Multivariate volume visualization through dynamic projections

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Publication Type pre-print
School or College <blank>
Department Computing, School of
Creator Pascucci, Valerio
Other Author Liu, Shusen; Wang, Bei; Thiagarajan, Jayaraman J.; Bremer, Peer-Timo
Title Multivariate volume visualization through dynamic projections
Date 2014-01-01
Description 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.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 35
Last Page 42
Language eng
Bibliographic Citation Liu, S., Wang, B., Thiagarajan, J. J., Bremer, P.-T., & Pascucci, V. (2014). Multivariate volume visualization through dynamic projections. IEEE Symposium on Large Data Analysis and Visualization, 35-42.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 3,990,839 bytes
Identifier uspace,19316
ARK ark:/87278/s6768qgr
Setname ir_uspace
Date Created 2015-03-20
Date Modified 2021-05-06
ID 712872
Reference URL https://collections.lib.utah.edu/ark:/87278/s6768qgr