Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions

  • Kenny Gruchalla
  • Mark Rast
  • Elizabeth Bradley
  • Pablo Mininni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

We introduce an iterative feature-based transfer function design that extracts and systematically incorporates multivariate feature-local statistics into a texture-based volume rendering process. We argue that an interactive multivariate feature-local approach is advantageous when investigating ill-defined features, because it provides a physically meaningful, quantitatively rich environment within which to examine the sensitivity of the structure properties to the identification parameters. We demonstrate the efficacy of this approach by applying it to vortical structures in Taylor-Green turbulence. Our approach identified the existence of two distinct structure populations in these data, which cannot be isolated or distinguished via traditional transfer functions based on global distributions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kenny Gruchalla
    • 1
    • 2
  • Mark Rast
    • 2
    • 3
  • Elizabeth Bradley
    • 2
  • Pablo Mininni
    • 4
    • 3
  1. 1.National Renewable Energy LaboratoryGoldenUSA
  2. 2.University of ColoradoBoulderUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Universidad de Buenos AiresArgentina

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