Positive Visualization: Constraining the Modified Quadratic Shepard Interpolant
by
Ken Brodlie
University of Leeds, visiting Lincoln University

Interpolation is a fundamental technique in visualizing sparse datasets. If the data is sampled from a source known to be positive everywhere, it is important to preserve this positivity in the interpolant. Linear interpolants will satisfy this property naturally, but it seems harder to achieve in higher order methods. This talk will describe work in progress to develop positive variants of the modified quadratic Shepard method, for both curves and surfaces.

Date received: February 6, 2002

Copyright © 2002 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caie-19.