Abstract
| Original language | English |
|---|---|
| Pages | 38-47 |
| Number of pages | 10 |
| Publication status | Published - 18 Jun 2012 |
| Externally published | Yes |
| Event | conference - Duration: 18 Jun 2012 → … |
Conference
| Conference | conference |
|---|---|
| Period | 18/6/12 → … |
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GRAPP 2012 IVAPP 2012 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications. / Ayala, D.; Vergés, E.; Cruz, I.
2012. 38-47 Paper presented at conference, .Research output: Contribution to conference › Paper
TY - CONF
T1 - GRAPP 2012 IVAPP 2012 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications
AU - Ayala, D.
AU - Vergés, E.
AU - Cruz, I.
PY - 2012/6/18
Y1 - 2012/6/18
N2 - Topological characteristics are fundamental in many areas. In this paper we present a method that computes the Euler characteristic and the genus of a volume dataset. The followed approach, based on the analogy between binary volume datasets and orthogonal pseudo-polyhedra (OPP), computes the mentioned values using two models that are suitable for representing OPP: the Extreme Vertices Model (EVM) and the Ordered Union of Disjoint Boxes (OUDB). We show the results of these methods for phantom models as well as real datasets, and compare the efficiency of the presented methods with those based on the classic voxel model.
AB - Topological characteristics are fundamental in many areas. In this paper we present a method that computes the Euler characteristic and the genus of a volume dataset. The followed approach, based on the analogy between binary volume datasets and orthogonal pseudo-polyhedra (OPP), computes the mentioned values using two models that are suitable for representing OPP: the Extreme Vertices Model (EVM) and the Ordered Union of Disjoint Boxes (OUDB). We show the results of these methods for phantom models as well as real datasets, and compare the efficiency of the presented methods with those based on the classic voxel model.
M3 - Paper
SP - 38
EP - 47
ER -