Segmentation and visualization of multivariate features using feature-local distributions

Authors:
Kenny Gruchalla;Mark Rast;Elizabeth Bradley;Pablo Mininni
Affiliations:
National Renewable Energy Laboratory, Golden, Colorado and University of Colorado, Boulder, Colorado;University of Colorado, Boulder, Colorado and National Center for Atmospheric Research, Boulder, Colorado;University of Colorado, Boulder, Colorado;Universidad de Buenos Aires, Argentina and National Center for Atmospheric Research, Boulder, Colorado
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

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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.