University of Cambridge > Talks.cam > Microsoft Research Cambridge, public talks > Manifold correspondence: a signal processing perspective

Manifold correspondence: a signal processing perspective

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.

This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending

In recent years, geometric data is gaining increasing interest both in the academia and industry. In computer graphics and vision, this interest is owed to the rapid development of 3D acquisition and printing technologies, as well as the explosive growth of publicly-available 3D shape repositories. In machine learning, there is a gradual understanding that geometric structure plays an important role in high-dimensional complicated datasets. In this talk, I will use the problem of manifold correspondence (a fundamental and notoriously hard problem with a wide range of applications in geometric processing, graphics, vision, and learning) as a showcase for classical methods from the domain of signal processing (such as sparse coding, joint diagonalization, and matrix completion) applied to geometric problems. I will show applications to 3D shape correspondence, multi-view clustering, and image labelling.

This talk is part of the Microsoft Research Cambridge, public talks series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity