|Website:||website containing additional information|
|Period:||period 2 (week 46 through 5, i.e., 13-11-2006 through 2-2-2007; retake week 11)
|Participants:||up till now 7 subscriptions|
|Schedule:||Dit is een oud rooster!
|Contents:||The course Pattern Recognition is about the classification and analysis
As an important example we will look at patterns in images and music notation, but the methods are generally applicable.
There are numerous applications of pattern recognition techniques, such as
industrial inspection (e.g. quality control of materials), biomedical
inspection (e.g. chromosome analysis), remote sensing (earth observation),
astronomy (galaxy research), and security (e.g. fingerprint and handwriting
Because more and more measured data is generated, the need for automatic
analysis also increases.
We will look at patterns in two ways: as a collection of features (such as
color and image gradient direction) that occur with a certain probability,
and as a configuration of geometric primitives (such as points, lines,
The two corresponding ways of pattern recognition are statistical and
geometrical pattern recognition.
The seminar will cover two main topics: basic statistical pattern recognition, and pattern recognition in the application domain biometrics.
|Literature:||For statistical pattern recognition:
S. Theodoridis, K. Koutroumbas, Pattern Recognition, third edition, Academic Press, ISBN 0-12-369531-7.
For biometric pattern recognition:
articles will be made available.
After a few introductory lectures by the lecturer, about statistical pattern recognition, and geometrical shape recognition, students present specific articles
and book chapters.
|Exam form:||The grade depends on the given presentations (40%) summaries (40%) and a small exam (20%).
|Minimum effort to qualify for 2nd chance exam:||To participate in the retake of the exam, the original grade must be at least 4.|
|Description:||Topics that are treated are: probability density functions, Bayesian
decision theory, feature space, supervised and unsupervised classification,
parametric and non-parametric decision models, geometric patterns,
shape similarity measures.
Neural networks are not treated to avoid overlap with the course Neural Networks,
logic and reasoning aspects are not treated to avoid overlap with the
course Probabilistic Reasoning.