ISVC 2011, Part I, LNCS
We introduce an iterative feature-based transfer function de- sign 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 exis- tence of two distinct structure populations in these data, which cannot be isolated or distinguished via traditional transfer functions based on global distributions.
Gruchalla, Kenny; Rast, Mark; Bradley, Elizabeth; and Mininni, Pablo, "Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions" (2011). Astrophysical & Planetary Sciences Faculty Contributions. 5.