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Pattern recognition

Website:website containing additional information
Course code:INFPR
Credits:6 ECTS (=4.2 old credit points)
Period:period 3 (week 2 through 9, i.e., 6-1-2003 through 28-2-2003; retake week 19)
Participants:up till now 10 subscriptions
Schedule:Dit is een oud rooster!
formgrouptimeweekroomteacher
college   Mon 11-132-9 BBL-416
Thu 15-172-9 BBL-416
lecture          Remco Veltkamp
 
werkcollege   Fri 13-152-9 BBL-416
Contents:The course Pattern Recognition is about the classification and analysis of patterns. As an important example we will look at patterns in images, but the methods are generally applicable. There are numerous application 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 (fingerprint and handwriting analysis). Because more and more measured data is generated, the need for automatic analysis also increases. We will 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, regions). The two corresponding ways of pattern recognition are statistical and geometrical pattern recognition.
Literature:For statistical pattern recognition: Duda, Hart, Stork: Pattern Classification (2nd ed), John Wiley 2001. For geometrical pattern recognition: articles will be made available.
Course form:Seminar. After a few introductory lectures by the teacher, especially about statistical pattern recognition, students present specific articles and book chapters. The students must make a few summary reports.
Exam form:The grade depends on the presentations and summary reports.
Minimum effort to qualify for 2nd chance exam:Om aan de aanvullende toets te mogen meedoen is ontbreken van ten hoogte 1 toetsactiviteit toegestaan.
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 the course Neural Networks, logic and reasoning aspects are not treated to avoid overlap with the course Probabilistic Reasoning.
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