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Proceedings Paper

Interactive data-centric viewpoint selection
Author(s): Han Suk Kim; Didem Unat; Scott B. Baden; Jürgen P. Schulze
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Paper Abstract

We propose a new algorithm for automatic viewpoint selection for volume data sets. While most previous algorithms depend on information theoretic frameworks, our algorithm solely focuses on the data itself without off-line rendering steps, and finds a view direction which shows the data set's features well. The algorithm consists of two main steps: feature selection and viewpoint selection. The feature selection step is an extension of the 2D Harris interest point detection algorithm. This step selects corner and/or high-intensity points as features, which captures the overall structures and local details. The second step, viewpoint selection, takes this set and finds a direction that lays out those points in a way that the variance of projected points is maximized, which can be formulated as a Principal Component Analysis (PCA) problem. The PCA solution guarantees that surfaces with detected corner points are less likely to be degenerative, and it minimizes occlusion between them. Our entire algorithm takes less than a second, which allows it to be integrated into real-time volume rendering applications where users can modify the volume with transfer functions, because the optimized viewpoint depends on the transfer function.

Paper Details

Date Published: 25 January 2012
PDF: 12 pages
Proc. SPIE 8294, Visualization and Data Analysis 2012, 829405 (25 January 2012); doi: 10.1117/12.907480
Show Author Affiliations
Han Suk Kim, Univ. of California, San Diego (United States)
Didem Unat, Univ. of California, San Diego (United States)
Scott B. Baden, Univ. of California, San Diego (United States)
Jürgen P. Schulze, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 8294:
Visualization and Data Analysis 2012
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts, Editor(s)

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