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International Journal for Uncertainty Quantification
IF: 0.967 5-Year IF: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2015013730
pages 433-451

AN UNCERTAINTY VISUALIZATION TECHNIQUE USING POSSIBILITY THEORY: POSSIBILISTIC MARCHING CUBES

Yanyan He
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UTAH 84112, USA
Mahsa Mirzargar
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UTAH 84112, USA
Sophia Hudson
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UTAH 84112, USA
Robert M. Kirby
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UTAH 84112, USA; School of Computing, University of Utah, Salt Lake City, UTAH 84112, USA
Ross T. Whitaker
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UTAH 84112, USA; School of Computing, University of Utah, Salt Lake City, UTAH 84112, USA

ABSTRACT

This paper opens the discussion about using fuzzy measure theory for isocontour/isosurface extraction in the field of uncertainty visualization. Specifically, we propose an uncertain marching cubes algorithm in the framework of possibility theory, called possibilistic marching cubes. The proposed algorithm uses the dual measures−possibility and necessity−to represent the uncertainty in the spatial location of isocontour/isosurface, which is propagated from the uncertainty in ensemble data. In addition, a novel parametric way of constructing marginal possibility distribution is proposed so that the epistemic uncertainty due to the limited size of the ensemble is considered. The effectiveness of the proposed possibilistic marching cubes algorithm is demonstrated using 2D and 3D examples.