Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2018/2019

Principles and methods of computer vision, methods and principles of image acquiring, preprocessing methods (statistical processing), filtering, pattern recognition, integral transformations - Fourier transform, image morphology, classification problems, automatic classification, D methods of computer vision, open problems of computer vision.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Dr. Ing. Pavel Zemčík, dr. h. c.

Department

Department of Computer Graphics and Multimedia (UPGM)

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

The students will get acquainted with the principles and methods of computer vision. They will learn in more detail selected methods and algorithms of vision and image acquiring. They will also get acquainted with the possibilities of the scanned data processing. Finally, they will learn how to apply the gathered knowledge practically.
The students will improve their teamwork skills, mathematics, and exploitation of the "C" language.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Homeworks, Mid-term test, individual project.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To get acquainted with the principles and methods of computer vision. To learn in more detail selected methods and algorithms of vision and image acquiring. To get acquainted with the possibilities of the scanned data processing. To learn how to apply the gathered knowledge practically.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Horn, B.K.P.: Robot Vision, McGraw-Hill, 1988, ISBN 0-07-030349-5 Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3  Russ, J.C.: The IMAGE PROCESSING Handbook, CRC Press, 1995, ISBN 0-8493-2532-3 Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X

Recommended reading

Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X
Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3 (EN)
Horn, B.K.P.: Robot Vision, McGraw-Hill, 1988, ISBN 0-07-030349-5
Russ, J.C.: The IMAGE PROCESSING Handbook, CRC Press, 1995, ISBN 0-8493-2532-3

Classification of course in study plans

  • Programme IT-MSC-2 Master's

    branch MBI , 0 year of study, winter semester, elective

  • Programme IT-MGR-1H Master's

    branch MGH , 0 year of study, winter semester, recommended course

  • Programme IT-MSC-2 Master's

    branch MSK , 0 year of study, winter semester, elective
    branch MMM , 0 year of study, winter semester, elective
    branch MBS , 0 year of study, winter semester, elective
    branch MPV , 0 year of study, winter semester, compulsory-optional
    branch MIS , 2 year of study, winter semester, elective
    branch MIN , 0 year of study, winter semester, compulsory-optional
    branch MGM , 0 year of study, winter semester, compulsory-optional

  • Programme IT-MSC-2 Master's

    branch MGMe , 0 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Michal Hradiš, Ph.D.
Ing. Michal Španěl, Ph.D.
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
doc. Ing. Martin Čadík, Ph.D.
doc. Ing. Vítězslav Beran, Ph.D.

Syllabus

  1. Úvod, základy, motivace a aplikace/Introduction, motivation and applications (Hradiš 20.9. slajdy, slajdy, highlights)
  2. Základní principy klasifikace s učitelem - AdaBoost/Basic principles of machine learning with teacher - AdaBoost  (Zemčík 27.9. slajdy-cz, slides-en)
  3. Shlukování, statistické metody/Clustering, statistical methods (Španěl 4.10. slajdy)
  4. Segmentace, analýza barev, analýza histogramu/Segmentation, colour analysis, histogram analysis (Španěl 11.10. slajdy1, slajdy2, slajdy3)
  5. Segmentace,  analýza barev/Segmentation, Colour Analysis, ... finishing (Španěl), Object Detection - Trees (Juránek, 18.10. slajdy-en)
  6. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images (Čadík 25.10. slajdy)
  7. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing (Hradiš, 1.11. slajdy1,  slajdy2, slajdy2-en)
  8. Invariantní Oblasi Obrazu/Invariant Image Regions (Beran, 8.11. slajdy)
  9. Test, Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 15.11. slajdy )
  10. Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging II (Hradiš, 22.11. slajdy )
  11. Registrace obrazu (Čadík, 29.11., slajdy)
  12. 3D Vision/3D Vidění (6.12. Richter FEKT slajdy)
  13. Akcelerace zpracování obrazu, závěr (Zemčík, 13.12.)

POZOR!!! Témata přednášek i data jsou orientační a budou v průběhu semestru aktualizována.

NOTE: The topics and dates are just FYI, not guaranteed,  and will be continuously updated.

Project

26 hod., compulsory

Teacher / Lecturer

Ing. Roman Juránek, Ph.D.
Ing. Michal Hradiš, Ph.D.

Syllabus

  1. Homeworks (4-5 runs) at the beginning of semester
  2. Individually assigned project for the whole duration of the course.