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Statistically ``hearing'' the shapes of thingsPDE and Applied Math Seminar
|Speaker: ||Lotfi Hermi, Univeristy of Arizona|
|Location: ||1147 MSB|
|Start time: ||Fri, Apr 10 2009, 4:10PM|
Spectral methods based on the finite difference discretization for
Laplacian eigenvalue problems are emerging as a very robust tool in
computer vision applications such as shape recognition and image
retrieval. Features that arise naturally in the theoretical study of
eigenvalue estimates and bounds--based on combinations of eigenvalues, or
on dimensionless combinations of these physical attributes with various
geometric quantities--are invariant under rotation and translation and are
tolerant to noise, and thus can be used to uniquely characterize and
(statistically) "hear" objects.
In this talk, I will describe some of the algorithms and demonstrate these
notions through various applications, including some recent experiments on
the SQUID Databases (Shape Queries Using Image Databases).
I also give a flavor of how one goes about deriving some of the
theoretical bounds for such spectral functions.
Note special day.