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Random projection depth for multivariate mathematical morphology

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.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00751347
Contributor : Santiago Velasco-Forero <>
Submitted on : Tuesday, November 13, 2012 - 11:29:14 AM
Last modification on : Thursday, September 24, 2020 - 4:38:04 PM
Long-term archiving on: : Thursday, February 14, 2013 - 3:41:09 AM

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Santiago Velasco-Forero, Jesus Angulo. Random projection depth for multivariate mathematical morphology. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2012, 6 (7), pp.753-763. ⟨10.1109/JSTSP.2012.2211336⟩. ⟨hal-00751347⟩

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