Course detail

Computer Vision

FEKT-MPC-POVAcad. year: 2024/2025

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.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Entry knowledge

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.

Rules for evaluation and completion of the course

In the subject Computer Vision, laboratory exercises (40 points) and a final written (49 points) and oral (11 points) exam are evaluated. The written part lasts 90 minutes, no aids. The condition for admission to the exam is a credit from the exercises, ie attendance at all exercises and gaining at least 50% of points. The condition for passing the exam is to gain at least 50% of points from the final written part. Oral examination is optional.
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.

Aims

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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Horák, K. a kol.: Počítačové vidění. Skriptum VUT v Brně. 132 s. 2008. (CS)

Recommended reading

Gonzalez R.C.,Woods R.E.: Digital Image Processing (4th Edition). Pearson 2017. ISBN 978-1292223049 (CS)
Hlaváč V., Šonka M.: Počítačové vidění. Grada 1992. ISBN 80-85424-67-3. (CS)
Russ J.C.: The Image Processing Handbook. CRC Press 1995. ISBN 0-8493-2516-1. (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)

Classification of course in study plans

  • Programme MPC-KAM Master's 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Syllabus

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.

Laboratory exercise

39 hod., compulsory

Teacher / Lecturer

Syllabus

1. Spectral characteristics
2. Active triangulation
3. Thermal vision
4. Hardware image processing
5. Automatic focus
6. Defectoscopy
7. Calibration of the microscope
8. Passive triangulation
9. Description and classification
10. Motion detection in a traffic task