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Publications about wavelet
Result of the query in the list of publications :
2 Conference articles |
1 - 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.
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2 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images.
K. Brady and I. H. Jermyn and J. Zerubia. In Proc. British Machine Vision Conference (BMVC), Norwich, U. K., September 2003.
Keywords : probabilistic, Adaptive, wavelet, Texture.
@INPROCEEDINGS{Brady03a,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Norwich, U. K.}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03bmvc.pdf}, |
| keyword |
= |
{probabilistic, Adaptive, wavelet, Texture} |
| } |
Abstract :
Remote sensing imagery plays an important role in many elds. It has
become an invaluable tool for diverse applications ranging from cartography
to ecosystem management. In many of the images processed in these types
of applications, semantic entities in the scene are correlated with textures
in the image. In this paper, we propose a new method of analysing such
textures based on adaptive probabilistic models of wavelet packets. Our approach
adapts to the principal periodicities present in the textures, and can
capture long-range correlations while preserving the independence of the
wavelet packet coefcients. This technique has been applied to several remote
sensing images, the results of which are presented. |
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2 Technical and Research Reports |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Rapport de recherche 7758, INRIA, October 2011.
Keywords : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
@TECHREPORT{RR-7758,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas}, |
| year |
= |
{2011}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7758}, |
| url |
= |
{http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
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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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