Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2023/2024

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)
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146 (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)
(EN)

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, elective
    branch MPV , any year of study, winter semester, compulsory-optional
    branch MGM , any year of study, winter semester, compulsory-optional

  • Programme IT-MGR-2 Master's

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

  • Programme IT-MGR-2 Master's

    branch MSK , any year of study, winter semester, elective

  • Programme IT-MGR-1H Master's

    specialization MGH , any year of study, winter semester, recommended

  • Programme IT-MGR-2 Master's

    branch MBS , any year of study, winter semester, elective
    branch MIN , any year of study, winter semester, compulsory-optional
    branch MMM , any year of study, winter semester, elective

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, elective
    specialization NBIO , any year of study, winter semester, elective
    specialization NGRI , any year of study, winter semester, elective
    specialization NNET , any year of study, winter semester, elective
    specialization NVIZ , any year of study, winter semester, compulsory
    specialization NCPS , any year of study, winter semester, compulsory
    specialization NSEC , any year of study, winter semester, elective
    specialization NEMB do 2021/22 , any year of study, winter semester, elective
    specialization NEMB , any year of study, winter semester, elective
    specialization NHPC , any year of study, winter semester, elective
    specialization NISD , any year of study, winter semester, elective
    specialization NIDE , any year of study, winter semester, elective
    specialization NISY do 2020/21 , any year of study, winter semester, elective
    specialization NISY , any year of study, winter semester, elective
    specialization NMAL , any year of study, winter semester, elective
    specialization NMAT , any year of study, winter semester, elective
    specialization NSEN , any year of study, winter semester, elective
    specialization NVER , any year of study, winter semester, elective
    specialization NSPE , any year of study, winter semester, elective

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

  • Programme IT-MGR-2 Master's

    branch MIS , 2. year of study, winter semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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

Syllabus

  1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zemčík 22.9.)
  2. Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models/Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 29.9.)
  3. Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 6.10. slajdy1, slajdy2, slajdy3)
  4. Scanning object detection, boosted classifiers, acceleration/Detekce objektů oknem, boostované klasifikátory, akcelerace (Zemčík 13.10.)
  5. Object Detection - Trees, Random Forests, Yolo?/Detekce objektů - Stromy, "Random Forests",Yolo? (Juránek, 20.10. slajdy-en)
  6. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 27.10. slajdy)
  7. Hough Transform, RHT, RANSAC, Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování sekvencí (Hradiš, 3.11. slajdy1, slajdy2, slajdy2-en)
  8. 3D Computer Vision/3D počítačové vidění (10.11. Šolony)
  9. xxx International Students Day 17.11. xxx
  10. Test, Stereovision, SLAM/Stereoviděni, SLAM (24.11. Šolony)
  11. Invariant Image Regions/Invariantní oblasti obrazu (Beran, 1.12. slajdy)
  12. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 8.12. slajdy)
  13. Image Registration/Registrace obrazu (Čadík, 15.12. slajdy)

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 hours, compulsory

Teacher / Lecturer

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

Syllabus

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

eLearning

eLearning: currently opened course