Mathematical morphology for vector images using statistical depth

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.
Type de document :
Communication dans un congrès
Pierre Soille, Martino Pesaresi, and Georgios K. Ouzounis. 10th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing, ISMM 2011, Jul 2011, Verbania-Intra, Italy. Springer, 6671, pp.355-366, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-21569-8_31〉
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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 : vendredi 27 octobre 2017 - 17:36:02

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Santiago Velasco-Forero, Jesus Angulo. Mathematical morphology for vector images using statistical depth. Pierre Soille, Martino Pesaresi, and Georgios K. Ouzounis. 10th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing, ISMM 2011, Jul 2011, Verbania-Intra, Italy. Springer, 6671, pp.355-366, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-21569-8_31〉. 〈hal-00658951〉

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