Course detail

Computer Vision

FEKT-MPOVAcad. year: 2018/2019

The Computer Vision course addresses methods for acquisition and digital processing of an image data. The main parts of the course are technical equipments, algorithms and methods for image processing.

Learning outcomes of the course unit

An absolvent of the course is able to design and to implement algorithms and methods for processing of an image data, pattern recognition and dynamic scene analysis.


The knowledge on the level of the Bachelor's degree is required in the Computer Vision course. Moreover, knowledge and skills equivalent to BZSV course are required.


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. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

Weekly laboratory exercises (40 pts) and a final exam (60 pts) are evaluated during the Computer Vision course. For successful pass the course, obtaining of at least half of available points is required in all mentioned parts.

Language of instruction


Work placements

Not applicable.

Course curriculum

1. Introduction and motivation.
2. Basic physics concepts.
3. Optics in computer vision.
4. Electronics in computer vision.
5. Segmentation.
6. Detection of geometrical primitives.
7. Objects detection and plane measuring.
8. Objects description.
9. Classification and automatic sorting.
10. Optical character recognition.
11. Motion analysis.
12. Optical 3D measuring.
13. Traffic applications.


An absolvent is able to describe algorithms for image processing and to implement them into an superordinate system of computer vision.

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-M1 Master's

    branch M1-KAM , 1. 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


Laboratory exercise

26 hours, compulsory

Teacher / Lecturer