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Michael Black

 

(Max Planck Institute for Intelligent Systems Perceiving Systems Department Tübingen, Germany http://is.mpg.de/ps)

 

Wednesday 1st February 2012

16.00pm

 

B10 Seminar Room,

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

 

Modernizing Muybridge: From 3D Models of the Body to Decoding the Brain

 

In the late 1800's a revolution started.  Photography allowed the capture and study of human and animal motion.  At the same time electrical signals were recorded from the surfaces of living brains.
Today modern computer vision and neuroscience are coming together to reveal clues as to how the brain controls the complex movements of our bodies.  I will review recent work on video-based human body shape and motion estimation that uses statistical models of 3D body shape learned from thousands of laser range scans of the human body.
I will also describe how markerless motion capture is leading to a new understanding of the neural control of natural movement.  Building on these insights it is now possible to restore or improve lost function in people with central nervous system injury by directly coupling brains with computers.  I will summarize our recent work on developing brain-machine interfaces that allow paralyzed individuals to control movement in the world with only their thoughts.

 

Bio:
Michael Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D.  from Yale University (1992).  After post-doctoral research at the University of Toronto, he worked at Xerox PARC as a member of research staff and an area manager.  From 2000 to 2010 he was on the faculty of Brown University in the Department of Computer Science (Assoc. Prof. 2000-2004, Prof. 2004-2010).  He is presently one of the founding directors at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department.  He is also an Adjunct Professor (Research) in Computer Science at Brown University and a Visiting Professor of Electrical Engineering at Stanford University.  His work has won several awards including the IEEE Computer Society Outstanding Paper Award (1991), Honorable Mention for the Marr Prize (1999 and 2005), and the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision.