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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2006, Vol. 7 Issue (7): 1134-1140    DOI: 10.1631/jzus.2006.A1134
Rendering & Animation     
Out-of-core clustering of volumetric datasets
GRANBERG Carl J., LI Ling
Department of Computing, Curtin University of Technology, Perth, Australia
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Abstract  In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree (OCBT) structure created using a top-down or divisive clustering method. The OCBT structure allows fast and efficient sub volume queries to be made in combination with level of detail (LOD) queries of the tree. The initial partitioning of the large out-of-core dataset is done by using non-axis aligned planes calculated using Principal Component Analysis (PCA). A hybrid OCBT structure is also proposed where an in-core cluster binary tree is combined with a large out-of-core file.

Key words： Out-of-core clustering      Hybrid rendering      Scientific visualization     
Received: 07 April 2006     
CLC:  TP39  
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GRANBERG Carl J.
LI Ling
Cite this article:

GRANBERG Carl J., LI Ling. Out-of-core clustering of volumetric datasets. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2006, 7(7): 1134-1140.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2006.A1134     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2006/V7/I7/1134

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