Mathematical Morphology for Vector Images Using Statistical Depth

  • Santiago Velasco-Forero
  • Jesus Angulo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6671)

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

Multivariate Morphology Depth function Hyperspectral Images 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Santiago Velasco-Forero
    • 1
  • Jesus Angulo
    • 1
  1. 1.CMM-Centre de Morphologie Mathématique, Mathématiques et SystèmesMINES ParisTechFontainebleau CEDEXFrance

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