[Colloquium] TTIC Talk: Mathieu Salzmann, UC Berkeley

[Julia MacGlashan]

When:             *Thursday****, June 10 @ 1:00pm
*

Where:           * TTIC Conference Room #526*, 6045 S Kenwood Ave, 5th Floor


Who:              * Mathieu Salzmann, UC Berkeley***


Title:          *     **Combining Explicit Constraints and Learning for
Non-Rigid Reconstruction*******



 Non-rigid reconstruction is of critical importance in domains such as
personal robotics for assistance to disabled people, or medical imaging for
surgical interventions. Addressing these challenging tasks requires
effective deformable shape recovery techniques or articulated pose
estimation methods. Throughout the years, these two problems have evolved as
separate fields. Recent approaches to deformable shape recovery have focused
on improving the algorithmic formulation, but have only considered the case
of reconstruction from point-to-point correspondences. In contrast, many
techniques for pose estimation have followed a discriminative approach,
which allows for the use of more general image cues. However, these
techniques typically employ discriminative methods that do not model the
constraints between the output dimensions. In this talk, I will propose a
unified framework for articulated pose estimation and 3D surface
reconstruction. To this end, I will present an approach to explicitly
constraining the prediction of discriminative methods. Furthermore, I will
show that the resulting formulation allows for the combination of generative
and discriminative methods into a single, common framework.

BIO:

Since February 2009, Mathieu Salzmann is a postdoctoral fellow in Prof.
Trevor Darrell's group at the International Computer Science Institute and
EECS at UC Berkeley. Previously, he completed a Ph.D with Prof. Pascal Fua
at the Computer Vision laboratory at EPFL in Switzerland. He received his
M.Sc. degree in Computer Science from EPFL in 2004.

Host:             Raquel Urtasun, rurtasun at ttic.edu
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