**Title:** Graphical Models for Computer Vision

By
Prof. Brian Potetz

The
University of Kansas

**Abstract:**

Statistical
approaches to computer vision problems are quickly becoming more feasible and
more popular. Often, visual problems are modeled using Markov Random Fields or
Factor Graphs where each variable node encodes a single pixel or image region.
This results in highly interconnected graphs which make both inference and
learning more difficult. In many cases, variables in these graphical models are
real-valued or have many possible states, further complicating inference.

In
this tutorial we will first present an overview of statistical models of
natural images, and then discuss some statistical approaches to common computer
vision applications. We will then discuss the particular approaches to
inference (belief propagation, graph cuts) and learning (contrastive
divergence, iterative scaling) that are commonly employed in the computer
vision community.