Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Seminar Pattern recognition

Website:website containing additional information
Course code:INFOPR
Credits:7.5 ECTS
Period:period 2 (week 46 through 5, i.e., 10-11-2008 through 30-1-2009; retake week 11)
Timeslot:C
Participants:up till now 15 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
formgrouptimeweekroomteacher
seminar          Remco Veltkamp
 
Contents:The course Pattern Recognition is about the classification and analysis of patterns. 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 analysis). Because more and more measured data is generated, the need for automatic analysis also increases. 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.
Course form:Seminar. After a few introductory lectures by the lecturer, students present specific articles and book chapters.
Exam form:The grade depends on the given presentations (40%) summaries (40%) and a small written exam (20%).
Minimum effort to qualify for 2nd chance exam:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.
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.
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