Course detail

Computer Vision

FEKT-MPOVAcad. year: 2019/2020

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.

Guarantor

Ing. Ilona Janáková, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Graduates of the course are acquainted with basic physical principles and technical equipments used in computer vision. The graduate is able to design and implement algorithms and methods of image data processing, object segmentation, pattern recognition, dynamic scene analysis, optical 3D measurement, OCR, etc. In laboratory exercises students solve problems representing the most common applications of computer vision.

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 (Midas) system. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

In the subject Computer vision are evaluated compulsory laboratory exercises (40 points) and final written (49 points) and oral (11 points) exam. The condition for admission to the exam is the credit from the exercises, ie attendance at all exercises and gain at least 50% points. For successful completion of the course it is necessary to obtain at least half the number of points from the exercises and the written part of the exam. The oral part of the exam is non-compulsory.

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

Not applicable.

Recommended reading

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 EEKR-M Master's

    branch M-KAM , 1 year of study, winter semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

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

Syllabus

Introduction, applications fo computer vision
Basic principles of computer vision
Methods and principles of image acquiring
Representations of image data and their features
Image preprocessing, statistical image processing
Integral image transforms - Fourier transform
Features of Fourier transform, fast Fourier transform
Wavelet transform
Discrete cosine transform, L-V transform
Image morphology
Classification problems, automatic classification
3D methods of computer vision
Conclusion, open problems of computer vision

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Ilona Janáková, Ph.D.

Syllabus

Individually assigned project for the whole duration of the course