Supervised Morphology for Structure Tensor-Valued Images Based on Symmetric Divergence Kernels

Santiago Velasco-Forero 1 Jesus Angulo 1
1 CMM - Centre de Morphologie Mathématique
Abstract : Mathematical morphology is a nonlinear image processing methodology based on computing min/max operators in local neighbourhoods. In the case of tensor-valued images, the space of SPD matrices should be endowed with a partial ordering and a complete lattice structure. Structure tensor describes robustly the local orientation and anisotropy of image features. Formulation of mathematical morphology operators dealing with structure tensor images is relevant for texture filtering and segmentation. This paper introduces tensor-valued mathematical morphology based on a supervised partial ordering, where the ordering mapping is formulated by means of positive definite kernels and solved by machine learning algorithms. More precisely, we focus on symmetric divergences for SPD matrices and associated kernels.
Keywords : matrix-valued image tensor mathematical morphology supervised morphology structure tensor image processing
Type de document :
Communication dans un congrès
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger
https://hal-mines-paristech.archives-ouvertes.fr/hal-01536382
Contributeur : Jesus Angulo <>
Soumis le : dimanche 11 juin 2017 - 12:28:21
Dernière modification le : lundi 12 novembre 2018 - 10:56:29
Document(s) archivé(s) le : mercredi 13 décembre 2017 - 10:49:32

Fichier

VelascoAngulo_Tensor.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

ENSMP | INSTITUT-TELECOM | ENSMP_CMM | PARISTECH | PSL

Citation

Santiago Velasco-Forero, Jesus Angulo. Supervised Morphology for Structure Tensor-Valued Images Based on Symmetric Divergence Kernels. First International Conference on Geometric Science of Information (GSI'2013), Aug 2013, Paris, France. pp.543 - 550, ⟨10.1007/978-3-642-40020-9_60⟩. ⟨hal-01536382⟩

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

145

Téléchargements de fichiers

184

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD