Pattern Recognition

Subject description

Within this course, students are introduced to the fundamental concepts and methods used for processing and recognizing patterns, such as speech signals and computer images. The introductory part covers basic terms, terminology, and pattern representation techniques. Subsequently, students explore methods for pattern decomposition and determining heuristic features, along with analyses of application areas in pattern space using clustering techniques. Emphasis is also placed on identifying the best pattern features through various separation metrics and feature derivation using orthogonal transformations.

The course then investigates pattern classification methods by matching them with already classified patterns and using various decision functions, such as polynomial and probabilistic decision functions. Special attention is given to pattern classification using different models of neural networks. The concluding part addresses the testing of pattern recognizers and the evaluation of classification error probabilities.

The subject is taught in programs

Objectives and competences

The objective of the course is to provide the student with the knowledge of the basic mathematical and computer concepts that are used in the construction of artificial perception systems and are essential components of intelligent systems in automation. The acquired knowledge forms the basis for understanding and designing automatic pattern recognition systems as well as the artificial intelligent systems that are based on automatic learning and knowledge acquisition from different environmental sensor data.

Teaching and learning methods

The lectures provide a theoretical background of all the considered models and methods together with simple computational examples that illustrate the key characteristics of all the presented methods. A textbook and other study material, such as lecture notes with solved example problems and lecture slides, are available to the students. As part of the lectures, the students receive optional homework assignments including theoretical questions as well as computational exercises that enable the students to promptly verify the acquired knowledge. Practical work is carried out as part of the laboratory exercises, where the students solve given programming problems. As part of the laboratory exercises, the students also carry out additional elective projects within which the selected method of automatic pattern recognition in the selected field of application should be implemented. The results of the elective projects are reported in written reports.

Expected study results

After successful completion of the course, students should be able to:

describe the basic concepts and components of the automatic pattern recognition systems that describe their environments through the analysis of sensor data and their conversion into symbolic representations,
explain the basic methods of feature extraction from the sensor data as well as the basic methods of pattern clustering, pattern classification and pattern recognition,
use development tools, program environments, and databases to develop systems for automatic pattern recognition,
analyse sensor data and other basic measurements in order to extract features that are most appropriate for the given application area,
develop automatic pattern recognition systems for the selected application area, and
to evaluate the accuracy and reliability of the given automatic pattern recognition systems.

Basic sources and literature

N. Pavešić: Razpoznavanje vzorcev : uvod v analizo in razumevanje vidnih in slušnih signalov,  3. popravljena in dopolnjena izdaja,  Založba FE in FRI, 2012. ISBN 978-961-243-201-0. [COBISS.SI-ID 260256256]
M. Bishop, Christopher.: Pattern recognition and machine learning, 8. popravljena izdaja, Springer, 2009. ISBN 0-387-31073-8. [COBISS.SI-ID 7988308]
R. C. Gonzalez, R. E. Woods: Digital Image Processing, Pearson, 2018. ISBN 978-1-292-22304-9. [COBISS.SI-ID – 12389972]