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Colloquium - Saul

Machine-Learning Based Approaches to "Morphing"
AT&T Labs - Research

Given a multidimensional data set and a model of its density, we consider how to define the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals -- one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motion; given two points, the desired interpolation is found by solving a boundary value problem. We show that this interpolation can be done efficiently and discuss applications to the "morphing" of facial images.

Refreshments will be served immediately before the talk at 3:30pm.
Hosted by Dirk Grunwald.

Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
May 5, 2012 (14:13)