Time-Varying Data Visualization
Incorporating Highlighting Animations into Static Visualizations
in the proceedings of SPIE Electronic Imaging 07
Jonathan Woodring and Han-Wei Shen, 2007
Time-Varying, and Comparative Visualization with Contextual Cues
IEEE Visualization 2006, also in a special issue of IEEE Transactions on Visualization and Computer Graphics
Jonathan Woodring and Han-Wei Shen, Multi-variate
Feature Tracking using Earth Mover’s Distance and Global Optimization
Pacific Graphics 2006
Guangfeng Ji and Han-Wei Shen
A Multiresolution Volume Rendering Framework
for Large-Scale Time-Varying Data Visualization
International Workshop on Volume Graphics
Chaoli Wang, Jinzhu Gao, Liya Li, and Han-Wei Shen, June 2005
Efficient Isosurface Tracking
Using Precomputed Correspondence Table
Eurographics-IEEE TCVG Symposium on Visualization
Guangfeng Ji and Han-Wei Shen, May 2004
A Direct Rendering Technique
for Visualizing Time-Varying Data
2003 International Workshop on Volume Graphics
Jonathan Woodring, Han-Wei Shen, Chronovolumes, 2003
Volume Tracking Using Higher Dimensional Isocontouring
IEEE Visualization 2003
Guangfeng Ji, Han-Wei Shen, and Rephael Wenger, Oct. 2003
High Dimensional Direct Rendering of Time-Varying Volumes
IEEE Visualization 2003
Jonathan Woodring, Chaoli Wang, and Han-Wei Shen, Oct. 2003
Time-Critical Volume Rendering
ACM SIGGRAPH 2001 Technical Sketches and Applications.
Han-Wei Shen and Xinyue Li, Aug. 2001
Accelerating time-varying hardware volume rendering
using TSP trees and color-based error metrics
IEEE/ACM 2000 Symposium on Volume Visualzation
David Ellsworth, Ling-Jen Chiang, and H.-W. Shen, Oct. 2000
Visualization Techniques for Time-Varying Volume Data
2000 International Computer Symposium (ICS2000)
Kwan-Liu Ma and Han-Wei Shen, Dec, 2000
Time-Varying Volume Rendering
using a Time-Space Partitioning Tree
IEEE Visualization '99, San Francisco, California, October 1999
Han-Wei Shen, Ling-Jen Chiang, and Kwan-Liu Ma
One major factor that is contributing to the growth of data size is the increasingly widespread ability to perform very large scale time-varying simulations. Although intensive research has been undertaken to optimize the performance of visualizing very large data sets, most of the existing methods have not targeted at time-varying data.
One mission of the Gravite Research Group is to perform a comprehensive study of end-to-end solutions to facilitate efficient and effective analysis of large-scale time-varying data. The Gravite Research Group has developed algorithms to tackle a wide range of problems in time-varying datas
visualization, including feature extraction and tracking, multi-resolution time-varying data analysis, high dimensional rendering algorithms, aata structures for efficient indexing and storing time-varying data.
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The Ohio State University, Department of Computer Science and Engineering
395 Dreese Lab, 2015 Neil Avenue, Columbus OH 43210
Professor Han-Wei Shen
hwshen@cse.ohio-state.edu (V) 614 292 0060 (F) 614 2922911
