Hierarchical clustering of large volumetric datasets

Authors:
Carl J. Granberg;Ling Li
Affiliations:
Curtin University;Curtin University
Venue:
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Year:
2005

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Abstract

In this paper we propose a multiresolution hierarchical data structure called the Ordered Cluster Binary Tree (OCBT). The OCBT is a binary tree structure that extends a Cluster Binary Tree with spatial splitting similar to that of a k-D Tree. We also show how this tree can be improved to extract data efficiently at different sub volumes and levels of detail at run time. We also incorporate a bounding sphere hierarchy to enable early search termination. This clustering algorithm can be made out-of-core and thus enables datasets of several giga bytes in size.