamma-convergence.
@INPROCEEDINGS{GRAZIANI_ISBI2009,
|
| author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{A new variational method to detect points in biological images}, |
| year |
= |
{2009}, |
| month |
= |
{June}, |
| booktitle |
= |
{ISBI'09}, |
| organization |
= |
{IEEE International Symposium on Biomedical Imaging}, |
| address |
= |
{Boston, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/ISBI.2009.5193301}, |
| keyword |
= |
{Biological images, points detection, Gamma-convergence} |
| } |
Abstract :
We propose a new variational method to isolate points in biological images. As points are fine structures they are difficult to detect by derivative operators computed in the noisy image. In this paper we propose to compute a vector field from the observed intensity so that its divergence explodes at points. As the image could contains spots but also noise and curves where the divergence also blows up, we propose to capture spots by introducing suitable energy whose minimizers are given by the points we want to detect. In order to provide numerical experiments we approximate this energy by means of a sequence of more treatable functionals by a Gamma-convergence approach. Results are shown on synthetic and biological images. |
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20 - Fast Realization of Digital Elevation Model .
X. Descombes and A. Kraushonak and P. Lukashevish and B. Zalessky. In PRIP , pages 156-160, Minsk, Belarus, May 2009.
@INPROCEEDINGS{KRA-09,
|
| author |
= |
{Descombes, X. and Kraushonak, A. and Lukashevish, P. and Zalessky, B.}, |
| title |
= |
{Fast Realization of Digital Elevation Model }, |
| year |
= |
{2009}, |
| month |
= |
{May}, |
| booktitle |
= |
{PRIP }, |
| pages |
= |
{156-160}, |
| address |
= |
{Minsk, Belarus}, |
| url |
= |
{http://www.iapr.org/members/newsletter/Newsletter09-03/index_files/Page420.htm}, |
| pdf |
= |
{https://hal.inria.fr/inria-00423678/document}, |
| keyword |
= |
{} |
| } |
|
21 - Smoothing techniques for convex problems. Applications in image processing.
P. Weiss and M. Carlavan and L. Blanc-Féraud and J. Zerubia. In Proc. SAMPTA (international conference on Sampling Theory and Applications), Marseille, France, May 2009.
Keywords : nesterov scheme, convergence rate, Dual smoothing.
@INPROCEEDINGS{PWEISS_SAMPTA09,
|
| author |
= |
{Weiss, P. and Carlavan, M. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Smoothing techniques for convex problems. Applications in image processing}, |
| year |
= |
{2009}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. SAMPTA (international conference on Sampling Theory and Applications)}, |
| address |
= |
{Marseille, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Sampta09.pdf}, |
| keyword |
= |
{nesterov scheme, convergence rate, Dual smoothing} |
| } |
Abstract :
In this paper, we present two algorithms to solve some inverse problems coming from the field of image processing. The problems we study are convex and can be expressed simply as sums of lp-norms of affine transforms of the image. We propose 2 different techniques. They are - to the best of our knowledge - new in the domain of image processing and one of them is new in the domain of mathematical programming. Both methods converge to the set of minimizers. Additionally, we show that they converge at least as O(1/N) (where N is the iteration counter) which is in some sense an ``optimal'' rate of convergence. Finally, we compare these approaches to some others on a toy problem of image super-resolution with impulse noise. |
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22 - Dictionary-based probability density function estimation for high-resolution SAR data.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009), Vol. 7246, pages 72460S, San Jose, USA, January 2009.
Keywords : SAR image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM).
Copyright : SPIE
@INPROCEEDINGS{KrylovSPIE09,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Dictionary-based probability density function estimation for high-resolution SAR data}, |
| year |
= |
{2009}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009)}, |
| volume |
= |
{7246}, |
| pages |
= |
{72460S}, |
| address |
= |
{San Jose, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=812524}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00361384/en/}, |
| keyword |
= |
{SAR image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM)} |
| } |
Abstract :
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes. |
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4 Technical and Research Reports |
1 - Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach.
C. Benedek and X. Descombes and J. Zerubia. Research Report 7143, INRIA, Sophia Antipolis, December 2009.
Keywords : Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics.
@TECHREPORT{benedekRR_09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7143}, |
| address |
= |
{Sophia Antipolis}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00426615}, |
| keyword |
= |
{Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. |
Abstract :
In this report 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. |
|
2 - Space non-invariant point-spread function and its estimation in fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. Research Report 7157, INRIA, December 2009.
Keywords : Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy.
@TECHREPORT{ppankajakshan09c,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Space non-invariant point-spread function and its estimation in fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7157}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00438719/en/}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy} |
| } |
Résumé :
Dans ce rapport de recherche, nous rappelons brièvement comment la nature limitée de diffraction de l'objectif d'un microscope optique, et le bruit
intrinsèque peuvent affecter la résolution d'une image observée. Un algorithme de déconvolution aveugle a été proposé en vue de restaurer les fréquences manquants au delà de la limite de diffraction. Cependant, sous d'autres conditions, l'approximation du systéme imageur l'imagerie sans aberration n'est plus valide et donc les aberrations de la phase du front d'onde émergeant d'un médium ne sont plus ignorées. Dans la deuxième partie de
ce rapport de recherche, nous montrons que la distribution d'intensité originelle et la localisation d'un objet peuvent être retrouvées uniquement en obtenant de la phase du front d'onde
réfracté, à partir d'images d'intensité observées. Nous démontrons cela par obtention de la fonction de ou a partir d'une microsphère imagée. Le bruit et l'infl uence de la taille de la
microsphère peuvent être diminués et parfois complètement supprimes des images observées en utilisant un estimateur maximum a posteriori. Néanmoins, a cause de l'incohérence du système d'acquisition, une récupération de phase a partir d'intensités observées n'est possible que si la restauration de la phase est contrainte. Nous avons utilisé l'optique géométrique
pour modéliser la phase du front d'onde réfracté, et nous avons teste l'algorithme sur des images simulées. |
Abstract :
In this research report, we recall briefly how the diffraction-limited nature of an optical microscope's objective, and the intrinsic noise can affect the observed images' resolution. A blind deconvolution algorithm can restore the lost frequencies beyond the diffraction limit. However, under other imaging conditions, the approximation of aberration-free imaging, is not applicable, and the phase aberrations of the emerging wavefront from a specimen immersion medium cannot be ignored any more. We show that an object's location and its original intensity distribution can be recovered by retrieving the refracted wavefront's phase from the observed intensity images. We demonstrate this by retrieving the point-spread function from an imaged microsphere. The noise and the influence of the microsphere size can be mitigated and sometimes completely removed from the observed images by using a maximum a posteriori estimate. However, due to the incoherent nature of the acquisition system, phase retrieval from the observed intensities will be possible only if the phase is constrained. We have used geometrical optics to model the phase of the refracted wavefront, and tested the algorithm on some simulated images. |
|
3 - High resolution SAR-image classification.
V. Krylov and J. Zerubia. Research Report 7108, INRIA, November 2009.
Keywords : SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula.
Copyright : INRIA/ARIANA, 2009
@TECHREPORT{RR-7108,
|
| author |
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{Krylov, V. and Zerubia, J.}, |
| title |
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{High resolution SAR-image classification}, |
| year |
= |
{2009}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7108}, |
| url |
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{http://hal.archives-ouvertes.fr/inria-00433036/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/44/81/40/PDF/RR-7108.pdf}, |
| keyword |
= |
{SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle approche pour la classification des images de type Radar à Synthèse d’Ouverture (RSO) haute résolution. Cette approche combine la méthode des champs Markoviens (MRF) pour la classification bayésienne et un modèle de mélange fini pour l’estimation des densités de probabilité. Ce modèle de mélange fini est realisé grace à une approche fondée sur une espérance-maximisation stochastique, à partir d'un dictionnaire, pour l’estimation des densités de probabilité d’amplitude. Cette approche semi-automatique est étendue au cas important des images RSO avec plusieurs polarisations, en utilisant des copulas pour modéliser les distributions jointes. Des résultats expérimentaux, sur plusieurs images RSO réelles (Dual-Pol TerraSAR-X et Single-Pol COSMO-SkyMed), pour la classification de zones humides, sont présentés pour montrer l’efficacité de l’algorithme proposé. |
Abstract :
In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
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4 - A formal Gamma-convergence approach for the detection of points in 2-D images.
D. Graziani and L. Blanc-Féraud and G. Aubert. Research Report 7038, INRIA, May 2009.
Note : to appear Siam Journal of Imaging Science
Keywords : points detection, curvature-depending functionals, divergence-measure fields, Gamma-convergence, biological 2-D images.
@TECHREPORT{GRAZIANI_GAMMA_POINTS,
|
| author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
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{A formal Gamma-convergence approach for the detection of points in 2-D images}, |
| year |
= |
{2009}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7038}, |
| note |
= |
{to appear Siam Journal of Imaging Science}, |
| url |
= |
{https://hal.inria.fr/inria-00418526}, |
| keyword |
= |
{points detection, curvature-depending functionals, divergence-measure fields, Gamma-convergence, biological 2-D images} |
| } |
|
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2 Collection articles or Books chapters |
1 - Deconvolution aveugle d'une image.
L. Blanc-Féraud and Mugnier L. and A. Jalobeanu. In Problemes inverses en imagerie et en vision, pages 107-132, series Tr. IC2, Ed. Ali Mohammad-Djafari, Publ. Ed. Hermes, 2009.
Copyright : Ed. Hermes
@INCOLLECTION{BLANC_FERAUD_DECAVEUGLE,
|
| author |
= |
{Blanc-Féraud, L. and L., Mugnier and Jalobeanu, A.}, |
| title |
= |
{Deconvolution aveugle d'une image}, |
| year |
= |
{2009}, |
| booktitle |
= |
{Problemes inverses en imagerie et en vision}, |
| pages |
= |
{107-132}, |
| series |
= |
{Tr. IC2}, |
| editor |
= |
{Ali Mohammad-Djafari}, |
| publisher |
= |
{Ed. Hermes}, |
| url |
= |
{http://www.lavoisier.fr/livre/electricite-electronique/problemes-inverses-en-imagerie-et-en-vision-en-2-volumes-inseparables/mohammad-djafari/descriptif-9782746219977}, |
| keyword |
= |
{} |
| } |
|
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