Multivalued component-tree filtering

Abstract : We introduce the new notion of multivalued component-tree, that extends the classical component-tree initially devoted to grey-level images, in the mathematical morphology framework. We prove that multivalued component-trees can model images whose values are hierarchically organized. We also show that they can be efficiently built from standard component-tree construction algorithms, and involved in antiextensive filtering procedures. The relevance and usefulness of multivalued component-trees is illustrated by an applicative example on hierarchically classified remote sensing images.
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https://hal.univ-reims.fr/hal-01695070
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Camille Kurtz, Benoît Naegel, Nicolas Passat. Multivalued component-tree filtering. International Conference on Pattern Recognition (ICPR), 2014, Stockholm, Sweden. pp.1008-1013, ⟨10.1109/ICPR.2014.183⟩. ⟨hal-01695070⟩

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