Metric graph reconstruction from noisy data

Mridul Aanjaneya 1 Frédéric Chazal 2 Daniel Chen 1 Marc Glisse 2 Leonidas J. Guibas 1 Dmitriy Morozov 3
1 Computer Science Department [Stanford]
2 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
3 LBNL - Lawrence Berkeley National Laboratory [Berkeley]
Abstract : Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs. Building on the notions of correspondence and Gromov-Hausdorff distance in metric geometry, we describe a model for such data sets as an approximation of an underlying metric graph. We present a novel algorithm that takes as an input such a data set, and outputs the underlying metric graph with guarantees. We also implement the algorithm, and evaluate its performance on a variety of real world data sets.
Type de document :
Communication dans un congrès
27th Annual Symposium on Computational Geometry, 2011, Paris, France. pp.37-46, 2011, 〈10.1145/1998196.1998203〉
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/inria-00630774
Contributeur : Marc Glisse <>
Soumis le : lundi 10 octobre 2011 - 22:58:43
Dernière modification le : vendredi 23 février 2018 - 14:20:05
Document(s) archivé(s) le : mardi 13 novembre 2012 - 15:36:16

Fichier

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

Identifiants

Collections

INRIA

Citation

Mridul Aanjaneya, Frédéric Chazal, Daniel Chen, Marc Glisse, Leonidas J. Guibas, et al.. Metric graph reconstruction from noisy data. 27th Annual Symposium on Computational Geometry, 2011, Paris, France. pp.37-46, 2011, 〈10.1145/1998196.1998203〉. 〈inria-00630774〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

510

Téléchargements de fichiers

215

Contact


archive-ouverte@inria.fr