Thursday, September 11, 2003, 12:15 p.m. in GWC604
Rick Archibald
Department of Electrical Engineering
Multivariate Local Edge Detection on Scattered Data
Abstract
This talk will present a new local edge detection method that is
effective on multivariate irregular data in any domain. The method is
numerically cost efficient and entirely independent of any specific shape or
complexity of boundaries. Application of the minmod algorithm to various
orders of our edge detection method ensures a high rate of convergence away
from the discontinuities while reducing the inherent oscillations near the
discontinuities. It further enables distinction of jump discontinuities
from steep gradients, even in instances where only sparse non-uniform data
is available. The resulting minmod edge detection method is successfully
demonstrated in both one and two dimensions.
