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Conference papers

Learning graph neighborhood topological order for image and manifold morphological processing

Olivier Lezoray 1 Abderrahim Elmoataz 1 Vinh Thong Ta 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : The extension of lattice based operators to multivariate images is a challenging theme in mathematical morphology. We propose to consider manifold learning as the basis for the construction of a complete lattice by learning graph neighborhood topological order. With these propositions, we dispose of a general formulation of morphological operators on graphs that enables us to process by morphological means any kind of data modeled by a graph.
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Submitted on : Saturday, October 11, 2008 - 5:41:23 PM
Last modification on : Tuesday, October 19, 2021 - 11:34:55 PM
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Olivier Lezoray, Abderrahim Elmoataz, Vinh Thong Ta. Learning graph neighborhood topological order for image and manifold morphological processing. IEEE International Conference on Computer and Information Technology, 2008, Sydney, Australia. pp.350-355. ⟨hal-00329520⟩

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