 |
 |
Communication dans un congrès
Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images
Abstract : In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of the proposed method.
Littérature citée [11 références]
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-00637936
Contributeur : Alamin Mansouri <>
Soumis le : jeudi 3 novembre 2011 - 12:12:26
Dernière modification le : lundi 30 mars 2020 - 08:53:30
Document(s) archivé(s) le : samedi 4 février 2012 - 02:27:15
Contributeur : Alamin Mansouri <>
Soumis le : jeudi 3 novembre 2011 - 12:12:26
Dernière modification le : lundi 30 mars 2020 - 08:53:30
Document(s) archivé(s) le : samedi 4 février 2012 - 02:27:15
Fichier
_final_version_ICIAR.pdf
Fichiers produits par l'(les) auteur(s)
