Course detail

Computer Vision

FEKT-NPOVAcad. year: 2010/2011

See "Curriculum".

Language of instruction

English

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

Ing. Karel Horák, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Knowledge in computer vision theory.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of the course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Course curriculum

1. Introduction to computer vision
2. Image preprocessing
3. Integral transform I.
4. Integral transform II.
5. Image segmentation
6. Region-based segmentation and clustering
7. Description and shape reprezentation
8. Mathematical morphology
9. Classification and automatic sorting
10. Local features and correspondence
11. Image understanding
12. Motion analysis I.
13. Motion analysis II.

Work placements

Not applicable.

Aims

Knowledge in computer vision theory.

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

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 EECC-MN Master's

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

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, 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