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Publications of Pierre Weiss
Result of the query in the list of publications :
7 Conference articles |
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,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Troyes, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Gretsi_WeissBlancFeraudAubert_2010.PDF}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Pierre Weiss.pdf}, |
| keyword |
= |
{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 |
= |
{On the illumination invariance of the level lines under directed light. Application to change detection}, |
| year |
= |
{2008}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{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. |
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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 |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier}, |
| year |
= |
{2008}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6732}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00340975/fr/}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/34/09/75/PDF/RR-6732.pdf}, |
| keyword |
= |
{l1 norm, nesterov scheme, total variation minimization, wavelet} |
| } |
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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. 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,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Efficient schemes for total variation minimization under constraints in image processing}, |
| year |
= |
{2007}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6260}, |
| url |
= |
{http://hal.inria.fr/inria-00166096/fr/}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/26/16/35/PDF/RR-6260.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/26/16/35/PS/RR-6260.ps}, |
| keyword |
= |
{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. |
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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,
|
| author |
= |
{Weiss, P. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Some applications of L infinite norms in image processing}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6115}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/RR-6115.pdf}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Some applications of L infinite constraints.pdf}, |
| keyword |
= |
{projected subgradient descent, convergence rate, Total variation, compression bounded noise, meyer G norm, fast l1 minimization} |
| } |
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