Purdue University
School of Electrical and Computer Engineering
Video and Image Processing Program
EE641 Digital Image Processing II
Course Notes
Introductory Material
Reading List for Course
Introduction
Short Course Notes
1-D Autoregressive Processes
Random Variables and Estimation
Whittening Random Processes
AR Random Processes
Gaussian Random Fields
2-D AR Processes
Noncausal Gaussian Models
Image Restoration
Mixture Distributions and the EM Algorithm
Clustering and the EM Algorithm
EM Algorithm for Exponential Distributions
Markov Chains and HMM's
The Forward-Backward Algorithm
Discrete Markov Random Fields
Formal Notes
Discrete MRF Models
Segmentation using MRF's
MRF Models and the Hammersley Clifford Theorem
Scanned Notes
The Ising ModelScanned Notes
SimmulationScanned Notes
Continuous Markov Random Fields
Continuous MRF's
Application to Tomography