Overview

The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.

The topics covered in the course will include:

• Overview of problems of machine vision and pattern classification
• Image formation and processing
• Feature extraction from images
• Biological object recognition
• Bayesian Decision Theory
• Clustering
• Classification

Applications:

• Object detection and recognition
• Morphable models
• Tracking
• Gesture recognition

The course will have a strong hands-on component. Some additional reading from current research will be provided.

Prerequisites

Basic Linear Algebra, Probability, and Calculus.

Texts

Required Textbooks

Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. 2nd ed. New York, NY: Wiley, 2001. ISBN: 0471056693.

Optional Reading

Mallot, Hanspeter A. Computational Vision: Information Processing in Perception and Visual Behavior. Translated by John S. Allen. Cambridge, MA: MIT Press, 2000. ISBN: 0262133814.

Suggested Further Reading

Forsyth, David A., and Jean Ponce. Computer Vision: a Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 0130851981.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction: with 200 full-color illustrations. New York, NY: Springer, c2001. ISBN: 0387952845.

Grading