Advanced Pattern Recognition

Course guarantor:

Michal Haindl

Lecturer:

Tutor:

Michal Haindl

Supervisor:

Department of Theoretical Computer Science

Synopsis:

Lectures follow up the fundamental course Pattern Recognition 1 (MI-ROZ). The fundamentals of statistical pattern recognition based on multidimensional models, contextual classification and recent pattern recognition applications in the area of machine perception will be explained in the lectures.

Requirements:

General knowledge of the following courses: MI-ROZ, statistics and probability, programming in C++.

Syllabus of lectures:

1.Multidimensional models.

2.Random fields.

3.Pattern recognition based on multidimensional models.

4.Contextual classification.

5.Hidden Markov models.

6.Multiclassifier systems.

7.Advanced parameter estimation methods.

8.Unsupervised classification.

9.Modern methods of feature selection.

10.Benchmarking.

11.Data normalization and invariants.

12.Analysis and synthesis of image information.

13.Applications.

Syllabus of tutorials:

Multidimensional models.

2.Random fields.

3.Pattern recognition based on multidimensional models.

4.Contextual classification.

5.Hidden Markov models.

6.Multiclassifier systems.

7.Advanced parameter estimation methods.

8.Unsupervised classification.

9.Modern methods of feature selection.

10.Benchmarking.

11.Data normalization and invariants.

12.Analysis and synthesis of image information.

13.Applications.

Study Objective:

Pattern recognition is the foundation of artificial intelligence, machine perception, computer graphics and many other related research areas, such as data mining, hypermedia, etc. The course objective is to convey advance knowledge from pattern recognition with emphasis on the pattern recognition based on multidimensional statistical models and applications mostly in image processing.

Study materials:

- P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall,1982.

- R. Duda and P. Hart and D.G. Stork, Pattern Classification, J. Wiley, 2001.

- A. Webb, Statistical Pattern Recognition, J. Wiley, 2002.

- S.Theodoridis, K.Koutroumbas, Pattern Recognition, Elsevier, 2003.

- S. Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2009.

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Informatics (doctoral) (compulsory elective course)
• Informatics (compulsory elective course)