Introduction to the basic concepts in the field of shape recognition; introduction to the contemporary methods for shape recognition.

An overview of principles of contemporary methods for shape recognition. Ability to understand basic principles and methods used in shape recognition, as well as the possibility of simple knowledge extension working on the specific problem.

Statistical shape recognition: Bayes decision theory, parameter estimation and distribution, nearest neighbor method, linear discriminant. Dimensionality reduction: PCA analysis, Fisher discriminant, feature subset selection. Clustering, neural networks, Support Vector Machines, Hidden Markov models. Joint Learning.

Lectures; Auditory Practice; Computer Practice; Laboratory Practice; Consultations.