• 
Introduction 
1.5 hours 

￮ 
Overview of computer vision, related areas, and applications 

• 
Feature extraction 
4.5 hours 

￮ 
Detection of edges in images 


￮ 
Canny edge detector 


￮ 
Detection of corners 


￮ 
Harris corner detector 

• 
Probabilistic modeling 
4.5 hours 

￮ 
Review of probability and Bayes' theorem 


￮ 
Principles of probabilistic modeling 


￮ 
Estimation paradigms 


￮ 
Maximum likelihood estimation (MLE) 


￮ 
Bayesian estimation 

• 
Camera calibration 
4.5 hours 

￮ 
Camera models 


￮ 
Intrinsic and extrinsic parameters 


￮ 
Radial lens distortion 


￮ 
Direct parameter calibration 


￮ 
Camera parameters from the projection matrix 

• 
Epipolar geometry 
4.5 hours 

￮ 
Introduction to projective geometry 


￮ 
Epipolar constraints 


￮ 
The essential and fundamental matrices 

• 
Statistical estimation 
4.5 hours 

￮ 
The ExpectationMaximization (EM) algorithm 


￮ 
Implementation issues 


￮ 
EM variants 

• 
Model reconstruction 
4.5 hours 

￮ 
Reconstruction by triangulation 


￮ 
Reconstruction up to a scale factor 


￮ 
Reconstruction up to a projective transformation 

• 
Statistical filtering 
4.5 hours 

￮ 
Iterated estimation 


￮ 
Observability and linear systems 


￮ 
The Kalman Filter 


￮ 
The Extended Kalman Filter 

• 
Motion Estimation 
6.0 hours 

￮ 
Motion field of rigid objects 


￮ 
Motion parallax 


￮ 
Optical flow 


￮ 
The image brightness constancy equation 


￮ 
Differential techniques 


￮ 
Featurebased techniques 

• 
Recognition 
6.0 hours 

￮ 
Invariants 


￮ 
Invariantbased recognition algorithms 


￮ 
Image eigenspaces 


￮ 
Introduction to object modeling; shape from single image cues 

Total 
45 hours 