Course details

Computer Vision (in English)

POVa Acad. year 2025/2026 Winter semester 5 credits

Current academic year

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.

Guarantor

Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)

Course coordinator

Hradiš Michal, Ing., Ph.D. (DCGM)

Language of instruction

English

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 51 pts final exam (26 pts written part, 25 pts test part)
  • 9 pts mid-term test (3 pts written part, 6 pts test part)
  • 40 pts projects

Department

Department of Computer Graphics and Multimedia (UPGM)

Lecturer

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

Instructor

Bartl Vojtěch, Ing., Ph.D. (DCGM)
Behúň Kamil, Ing. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Juránek Roman, Ing., Ph.D. (DCGM)
Špaňhel Jakub, Ing., Ph.D. (DCGM)

Learning objectives

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 literature

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

Fundamental 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

Syllabus of lectures

    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.

Syllabus - others, projects and individual work of students

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

Progress assessment

Homeworks, Mid-term test, individual project.

Course inclusion in study plans

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