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UID:20250806T040915EDT-5971cmSuS4@132.216.98.100
DTSTAMP:20250806T080915Z
DESCRIPTION:Title: Spectral Correspondence and Learning of Surface Data - E
 xample on Brain Surfaces\n	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 neuroimaging and\, more ge
 nerally\, problems where data fundamentally lives on surfaces. Key operati
 ons\, such as segmentation and registration\, typically need a common mapp
 ing of surfaces\, often obtained via slow and complex mesh deformations in
  a Euclidean space. Here\, we exploit spectral coordinates derived from th
 e Laplacian eigenfunctions of shapes and also address the inherent instabi
 lity of spectral shape decompositions. Spectral coordinates have the advan
 tage 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\, a
 nd second\, on the learning of surface data\, with an example on automatic
  brain surface parcellation.\n
DTSTART:20181123T183000Z
DTEND:20181123T193000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Herve Lombaert (ETS Montreal/Inria Sophia-Antipolis)
URL:/mathstat/channels/event/herve-lombaert-ets-montre
 alinria-sophia-antipolis-291805
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