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Communication Dans Un Congrès Année : 2012

Nonlocal and Multivariate Mathematical Morphology

Résumé

The generalization of mathematical morphology to multivariate images is addressed in this paper. The proposed approach is fully unsupervised and consists in constructing a complete lattice from an image as a rank transformation together with a learned ordering of vectors. This unsupervised ordering of vectors relies on three steps: dictionary learning, manifold learning and out of sample extension. In addition to providing an efficient way to construct a vectorial ordering, nonlocal configurations based on color patches can be easily handled and provide much better results than with classical local morphological approaches.
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Dates et versions

hal-00813414 , version 1 (21-01-2014)

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Olivier Lezoray, Abderrahim Elmoataz. Nonlocal and Multivariate Mathematical Morphology. International Conference on Image Processing (IEEE), 2012, Orlando, United States. pp.129-132, ⟨10.1109/ICIP.2012.6466812⟩. ⟨hal-00813414⟩
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