HAL : inria-00630774, version 1

DOI : 10.1145/1998196.1998203

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27th Annual Symposium on Computational Geometry, Paris : France (2011)

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

(2011)

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.

1 :	Computer Science Department [Stanford]
	Stanford University
2 :	GEOMETRICA (INRIA Sophia Antipolis / INRIA Saclay - Ile de France)
	INRIA
3 :	Lawrence Berkeley National Laboratory (LBNL)
	Lawrence Berkeley National Lab

Domaine

:

Informatique/Géométrie algorithmique

inria-00630774, version 1

http://hal.inria.fr/inria-00630774

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Contributeur : Marc Glisse <>

Soumis le : Lundi 10 Octobre 2011, 22:58:43

Dernière modification le : Vendredi 6 Janvier 2012, 13:35:54