A one-dimensional homologically persistent skeleton of an unstructured point cloud in any metric space

Real data are often given as a noisy unstructured point cloud, which is hard to visualize. The important problem is to represent topological structures hidden in a cloud by using skeletons with cycles. All past skeletonization methods require extra parameters such as a scale or a noise bound. We def...

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Bibliographic Details

Published in:	Computer graphics forum Vol. 34; no. 5; pp. 253 - 262
Main Author:	Kurlin, V.
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.08.2015
Subjects:	Analysis Approximation Categories and Subject Descriptors (according to ACM CCS) Clouds Graph theory Graphs Homology I.5.1 [Pattern Recognition]: Models-Structural Metric space Optimization Studies Three dimensional models Topological manifolds Topology Visualization
ISSN:	0167-7055, 1467-8659
Online Access:	Get full text
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