1 research outputs found
Visualization of Unsteady Flow Using Heat Kernel Signatures
We introduce a new technique to visualize complex flowing phenomena by using
concepts from shape analysis. Our approach uses techniques that examine the
intrinsic geometry of manifolds through their heat kernel, to obtain
representations of such manifolds that are isometry-invariant and multi-scale.
These representations permit us to compute heat kernel signatures of each point
on that manifold, and we can use these signatures as features for
classification and segmentation that identify points that have similar
structural properties.
Our approach adapts heat kernel signatures to unsteady flows by formulating a
notion of shape where pathlines are observations of a manifold living in a
high-dimensional space.
We use this space to compute and visualize heat kernel signatures associated
with each pathline.
Besides being able to capture the structural features of a pathline, heat
kernel signatures allow the comparison of pathlines from different flow
datasets through a shape matching pipeline. We demonstrate the analytic power
of heat kernel signatures by comparing both (1) different timesteps from the
same unsteady flow as well as (2) flow datasets taken from ensemble simulations
with varying simulation parameters. Our analysis only requires the pathlines
themselves, and thus it does not utilize the underlying vector field directly.
We make minimal assumptions on the pathlines: while we assume they are sampled
from a continuous, unsteady flow, our computations can tolerate pathlines that
have varying density and potential unknown boundaries. We evaluate our approach
through visualizations of a variety of two-dimensional unsteady flows.Comment: Topic: Visualization, Topic: Heat Kernel, Topic: Flow Visualization,
Topic: Heat Kernel Signature