Learning graph neighborhood topological order for image and manifold morphological processing

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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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, Australia. pp.350-355. ⟨hal-00329520⟩

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