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• W2227244893 abstract "The generalization of mathematical morphology to multivariate vector spaces is addressed in this paper. The proposed approach is fully unsupervised and consists in learning a complete lattice from an image as a nonlinear bijective mapping, interpreted in the form of a learned rank transformation together with an 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, the proposed approach can become a supervised ordering by the integration of pairwise constraints. The performance of the approach is illustrated with color image processing examples." @default.
• W2227244893 created "2016-06-24" @default.
• W2227244893 creator A5062753174 @default.
• W2227244893 date "2016-02-01" @default.
• W2227244893 modified "2023-10-06" @default.
• W2227244893 title "Complete lattice learning for multivariate mathematical morphology" @default.
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• W2227244893 doi "https://doi.org/10.1016/j.jvcir.2015.12.017" @default.
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