Course detail

Processing of Multidimensional Signals

FEKT-BZVSAcad. year: 2015/2016

The Processing of Multidimensional Signals course addresses one-dimensional time signals and two-dimensional image signals as well. Computer based methods and procedures intended for signal and image processing are the main parts of the course.

Learning outcomes of the course unit

An absolvent of the course is able to design and to implement algorithms and methods for processing of both one-dimensional time signals and two-dimensional image signals.


The basic knowledge on the level of secondary school is required in the Processing of Multidimensional Signals course.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)
Russ J.C.: The Image Processing Handbook. CRC Press 1995. ISBN 0-8493-2516-1. (EN)
Hlaváč V., Šonka M.: Počítačové vidění. Grada 1992. ISBN 80-85424-67-3. (CS)

Planned learning activities and teaching methods

Teaching methods include lectures and computer exercises. Course is taking advantage of e-learning (Midas) system.

Assesment methods and criteria linked to learning outcomes

Weekly computer exercises (10x4 pts) and a final exam (60 pts) are evaluated during the Processing of Multidimensional Signals course. For successful pass the course, obtaining of at least half of available points is required in both mentioned parts.

Language of instruction

Czech, English

Work placements

Not applicable.

Course curriculum

1. Introduction to Signal Processing.
2. Discrete Image.
3. Image Acquisition.
4. Brightness Transformation.
5. Geometric Transformation.
6. Integral Transformation.
7. Edge and Corner Detection.
8. Noise Filtering.
9. Image Segmentation.
10. Description.
11. Classification.
12. Mathematical Morphology.


The course is divided into two parts: discrete signals and discrete images. First of all, fundamentals of signal processing, sampling theory, signal reconstruction and discrete filters are introduced with a view to further image processing. Second part of the course contains theory of discrete image processing as geometric and brightness transformations, integral transformations, gradient operators, mathematical morphology and fundamentals of segmentation and classification.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Classification of course in study plans

  • Programme EEKR-B Bachelor's

    branch B-AMT , 3. year of study, winter semester, 6 credits, optional specialized

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, winter semester, 6 credits, optional specialized

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

39 hours, compulsory

Teacher / Lecturer