Date and Time

Wednesday, April 5, 2006 - 4:00pm to 5:30pm

Location

Computer Science Small Auditorium (Room 105)

Type

Colloquium

Speaker

Stefan Roth [1], from Brown University

Host

Adam Finkelstein

Website

I will introduce several low-level vision tasks, such as image
reconstruction and optical flow estimation, and show how they can be
approached in a unified way as Bayesian inference problems. One key
component of these Bayesian approaches is modeling the prior
distribution. In image reconstruction applications, for example in
image denoising, this amounts to modeling the prior probability of
observing a particular image among all possible images. I will review
Markov random fields (MRFs) and show how they can be used to formulate
image priors. Past MRF approaches have mostly relied on simple random
field structures that only model interactions between neighboring
pixels, which is not powerful enough to capture the rich statistics of
natural images. In my talk I will introduce a new high-order Markov
random field model, termed Fields of Experts (FoE), that better
captures the structure of natural images by modeling interactions
among larger neighborhoods of pixels. The parameters of this model
are learned from a database of natural images using contrastive
divergence learning. I will demonstrate the capabilities of the FoE
model on various image reconstruction applications. Furthermore, I
will show that the Fields-of-Experts model is applicable to a wide
range of other low-level vision problems and discuss its application
to modeling and estimating optical flow.