CS780 - Computer Vision - Fall'2003

• This course introduces computational models of computer vision and their implementation on computers, and focuses on material that is fundamental and has a broad scope of application.  Topics include contemporary developments in all mainstream areas of computer vision e.g., Image Formation, Feature Detection/Representation, Classification and Recognition, Motion Analysis, Camera Calibration, 3D/Stereo Vision, Shape From X (motion, shading, texture, etc.), and typical applications such as Biometrics.
  

• E. Trucco and A. Verri, Introductory Techniques for 3D Computer Vision, Prentice Hall, 1998.
• D. Forsyth and J. Ponce, Computer Vision - A modern approach, Prentice Hall, 2003. (online draft).
• D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, Freeman, San Francisco, 1982.
• Selected papers.

1. Introduction
• Computer Vision Fundamentals
• Related Fields: IP, PR, AI
2. Image Formation
• Basic Optics
• Basic Radiometry
• Camera Models
• Camera Parameters
3. Geometric Feature Representation
• Noise Reduction
• Edge Detection (Canny)
• Corner Detection
• Line & Curve Detection (Hough Transform),
• Ellipse Detection
• Deformable Contours (snakes)
4. Statistical Feature Representation
• Principal Component Analysis (PCA)
• Independent Component Analysis (ICA)
• Shape and Texture
• Gabor Wavelets
5. Classification and Recognition Methods
• Bayes Classifier and the MAP Rule
• LDA/FLD
• Kernel Methods (kernel PCA, kernel FLD, etc.)
6. Motion Analysis
• Motion Field
• Optical Flow
• Tracking (Kalman filter, Condensation)
• Structure From Motion
7. Biometrics
• Face Detection
• Face Tracking
• Face Recognition
8. Camera Calibration
• Intrinsic/Extrinsic Camera Parameters
• Explicit Parameter Calibration
• Projection Matrix-based Parameter Calibration
  
9. 3D Vision - Stereo Vision
• Correspondence
• Epipolar Geometry (E and F matrices)
• 3D Reconstruction
10. Shape From X
• Shape From Shading
• Shape From Texture
11. Evolutionary Computation for Computer Vision
• Genetic Algorithms (GA)
• Evolutionary Strategy (ES)
• Evolutionary Programming (EP)
12. Neural Computation for Computer Vision
• Multilayer Perceptrons and BP Algorithm
• Radial-Basis Function Networks
13. Machine Learning  for Computer Vision
• Bayesian Learning
• Decision Tree
• Reinforcement Learning
14. Statistical Learning Theory (STL)
• Structural Risk Minimization (SRM)
• Support Vector Machines (SVM)

Projects (topics are related to our course Contents)

Presentation (~10 mins.)

Paper (~10 pages)

Class attendance

• The Computer Vision Homepage
• CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
• Vision Science: An Internet Resource for Research in Human and Animal Vision
• Computer Vision Online Publications
• Sussex Computer Vision Teach Files
• The Face Recognition Home Page
• Face Detection Home Page
• MATLAB
• Matlab Primer (Third Edition, By Kermit Sigmon - pdf file)
• Matlab Getting Started (copyright Mathworks - pdf file)
• Matlab Image Processing Toolbox User's Guide (copyright Mathworks - pdf file)