Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
William T. Freeman
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Monday, January 27, 2003, 4:15PM
The goal of low-level vision is to estimate an underlying scene, given
an observed image. Real-world scenes (eg, shapes, or patterns of
reflectance) can be complex, conventionally requiring high dimensional
representations which can be hard to estimate and to store. But in
many situations, scene and image are closely related and it is
possible to learn a functional relationship between them. The scene
information can then be represented in reference to the image, where
the functional specifies how to translate the image into the
We illustrate the use of this representation for encoding shape
information. We show that this representation has appealing
properties such as low dimensionality, locality, and slow variation across
space and scale. These properties allow us to improve initial shape
estimates from modalities such as stereo.
I'll also show two 5 minute "shorts": presentations showing results
I'm excited about on the topics of non-parametric belief propagation and
separating shading and reflectance.
About the Speaker
William T. Freeman is an Associate Professor of Electrical Engineering
and Computer Science at the Artificial Intelligence Laboratory at MIT,
joining the faculty in September, 2001. From 1992 - 2001 he worked at
Mitsubishi Electric Research Labs (MERL), most recently as
Sr. Research Scientist and Associate Director. He studied computer
vision for his PhD in 1992 from the Massachussetts Institute of
Technology, and received a BS in physics and MS in electrical
engineering from Stanford in 1979, and an MS in applied physics from
Cornell in 1981.
His current research interests include machine learning applied to
computer vision and graphics, and Bayesian models of visual
perception. Previous research topics include steerable filters and
pyramids, the generic viewpoint assumption, color constancy, computer
vision for computer games, and separating "style and content".
Hobbies include kite aerial photography (see
http://www.ai.mit.edu/people/wtf/ ) and ping-pong.
Non-parametric belief propagation:
Separating shading and reflectance
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