Rank transformation and Manifold Learning for Multivariate Mathematical Morphology

Olivier Lezoray (1) , Christophe Charrier (1) , Abderrahim Elmoataz (1)

1 Equipe Image - Laboratoire GREYC - UMR6072

The extension of lattice based operators to multivariate images is still a challenging theme in mathematical morphology. In this paper, we propose to explicitly construct complete lattices and replace each element of a multivariate image by its rank, creating a rank image suitable for classical morphological processing. Manifold learning is considered as the basis for the construction of a complete lattice after reducing a multivariate image to its main data by Vector Quantization. A quantitative comparison between usual ordering criteria is performed and experimental results illustrate the abilities of our proposal.

Domaines: Traitement des images [eess.IV]

Olivier Lezoray, Christophe Charrier, Abderrahim Elmoataz. Rank transformation and Manifold Learning for Multivariate Mathematical Morphology. European Signal Processing Conference (EUSIPCO), Aug 2009, Glasgow, United Kingdom. pp.35-39. ⟨hal-00380591⟩