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iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization
Dec. 2011 (vol. 17 no. 12)
pp. 1959-1968
ASCII Text
x 
 
Ziyi Zheng, Nafees Ahmed, Klaus Mueller, "iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 1959-1968, Dec., 2011.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2011.218,
author = {Ziyi Zheng and Nafees Ahmed and Klaus Mueller},
title = {iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {17},
number = {12},
issn = {1077-2626},
year = {2011},
pages = {1959-1968},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.218},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization
IS - 12
SN - 1077-2626
SP1959
EP1968
EPD - 1959-1968
A1 - Ziyi Zheng,
A1 - Nafees Ahmed,
A1 - Klaus Mueller,
PY - 2011
KW - Direct volume rendering
KW - k-means
KW - entropy
KW - view suggestion
KW - set-cover problem
KW - ant colony optimization.
VL - 17
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Ziyi Zheng, Stony Brook University
Nafees Ahmed, Stony Brook University
Klaus Mueller, Stony Brook University
The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.

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Index Terms:
Direct volume rendering, k-means, entropy, view suggestion, set-cover problem, ant colony optimization.
Citation:
Ziyi Zheng, Nafees Ahmed, Klaus Mueller, "iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 1959-1968, Dec. 2011, doi:10.1109/TVCG.2011.218
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