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Publications de type 'inproceedings'
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
51 - A phase field higher-order active contour model of directed networks.
A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans 2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV, Kyoto, Japan, septembre 2009.
Mots-clés : Geometric prior, Forme, Higher-order actif contours, Champ de Phase, Directed networks.
Copyright : ©2009 IEEE.
@INPROCEEDINGS{ElGhoul09b,
|
| author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{A phase field higher-order active contour model of directed networks}, |
| year |
= |
{2009}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV}, |
| address |
= |
{Kyoto, Japan}, |
| url |
= |
{https://hal.inria.fr/inria-00409910}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/40/99/10/PDF/nordia09aymenelghoul.pdf}, |
| keyword |
= |
{Geometric prior, Forme, Higher-order actif contours, Champ de Phase, Directed networks} |
| } |
Abstract :
The segmentation of directed networks is an important
problem in many domains, e.g. medical imaging (vascular
networks) and remote sensing (river networks). Directed
networks carry a unidirectional flow in each branch, which
leads to characteristic geometric properties. In this paper,
we present a nonlocal phase field model of directed networks.
In addition to a scalar field representing a region
by its smoothed characteristic function and interacting nonlocally
so as to favour network configurations, the model
contains a vector field representing the ‘flow’ through the
network branches. The vector field is strongly encouraged
to be zero outside, and of unit magnitude inside the region;
and to have zero divergence. This prolongs network
branches; controls width variation along a branch; and
produces asymmetric junctions for which total incoming
branch width approximately equals total outgoing branch
width. In conjunction with a new interaction function, it
also allows a broad range of stable branch widths. We
analyse the energy to constrain the parameters, and show
geometric experiments confirming the above behaviour. We
also show a segmentation result on a synthetic river image. |
|
52 - Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes.
M. Carlavan et P. Weiss et L. Blanc-Féraud et J. Zerubia. Dans Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, septembre 2009.
Mots-clés : Deconvolution, nesterov scheme, Ondelettes, l1 norm.
@INPROCEEDINGS{GRETSICarlavan09,
|
| author |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes}, |
| year |
= |
{2009}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, |
| keyword |
= |
{Deconvolution, nesterov scheme, Ondelettes, l1 norm} |
| } |
Résumé :
De nombreuses méthodes de restauration d'images consistent à minimiser une énergie convexe. Nous nous focalisons sur l'utilisation de ces méthodes et considérons la minimisation de deux critères contenant une norme l1 des coefficients en ondelettes. La plupart des travaux publiés récemment proposent un critère à minimiser dans le domaine des coefficients en ondelettes, utilisant ainsi un a priori de parcimonie. Nous proposons un algorithme rapide et des résultats de déconvolution par minimisation d'un critère dans le domaine image, avec un a priori de régularité exprimé dans le domaine image utilisant une décomposition redondante sur une trame. L'algorithme et le modèle proposés semblent originaux pour ce problème en traitement d'images et sont performants en terme de temps de calculs et de qualité de restauration. Nous montrons des comparaisons entre les deux types d' a priori. |
Abstract :
Many image restoration techniques are based on convex energy minimization. We focus on the use of these techniques and consider the minimization of two criteria holding a l1-norm of wavelet coefficients. Most of the recent research works are based on the minimization of a criterion in the wavelet coefficients domain, namely as a sparse prior. We propose a fast algorithm and deconvolution results obtained by minimizing a criterion in the image domain using a redundant decomposition on a frame. The algorithm and model proposed are unusual for this problem and very efficient in term of computing time and quality of restoration results. We show comparisons between the two different priors. |
|
53 - Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes.
C. Chaux et L. Blanc-Féraud. Dans Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, septembre 2009.
@INPROCEEDINGS{ChauxGRETSI09,
|
| author |
= |
{Chaux, C. and Blanc-Féraud, L.}, |
| title |
= |
{Estimation d'hyperparamètres pour la résolution de problèmes inverses à l'aide d'ondelettes}, |
| year |
= |
{2009}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://hdl.handle.net/2042/28911}, |
| keyword |
= |
{} |
| } |
Résumé :
Nous nous intéressons à l'estimation des paramètres de régularisation pour la restauration d'image floue et bruitée. Dans l'approche variationnelle, la restauration consiste à minimiser un critère convexe composé d'un terme de rappel aux données (quadratique) et d'un terme de régularisation (norme I1) opérant dans le domaine ondelettes. Nous proposons une méthode d'estimation des paramètres de régularisation (hyperparamètres, un par sous-bande) par maximum de vraisemblance, à partir de la seule image observée. La difficulté de l'estimation en données incomplètes est de pouvoir échantillonner des lois sur des champs de variables aléatoires dont les interactions entre voisins sont étendues, du fait de l'opérateur linéaire de flou. Nous proposons une méthode qui permet de calculer ces échantillons par MCMC (échantillonnage de Gibbs et Metropolis-Hastings). Pour l'estimation, nous utilisons une méthode de gradient. Les résultats de simulation obtenus montrent la faisabilité de la méthode et ses bonnes performances en terme d'estimation. |
|
54 - Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images.
A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, août 2009.
Mots-clés : Geometric prior, Forme, Contour actif d'ordre supérieur, Champ de Phase, remote sensing.
Copyright : EURASIP
@INPROCEEDINGS{ElGhoul09a,
|
| author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images}, |
| year |
= |
{2009}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Glasgow, Scotland}, |
| url |
= |
{http://hal.inria.fr/inria-00390446/fr/}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/39/04/46/PDF/eusipco09aymenelghoul.pdf}, |
| keyword |
= |
{Geometric prior, Forme, Contour actif d'ordre supérieur, Champ de Phase, remote sensing} |
| } |
Abstract :
The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC ‘inflection point’ model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images. |
|
55 - Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster).
F. Chatelain et X. Descombes et J. Zerubia. Dans Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Bonn, Germany, août 2009.
@INPROCEEDINGS{ChatelainEMMCVPR09,
|
| author |
= |
{Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster)}, |
| year |
= |
{2009}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{Bonn, Germany}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-642-03641-5_17}, |
| keyword |
= |
{} |
| } |
|
56 - A proximal method for inverse problems in image processing.
P. Weiss et L. Blanc-Féraud. Dans Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, août 2009.
Mots-clés : Extragradient method, proximal method, Decomposition d'images, Meyer's model, convergence rate.
@INPROCEEDINGS{PWEISS_Eusipco,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L.}, |
| title |
= |
{A proximal method for inverse problems in image processing}, |
| year |
= |
{2009}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Glasgow, Scotland}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
| keyword |
= |
{Extragradient method, proximal method, Decomposition d'images, Meyer's model, convergence rate} |
| } |
Abstract :
In this paper, we present a new algorithm to solve some inverse problems coming from the field of image processing. The models we study consist in minimizing a regularizing, convex criterion under a convex and compact set. The main idea of our scheme consists in solving the underlying variational inequality with a proximal method rather than the initial convex problem. Using recent results of A. Nemirovski [13], we show that the scheme converges at least as O(1/k) (where k is the iteration counter). This is in some sense an optimal rate of convergence. Finally, we compare this approach to some others on a problem of image cartoon+texture decomposition. |
|
57 - Complex wavelet regularization for solving inverse problems in remote sensing.
M. Carlavan et P. Weiss et L. Blanc-Féraud et J. Zerubia. Dans Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, juillet 2009.
Mots-clés : Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet.
|
58 - Conditional mixed-state model for structural change analysis from very high resolution optical images.
B. Belmudez et V. Prinet et J.F. Yao et P. Bouthemy et X. Descombes. Dans Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, juillet 2009.
Mots-clés : Change detection, mixed Markov models.
@INPROCEEDINGS{bel09,
|
| author |
= |
{Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, |
| title |
= |
{Conditional mixed-state model for structural change analysis from very high resolution optical images}, |
| year |
= |
{2009}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{IGARSS}, |
| address |
= |
{Cape Town, South Africa}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00398062/}, |
| keyword |
= |
{Change detection, mixed Markov models} |
| } |
Abstract :
The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results. |
|
59 - Point-spread function retrieval for fluorescence microscopy.
P. Pankajakshan et L. Blanc-Féraud et Z. Kam et J. Zerubia. Dans Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, juin 2009.
Mots-clés : fluorescence microscopy, point spread function, Algorithme EM, 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 |
= |
{juin}, |
| 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, Algorithme EM, 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. |
|
60 - A new variational method to detect points in biological images.
D. Graziani et L. Blanc-Féraud et G. Aubert. Dans ISBI'09, Org. IEEE International Symposium on Biomedical Imaging, Boston, USA, juin 2009.
Mots-clés : Images biologiques, 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 |
= |
{juin}, |
| booktitle |
= |
{ISBI'09}, |
| organization |
= |
{IEEE International Symposium on Biomedical Imaging}, |
| address |
= |
{Boston, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/ISBI.2009.5193301}, |
| keyword |
= |
{Images biologiques, 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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