Mathematical morphology for vector images using statistical depth

Santiago Velasco-Forero; Jesus Angulo

Mathematical morphology for vector images using statistical depth

Santiago Velasco-Forero ¹ Jesus Angulo ¹

1 CMM - Centre de Morphologie Mathématique

Abstract : The open problem of the generalization of mathematical morphology to vector images is handled in this paper using the paradigm of depth functions. Statistical depth functions provide from the "deepest" point a "center-outward ordering" of a multidimensional data distribution and they can be therefore used to construct morphological operators. The fundamental assumption of this data-driven approach is the existence of "background/foreground" image representation. Examples in real color and hyperspectral images illustrate the results.

Keywords : Data-driven approach Depth function Hyper-spectral images Image representations Morphological operator Multidimensional data Multivariate Morphology Open problems Vector images

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https://hal-mines-paristech.archives-ouvertes.fr/hal-00658951
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 11 janvier 2012 - 16:10:19
Dernière modification le : lundi 12 novembre 2018 - 10:53:15

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