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Contents: | The course Pattern Recognition is about the classification and analysis of patterns. 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 analysis). 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, regions). 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. | |

Course form: | Seminar. 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. |