A. Bastys "Introduction to pattern recognition"

Catalog description:

Pattern recognition, clustering, feature extraction, neural networks, genetic algorithms.

Current Text:

A. Bastys, Atpaþinimo teorijos elementai, Vilniaus universiteto leidykla, 1997.

Additional Reference:

John A. Richards, Remote sensing digital Image Analysis, Springer-Verlang, 1995.

Goals:

The students should learn the main components of patterns recognition theory - feature extraction, dimensionality reduction, supervised and unsupervised classification. They should also be able to solve by computer some practical problems connected with optical text and speech recognition.

Content:

• Human visual and audio systems.
• The Maars' vision theory.
• Optical characters and speech recognition problems.
• Signal processing for patterns recognition.
• Clustering and unsupervized classification.
• Supervised classification techniques.
• Feature extraction and reduction.
• Application of neural networks for patterns recognition.
• Genetic algorithms.

Required Background:

Course of mathematical analysis, linear algebra, digital signal processing, programming.