**EN2520 Pattern Recognition And Computer Vision**

**Spring Semester, 2010**

Professor David B. Cooper,
Office: Barus Holley, 318, Tel: 863-2601,

E-mail: cooper@lems.brown.edu

First meeting: Wednesday, 9:00 AM in Barus &
Holley 159.

I would like to change the meeting times to twice a
week.

**Text: Pattern Classification, 2 ^{nd} edition,
by R. Duda, P. Hart, D. Stork.**

**Good Reference: Pattern Recognition and Machine
Learning, by C. Bishop.**

This
course is a solid reasonably broad treatment of pattern recognition, with some
application to computer vision. The
pattern recognition topics in this course are fundamental to speech analysis,
computer vision, machine learning, statistical signal processing, human
perception and cognition, data mining, medical diagnosis by computer, etc. These are problems involving making reliable
decisions based on large complicated data sets in the presence of considerable
uncertainty and noise. Roughly 10% of
the course will be devoted to application of the theory and techniques to
estimating free-form 3D shapes and recognizing them based on unorganized messy
3D data or from images taken at multi-views.
The emphasis here is on how to put a complex application problem within the pattern recognition
framework.

**Topics**

**1. ****Bayesian
Decision Theory:** A solid treatment of classification theory in
terms of Bayesian costs, decision functions and the geometry of decision
regions for continuous and discrete random variables. Classification error probabilities and
bounds; missing features; Bayesian belief networks.

**2. ****Maximum-Likelihood
And Bayesian Parameter Estimation, and Bayesian Recognition Using A Priori
Partially Unknown Distributions: ** General theory; Sufficient statistics; Large
sample behavior for arbitrary distributions; Principal component analysis and
discriminants; EM algorithm.

**3. ****Nonparametric
Recognition:** Parzen windows classifiers;
K-Nearest-Neighbor classifiers.

**4. ****Support
Vector Machines.**

**5. ****Multilayer
Neural Networks: ** Introduction to feedforward operation and
classification; Backpropagation algorithm; Behavior considerations.

**6. ****Decision
Trees:** CART (classification and
regression trees).

**7. ****Algorithm-Independent
Machine Learning:** Resampling for estimating statistics and
classifier accuracy --- Bootstrap; Boosting.

**8. ****Unsupervised
Learning And Clustering:** Mixture densities and identifiability;
K-Means clustering; Unsupervised Bayesian learning; Decision-directed
approximation; Hierarchical clustering; Minimum spanning trees.

**9. ****Applications
to estimation and recognition of 3D
geometry from 3D range data or from multi-view images.**

**Course Requirements**

A midterm and a final
examination, 6 homework assignments, a few MATLAB assignments.

**Prerequisite:**

A solid one semester
undergraduate course in statistics or probability theory, or the equivalent,
and some knowledge of linear algebra.

Useful Books on Reserve for
Course EN2520

1. Probability, Random Variables and Stochastic
Processes, 4^{th} edition, by A. Papoulis and S.U. Pillai, McGraw Hill,
2002. ISBN 0-07-366011-6. QA273.P2
2002.

2. Statistical Signal Processing. Detection, Estimation and Time Series
Analysis, by L.L. Scharf, Addison Wesley, 1990. ISBN 0-201-19038-9. TK5102.5.S3528 1990.

3. Introduction to Statistical Pattern
Recognition, 2^{nd} edition, by K. Fukunaga, Academic Press, 1990. ISBN 0-12-269851-7.

4. Elements of Statistical Learning. Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, J. Friedman,
Springer, 2001. ISBN
0-387-95284-5. Q325.75.F75 2001.