A Computer Vision Approach for Visualization from Sparse, Noisy 3-D Data
Abstract
We are interested in feature extraction from volume data
in terms of coherent surfaces and 3-D space curves. The
input can be an inaccurate scalar, vector or tensor field,
sampled densely or sparsely on a regular 3-D grid.
Even if such data is obtained from state-of-the-art
sensors or data sources, unavoidable outlier noise and missing
data can make the traditional iso-surface techniques
inappropriate. Here, we propose a unified computational
scheme, Tensor Voting, for dealing with such real data.
The representation scheme is one of a second-order symmetric
tensor that encodes preferred direction, as well as orientation
uncertainty information at discontinuities. Data communication
is achieved by a novel voting process that simultaneously infers
smooth structures, detect orientation discontinuities, and ignores
outlier noise. This process produces a dense tensor map, which
is further decomposed into a surface, a curve, and a junction
map. A modified marching process is then used for coherent
surface and curve extraction. In this talk, a live demo will
be given, and other application domains such as
flow visualization, vortex extraction, terrain, seismic data processing,
and some medical imagery applications will be discussed.
On-line references
Integrated Surface Inference from Sparse Data Sets