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Application of Uncertainty Modeling Frameworks to Uncertain Isosurface Extraction

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9376))

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Abstract

Proper characterization of uncertainty is a challenging task. Depending on the sources of uncertainty, various uncertainty modeling frameworks have been proposed and studied in the uncertainty quantification literature. This paper applies various uncertainty modeling frameworks, namely possibility theory, Dempster-Shafer theory and probability theory to isosurface extraction from uncertain scalar fields. It proposes an uncertainty-based marching cubes template as an abstraction of the conventional marching cubes algorithm with a flexible uncertainty measure. The applicability of the template is demonstrated using 2D simulation data in weather forecasting and computational fluid dynamics and a synthetic 3D dataset.

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Author information

Authors and Affiliations

  1. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA

    Mahsa Mirzargar, Yanyan He & Robert M. Kirby

Authors
  1. Mahsa Mirzargar
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  2. Yanyan He
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  3. Robert M. Kirby
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Corresponding author

Correspondence to Yanyan He .

Editor information

Editors and Affiliations

  1. Science and Technology, Japan Advanced Institute of, Nomi, Japan

    Van-Nam Huynh

  2. Science, Dept of Systems Innovation, Osaka University,Graduate School of, Osaka, Japan

    Masahiro Inuiguchi

  3. Université de Technologie de Compiègne, Compiègne, France

    Thierry Demoeux

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© 2015 Springer International Publishing Switzerland

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Mirzargar, M., He, Y., Kirby, R.M. (2015). Application of Uncertainty Modeling Frameworks to Uncertain Isosurface Extraction. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_32

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  • DOI: https://doi.org/10.1007/978-3-319-25135-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25134-9

  • Online ISBN: 978-3-319-25135-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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