Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2025/2026

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

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

Homeworks, Mid-term test, individual project.

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.
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", C++, and other languages.

Study aids

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

Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3 (EN)
IEEE Multimedia, IEEE, USA - série časopisů - různé články (EN)
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146 (EN)
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146 (EN)
(EN)

Classification of course in study plans

  • Programme IT-MGR-1H Master's

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

  • Programme MIT-EN Master's 0 year of study, winter semester, elective

  • Programme MITAI Master's

    specialization NSEC , 0 year of study, winter semester, elective
    specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, compulsory
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective
    specialization NISY , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, compulsory
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

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

Syllabus

    1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Hradiš 20.9.)
    2. Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models/Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 27.9.)
    3. Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 4.10. slajdy1, slajdy2, slajdy3)
    4. Scanning object detection, boosted classifiers, acceleration/Detekce objektů oknem, boostované klasifikátory, akcelerace (Zemčík 11.10.)
    5. Object Detection - Trees, Random Forests, Yolo/Detekce objektů - Stromy, "Random Forests",Yolo (Juránek, 18.10. slajdy-en)
    6. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 25.10. slajdy)
    7. Hough Transform, RHT, RANSAC, Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování sekvencí (Hradiš, 1.11. slajdy1, slajdy2, slajdy2-en)
    8. 3D Computer Vision/3D počítačové vidění (8.11. Šolony)
    9. Test, Stereovision, SLAM/Stereoviděni, SLAM (15.11. Šolony)
    10. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 22.11. slajdy)
    11. Image Registration/Registrace obrazu (Čadík, 29.11. slajdy)
    12. Invariant Image Regions/Invariantní oblasti obrazu (Beran, 6.12. slajdy)
    13. Conclusions, Hardware Accelerated Algorithms/Conclusions, Hardware Accelerated Algorithms (Zemčík 13. 12.)

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

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

Project

26 hod., compulsory

Teacher / Lecturer

Ing. Kamil Behúň
Ing. Roman Juránek, Ph.D.
Ing. Jakub Špaňhel, Ph.D.
Ing. Vojtěch Bartl, 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.