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Title: Multivariate volume visualization through dynamic projections

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. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Authors:
 [1] ;  [1] ;  [2] ;  [2] ;  [1]
  1. Univ. of Utah, Salt Lake City, UT (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
OSTI Identifier:
1221569
Report Number(s):
DOE-UTAH-PASCUCCI--0003
DOE Contract Number:
NA0002375
Resource Type:
Conference
Resource Relation:
Conference: 4. IEEE Symposium on Large Data Analysis and Visualization, Paris (France), 9-10 Nov 2014
Research Org:
Univ. of Utah, Salt Lake City, UT (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING HPC visualization