Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in
Computer Vision
Lecture 1: Markov Random Fields
Jens Rittscher and Chuck Stewart
1
Motivation
Up to now we have considered distributions of a single random varia
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in
Computer Vision
Lecture 6: Markov Chain Monte Carlo and
Gibbs Sampling
Jens Rittscher and Chuck Stewart
Figure 1: Noisy binary image. The left image displays the
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in
Computer Vision
Lecture 8: Belief Propagation
Jens Rittscher and Chuck Stewart
1
Overview
In the next several classes we will discuss more recent methods for est
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in Computer Vision
Lecture 1: Random Variables
Jens Rittscher and Chuck Stewart
1
Motivation
Imaging is a stochastic process:
If we take all the different sources
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in Computer Vision
Lecture 2: Maximum Likelihood and Bayesian
Estimation
Jens Rittscher and Chuck Stewart
1
Motivation and Problem
In Lecture 1 we briey saw how hi
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in Computer Vision
Lecture 4: Gaussian Mixture Models and the EM
Algorithm
Jens Rittscher and Chuck Stewart
1
Motivation
We will continue with our problem of model
Statistical and Learning Techniques for Computer Vision
CSCI 6966

Fall 2006
Statistical and Learning Techniques in Computer Vision
Lecture 3: NonParametric Density Estimation
Jens Rittscher and Chuck Stewart
1
Overview
Motivation for nonparametric methods
Review of point