TITLE:
Multi-scale Data Visualization: Illumination and Perception

Chris Weigle
Joint Institute for Computational Sciences
University of Tennessee
and
Oak Ridge National Labs

ABSTRACT:
Large datasets typically contain coarse features comprised of ï¬^Áner sub-
features. Even if the shapes of the small-scale structures are evident
in a 3D display, the aggregate shapes they suggest may not be easily
inferred. From previous studies in visual perception, the evidence has
not been clear whether physically-based illumination confers any
advantage over local illumination for understanding scenes that arise
in the visualization of large data sets.

I present my recent research showing that physically-based illumination
can improve the perception of static scenes of complex 3D geometry from
flow fields. I performed two human-subjects experiments (a depth
discrimination task and a shape discrimination task) to quantify the
effect of physically-based illumination on task performance. I show
that for the considered tasks physically-based illumination influences
participant performance as strongly as perspective projection,
suggesting that physically-based illumination is in fact a strong cue
to the layout of complex data.

BIO:
Chris Weigle is a post-doctoral researcher with the University of
Tennessee and Oak Ridge National Labs supported Joint Institute for
Computational Sciences. His research includes perceptual and cognitive
issues in scientific and medical visualization, with a particular
interest in displays of large-scale or geometrically-complex data.
Weigle received his Ph.D. in Computer Science from the University of
North Carolina in 2006.