Hi all,

You are invited to attend the talk by Prof. Hervé Lombaert this Friday, Feb 3, at 13:30, room AA6214.

Title: Spectral Matching & Learning of Surface Data - Example on Brain Surfaces

Abstract: How to analyze complex shapes, such as of the highly folded surface of

the brain?  In this talk, I will show how spectral representations of
shapes can benefit learning problems where data lives on surfaces.
Key operations, such as segmentation and registration, typically need
a common mapping of surfaces, often obtained via slow and complex mesh
deformations in a Euclidean space.  Here, we exploit spectral
coordinates derived from the Laplacian eigenfunctions of shapes and
also address the inherent instability of spectral shape
decompositions.  Spectral coordinates have the advantage over
Euclidean coordinates, to be geometry aware and to parameterize
surfaces explicitly.  This change of paradigm, from Euclidean to
spectral representations, enables a classifier to be applied
*directly* on surface data, via spectral coordinates.

The talk will focus, first, on spectral representations of shapes,
with an example on brain surface matching, and second, on the learning
of surface data, with an example on automatic brain surface
parcellation.

Speaker's bio: Hervé Lombaert is a Starting Research Scientist at Inria Sophia-Antipolis, France, and Associate Professor at ETS, Montreal - with research interests in Statistics on Shapes, Data & Medical Images.  He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada).  He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis, earned the select NSERC Postdoctoral Fellowship and the FQRNT Étudiant-Chercheur Étoile  -  more at [http://cim.mcgill.ca/~lombaert]

Best,

Dima