Course detail

Computer Vision

FEKT-MPC-POVAcad. year: 2022/2023

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

Mode of study

Not applicable.

Guarantor

Ing. Ilona Janáková, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

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.

Prerequisites

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.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

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

Assesment methods and criteria linked to learning outcomes

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.

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.

Work placements

Not applicable.

Aims

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.

Recommended optional programme components

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

Ing. Petr Petyovský, Ph.D.
Ing. Miloslav Richter, Ph.D.
Ing. Peter Honec, Ph.D.
Ing. Karel Horák, Ph.D.
Ing. Ilona Janáková, Ph.D.

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

Ing. Ilona Janáková, Ph.D.

Syllabus

Individually assigned project for the whole duration of the course