After successful completion of the course, students are able to enumerate the basic data structures and representations of structural pattern recognition: relations, graphs and graph pyramids, combinatorial maps, shape and context representations, formal and graph grammars, structure in motion, and topology and to justify the use of the related methodology with its pros and cons in selected applications.

Relations between patterns in space and time; representing structure: strings, arrays, trees, graphs, maps and grammars; shape and context, embedding in an n-dimensional space; distances on a given structure; graph spectra and eccentricity; operations on structures and among structures: recognition, parsing; exact and inexact matching; modification of structure under motion and tracking; topology and homology groups (nD holes, persistence); applications. For details see http://www.prip.tuwien.ac.at/Teaching/.

see content above

lecture 15h

homework:

specify problem 15h

design and select methodology 24h

evaluation concept 5h

presentation 2h

report 14h

- written and oral presentation of a self-chosen subproblem,

- jointly with the design and the selection of the methods for solving the problem, and

- evaluation and critical analysis of the results.

Digital Image Processing, Pattern Recognition, basic mathematical concepts: order theory, relations;

data structures: strings, arrays, trees, graphs, fromal grammars.