2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV) (2014)
Nov. 9, 2014 to Nov. 10, 2014
Shusen Liu , SCI Institute, University of Utah, USA
Bei Wang , SCI Institute, University of Utah, USA
Jayaraman J. Thiagarajan , Lawrence Livermore National Laboratory, USA
Peer-Timo Bremer , Lawrence Livermore National Laboratory, USA
Valerio Pascucci , SCI Institute, University of Utah, USA
We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Data visualization, Principal component analysis, Hurricanes, Navigation, Transfer functions, Image color analysis, Space exploration
S. Liu, B. Wang, J. J. Thiagarajan, P. Bremer and V. Pascucci, "Multivariate volume visualization through dynamic projections," 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), France, 2014, pp. 35-42.