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SUMMARY:Manifold correspondence: a signal processing perspective - Michael
  Bronstein\, USI Switzerland
DTSTART:20150203T130000Z
DTEND:20150203T140000Z
UID:TALK57567@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:In recent years\, geometric data is gaining increasing interes
 t both in the academia and industry. In computer graphics and vision\, thi
 s interest is owed to the rapid development of 3D acquisition and printing
  technologies\, as well as the explosive growth of publicly-available 3D s
 hape repositories. In machine learning\, there is a gradual understanding 
 that geometric structure plays an important role in high-dimensional compl
 icated datasets.\nIn this talk\, I will use the problem of manifold corres
 pondence (a fundamental and notoriously hard problem with a wide range of 
 applications in geometric processing\, graphics\, vision\, and learning) a
 s a showcase for classical methods from the domain of signal processing (s
 uch as sparse coding\, joint diagonalization\, and matrix completion) appl
 ied to geometric problems. I will show applications to 3D shape correspond
 ence\, multi-view clustering\, and image labelling.
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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