Kernel Metrics on Normal Cycles and Application to Curve Matching

Abstract : In this work we introduce a new dissimilarity measure for shape registration using the notion of normal cycles, a concept from geometric measure theory which allows to generalize curvature for non smooth subsets of the euclidean space. Our construction is based on the definition of kernel metrics on the space of normal cycles which take explicit expressions in a discrete setting. This approach is closely similar to previous works based on currents and varifolds [13,5]. We derive the computational setting for discrete curves in R 3 , using the Large Deformation Diffeomorphic Metric Mapping framework as model for deformations. We present synthetic experiments and compare with the currents and varifolds approaches.
Type de document :
Communication dans un congrès
MFCA 2015 : 5th MICCAI workshop on Mathematical Foundations of Computational Anatomy, Oct 2015, Munich, Germany
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01221101
Contributeur : Joan Alexis Glaunès <>
Soumis le : mercredi 14 décembre 2016 - 19:55:45
Dernière modification le : samedi 17 décembre 2016 - 01:05:18

Fichier

MFCA_normal_cycles_corr_2016.p...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01221101, version 2

Collections

Citation

Pierre Roussillon, Joan Alexis Glaunès. Kernel Metrics on Normal Cycles and Application to Curve Matching. MFCA 2015 : 5th MICCAI workshop on Mathematical Foundations of Computational Anatomy, Oct 2015, Munich, Germany. <hal-01221101v2>

Partager

Métriques

Consultations de
la notice

54

Téléchargements du document

16