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Publications of Pierre Weiss
Result of the query in the list of publications :
3 Articles |
1 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection.
P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, March 2011.
Keywords : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing.
@ARTICLE{SIIMS_2011,
|
| author |
= |
{Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
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{On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, |
| year |
= |
{2011}, |
| month |
= |
{March}, |
| journal |
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{SIAM Journal on Imaging Sciences}, |
| volume |
= |
{4}, |
| number |
= |
{1}, |
| pages |
= |
{448-471}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| keyword |
= |
{Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} |
| } |
Abstract :
We analyze the illumination invariance of the level lines of an image. We show that if the scene
surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition
for the level lines to be illumination invariant is that the three-dimensional scene be developable and
that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost”
invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This
allows us to devise a fast and simple algorithm that detects changes between pairs of remotely
sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of
the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results
illustrate the theory developed in this paper. The two real QuickBird images show that the proposed
change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes
for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on
demanding scenes.
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2 - Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons.
M. Carlavan and P. Weiss and L. Blanc-Féraud. Traitement du Signal, 27(2): pages 189-219, September 2010.
Keywords : Inverse Problems, Regularization, Total variation, Wavelets.
@ARTICLE{TSCarlavan2010,
|
| author |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L.}, |
| title |
= |
{Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{27}, |
| number |
= |
{2}, |
| pages |
= |
{189-219}, |
| url |
= |
{http://hal.inria.fr/inria-00503050/fr/}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/TS_Carlavan_Weiss_BlancFeraud_2010.pdf}, |
| keyword |
= |
{Inverse Problems, Regularization, Total variation, Wavelets} |
| } |
Résumé :
Dans cet article, nous nous intéressons à la régularisation de problèmes inverses reposant sur des critères l1 . Nous séparons ces critères en deux catégories : ceux qui favorisent la régularisation des signaux (à variation totale bornée par exemple) et ceux qui expriment le fait qu'un signal admet une représentation parcimonieuse dans un dictionnaire. Dans une première partie, nous donnons quelques éléments de comparaisons théoriques et pratiques sur les deux a priori, pour aider le lecteur à choisir l'un ou l'autre en fonction de son problème. Pour cette étude, nous utilisons les transformées communément utilisées telles que la variation totale, les ondelettes redondantes ou les curvelets. Dans une deuxième partie, nous proposons un état des lieux des algorithmes de premier ordre adaptés à la minimisation de ces critères. |
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3 - Efficient schemes for total variation minimization under constraints in image processing.
P. Weiss and L. Blanc-Féraud and G. Aubert. SIAM journal on Scientific Computing, 31(3): pages 2047-2080, 2009.
Keywords : Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time.
Copyright : Copyright Siam Society for Industrial and Applied
@ARTICLE{SIAM_JSC_PWEISS,
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{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
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{Efficient schemes for total variation minimization under constraints in image processing}, |
| year |
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{2009}, |
| journal |
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{SIAM journal on Scientific Computing}, |
| volume |
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{31}, |
| number |
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{2047-2080}, |
| url |
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{http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, |
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{http://www.math.univ-toulouse.fr/~weiss/Publis/SIAM_JSC09_PWEISS.pdf}, |
| keyword |
= |
{Total variation, l1 norm, nesterov scheme, Rudin Osher Fatemi, fast optimization, real time} |
| } |
|
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PhD Thesis and Habilitation |
1 - Algorithmes rapides d'optimisation convexe. Application à la reconstruction d'images et à la détection de changements.
P. Weiss. PhD Thesis, Universite de Nice Sophia Antipolis, November 2008.
Keywords : Convex optimization, nesterov scheme, Sparse representations, Total variation, Change detection, level lines.
Copyright :
@PHDTHESIS{These_Pweiss,
|
| author |
= |
{Weiss, P.}, |
| title |
= |
{Algorithmes rapides d'optimisation convexe. Application à la reconstruction d'images et à la détection de changements}, |
| year |
= |
{2008}, |
| month |
= |
{November}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/These_PWEISS_Compressee.pdf}, |
| keyword |
= |
{Convex optimization, nesterov scheme, Sparse representations, Total variation, Change detection, level lines} |
| } |
Résumé :
Cette thèse contient des contributions en analyse numérique et en vision par ordinateur. Dans une première partie, nous nous intéressons à la résolution rapide, par des méthodes de premier ordre, de problèmes d'optimisation convexe. Ces problèmes apparaissent naturellement dans de nombreuses tâches telles que la reconstruction d'images, l'échantillonnage compressif ou la décomposition d'images en texture et en géométrie. Ils ont la particularité d'être non différentiables ou très mal conditionnés. On montre qu'en utilisant des propriétés fines des fonctions à minimiser on peut obtenir des algorithmes de minimisation extrêmement efficaces. On analyse systématiquement leurs taux de convergence en utilisant des résultats récents dûs à Y. Nesterov. Les méthodes proposées correspondent - à notre connaissance - à l'état de l'art des méthodes de premier ordre. Dans une deuxième partie, nous nous intéressons au problème de la détection de changements entre deux images satellitaires prises au même endroit à des instants différents. Une des difficultés principales à surmonter pour résoudre ce problème est de s'affranchir des conditions d'illuminations différentes entre les deux prises de vue. Ceci nous mène à l'étude de l'invariance aux changements d'illuminations des lignes de niveau d'une image. On caractérise complètement les scènes qui fournissent des lignes de niveau invariantes. Celles-ci correspondent assez bien à des milieux urbains. On propose alors un algorithme simple de détection de changements qui fournit des résultats très satisfaisants sur des images synthétiques et des images Quickbird réelles. |
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7 Conference articles |
1 - Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009.
Keywords : Deconvolution, nesterov scheme, Wavelets, 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 |
= |
{September}, |
| 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, Wavelets, 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. |
|
2 - A proximal method for inverse problems in image processing.
P. Weiss and L. Blanc-Féraud. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009.
Keywords : Extragradient method, proximal method, Image decomposition, Meyer's model, convergence rate.
@INPROCEEDINGS{PWEISS_Eusipco,
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| title |
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{A proximal method for inverse problems in image processing}, |
| year |
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{2009}, |
| month |
= |
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{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
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| keyword |
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{Extragradient method, proximal method, Image decomposition, 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. |
|
3 - Complex wavelet regularization for solving inverse problems in remote sensing.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009.
Keywords : Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet.
|
4 - 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. |
|
5 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas.
A. Fournier and P. Weiss and L. Blanc-Féraud and G. Aubert. In International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008.
Keywords : Change detection, Level Lines, remote sensing.
Copyright : ©2008 IEEE. 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.
@INPROCEEDINGS{l_lines_icpr08,
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{Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
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{A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, |
| year |
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{2008}, |
| month |
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| booktitle |
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{International Conference on Pattern Recognition (ICPR)}, |
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{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, |
| keyword |
= |
{Change detection, Level Lines, remote sensing} |
| } |
|
6 - Compression artifacts reduction using variational methods: algorithms and experimental study.
P. Weiss and L. Blanc-Féraud and T. Andre and M. Antonini. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008.
Keywords : compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000.
Copyright :
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{Weiss, P. and Blanc-Féraud, L. and Andre, T. and Antonini, M.}, |
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{Compression artifacts reduction using variational methods: algorithms and experimental study}, |
| year |
= |
{2008}, |
| month |
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{March}, |
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{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Las Vegas, USA}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icassp2008.pdf}, |
| pdf |
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{ftp://ftp-sop.inria.fr/ariana/Articles/2008_ICASSP_WEISS.pdf}, |
| keyword |
= |
{compression artifact, fast l1 optimization, Total variation, contrast enhancement, nesterov scheme, jpeg2000} |
| } |
|
7 - Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes.
P. Weiss and L. Blanc-Féraud and G. Aubert. In Proc. Symposium on Signal and Image Processing (GRETSI), Troyes, France, September 2007.
Keywords : l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme.
@INPROCEEDINGS{Pierre Weiss,
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| keyword |
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{l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme} |
| } |
|
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4 Technical and Research Reports |
1 - On the illumination invariance of the level lines under directed light. Application to change detection.
P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. Research Report 6612, INRIA, 2008.
Keywords : Level Lines, illumination invariance, topographic map, Change detection, remote sensing, Urban areas.
Copyright :
@TECHREPORT{RR-6612,
|
| author |
= |
{Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
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{On the illumination invariance of the level lines under directed light. Application to change detection}, |
| year |
= |
{2008}, |
| institution |
= |
{INRIA}, |
| type |
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| number |
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{6612}, |
| url |
= |
{https://hal.archives-ouvertes.fr/inria-00310383}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/31/03/83/PDF/RR-6612.pdf}, |
| keyword |
= |
{Level Lines, illumination invariance, topographic map, Change detection, remote sensing, Urban areas} |
| } |
Abstract :
We analyze the illumination invariance of the level lines of an image. We show that if the scene surface has Lambertian reflectance and the light is directed, then a necessary condition for the level lines to be illumination invariant is that the 3D scene be developable and that its albedo satisfies some geometrical constraints. We then show that the level lines are ``almost'' invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. In a second part, this allows us to devise a very fast algorithm that detects changes between pairs of remotely sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of the algorithm both on synthetic OpenGL scenes and real Quickbird images. We compare the efficiency of the proposed algorithm with other classical approaches and show that it is superior both in practice and in theory. |
|
2 - Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. Research Report 6732, INRIA, 2008.
Keywords : l1 norm, nesterov scheme, total variation minimization, wavelet.
Copyright :
@TECHREPORT{RR-6732,
|
| author |
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{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
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{Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier}, |
| year |
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{2008}, |
| institution |
= |
{INRIA}, |
| type |
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{6732}, |
| url |
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| keyword |
= |
{l1 norm, nesterov scheme, total variation minimization, wavelet} |
| } |
|
3 - Efficient schemes for total variation minimization under constraints in image processing.
P. Weiss and L. Blanc-Féraud and G. Aubert. Research Report 6260, INRIA, July 2007.
Keywords : l1 norm, total variation minimization, duality lp norms, gradient and subgradient descent, nesterov scheme, texture + geometry decomposition.
@TECHREPORT{RR-6260,
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| year |
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| keyword |
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{l1 norm, total variation minimization, duality lp norms, gradient and subgradient descent, nesterov scheme, texture + geometry decomposition} |
| } |
Résumé :
Ce papier présente de nouveaux algorithmes pour minimiser la variation totale, et plus généralement des normes l^1, sous des contraintes convexes. Ces algorithmes proviennent d'une avancée récente en optimisation convexe proposée par Yurii Nesterov. Suivant la régularité de l'attache aux données, nous résolvons soit un problème primal, soit un problème dual. Premièrement, nous montrons que les schémas standard de premier ordre permettent d'obtenir des solutions de précision epsilon en O(frac1epsilon^2) itérations au pire des cas. Pour une contrainte convexe quelconque, nous proposons un schéma qui permet d'obtenir une solution de précision epsilon en O(frac1epsilon) itérations. Pour une contrainte fortement convexe, nous résolvons un problème dual avec un schéma qui demande O(frac1sqrtepsilon) itérations pour obtenir une solution de précision epsilon. Suivant la contrainte, nous gagnons donc un à deux ordres dans la rapidité de convergence par rapport à des approches standard. Finalement, nous faisons quelques expériences numériques qui confirment les résultats théoriques sur de nombreux problèmes. |
Abstract :
This paper presents new algorithms to minimize total variation and more generally l^1-norms under a general convex constraint. The algorithms are based on a recent advance in convex optimization proposed by Yurii Nesterov citeNESTEROV. Depending on the regularity of the data fidelity term, we solve either a primal problem, either a dual problem. First we show that standard first order schemes allow to get solutions of precision epsilon in O(frac1epsilon^2) iterations at worst. For a general convex constraint, we propose a scheme that allows to obtain a solution of precision epsilon in O(frac1epsilon) iterations. For a strongly convex constraint, we solve a dual problem with a scheme that requires O(frac1sqrtepsilon) iterations to get a solution of precision epsilon. Thus, depending on the regularity of the data term, we gain from one to two orders of magnitude in the convergence rates with respect to standard schemes. Finally we perform some numerical experiments which confirm the theoretical results on various problems. |
|
4 - Some applications of L infinite norms in image processing.
P. Weiss and G. Aubert and L. Blanc-Féraud. Research Report 6115, INRIA, September 2006.
Keywords : projected subgradient descent, convergence rate, Total variation, compression bounded noise, meyer G norm, fast l1 minimization.
@TECHREPORT{Some applications of L infinite constraints,
|
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{Weiss, P. and Aubert, G. and Blanc-Féraud, L.}, |
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| month |
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| keyword |
= |
{projected subgradient descent, convergence rate, Total variation, compression bounded noise, meyer G norm, fast l1 minimization} |
| } |
|
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