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Proceedings Paper

Classification algorithm of 3D volumetric data for hierarchical visualization
Author(s): Mie Sato; Yuh-Hwan Liu; Hiroki Takahashi; Masayuki Nakajima
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Paper Abstract

This paper presents one part of our work on 'hierarchical visualization.' Here, we propose an efficient hierarchical structure for the visualization of 3D volumetric data, where for instance, data compression techniques are embedded into the hierarchical structure. The hierarchical structure consists of two layers. The first hierarchy roughly visualizes the whole lossy compressed data so that we can quickly understand and interpret the outline of the whole 3D volumetric dataset. This step allows users to choose the desired parts of the 3D volumetric data. The second hierarchy is used for scrutinizing the detail of the chosen parts using lossless compressed data. To implement the hierarchical structure we propose a partitioning algorithm for 3D volumetric data based on principal component analysis. With principal component analysis, the original 3D volumetric space can be divided into a number of 3D volumetric blocks such that each block contains similar data. Analyzing these specific blocks and taking advantage of an octree hierarchy, from the whole 3D volumetric dataset we can easily access only the parts which are required for display. The proposed partitioning algorithm should be very useful in reducing the amount of rendering, which means that we will need only to precisely render the blocks required for display, and not the whole 3D volumetric dataset.

Paper Details

Date Published: 18 June 1998
PDF: 8 pages
Proc. SPIE 3422, Input/Output and Imaging Technologies, (18 June 1998); doi: 10.1117/12.311082
Show Author Affiliations
Mie Sato, Tokyo Institute of Technology (United States)
Yuh-Hwan Liu, Tokyo Institute of Technology (Japan)
Hiroki Takahashi, Tokyo Institute of Technology (Japan)
Masayuki Nakajima, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 3422:
Input/Output and Imaging Technologies
Yusheng Tim Tsai; Teh-Ming Kung; Jan Larsen, Editor(s)

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