Fields of Experts: A Framework for Learning Image Priors
Speaker: Stefan Roth , Brown University
Date: March 30 2005
Time: 2:45PM to 3:45PM
Location: G449 (Patil/Kiva)
Host: Greg Shakhnarovich, CSAIL
Contact: Greg Shakhnarovich, xx3-8170, gregory@csail
Relevant URL: ABSTRACT
We develop a novel framework for learning generic, expressive image
priors that capture the statistics of natural scenes and can be used
for a variety of machine vision tasks. The approach extends
traditional Markov Random Field (MRF) models by learning potential
functions over extended pixel neighborhoods. Field potentials are
modeled using a Products-of-Experts framework that exploits non-linear
functions of many linear filter responses. In contrast to previous
MRF approaches all parameters, including the linear filters
themselves, are learned from training data. We demonstrate the
capabilities of this "Field of Experts" model with two example
applications, image denoising and image inpainting, which are
implemented using a simple, approximate inference scheme.
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