Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2021/2022

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

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", C++, and other languages.

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

  1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zemčík 24.9.)
  2. Basic principles of machine learning with teacher - AdaBoost/Základní principy klasifikace s učitelem - AdaBoost (Zemčík 1.10.)
  3. Hough Transform, RHT, RANSAC, Time Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování časových sekvencí (Hradiš, 8.10.)
  4. Object Detection (Juránek, 15.10.)
  5. Clustering, statistical methods/Shlukování, statistické metody (Španěl 22.10.)
  6. Segmentation, colour analysis, histogram analysis/Segmentace, analýza barev, analýza histogramu (Španěl 29.10.)
  7. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 5.11.)
  8. Image Registration/Registrace obrazu (Čadík, 12.11.)
  9. Test, Invariant Image Regions/Invariantní oblasti obrazu (Beran, 19.11.)
  10. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 26.11.)
  11. 3D Computer Vision - Stereo/3D počítačové vidění - stereo (3.12. Šolony + guest/host Richter FEKT)
  12. D Computer Vision - SLAM/3D počítačové vidění - SLAM (10.12. Šolony)
  13. Acceleration of Processing in Computer Vision/Akcelerace výpočtů v počítačovém vidění (Zemčík, 17.12.)

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
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3
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
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146

Classification of course in study plans

  • Programme IT-MSC-2 Master's

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

  • Programme MITAI Master's

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

  • Programme IT-MGR-1H Master's

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

  • Programme IT-MSC-2 Master's

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

  • Programme MITAI Master's

    specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
    specialization NISY , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

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

Syllabus

  1. Introduction, motivation and applications (Zemčík 24.9. slides, slides, highlights)
  2. Basic principles of supervised machine learning - AdaBoost (Zemčík 1.10. slides-cz, slides-en)
  3. Hough Transform, RHT, RANSAC (Hradiš, 8.10. slides1, slides2, slides2-en)
  4. Object Detection (Juránek, 15.10. slides-en)
  5. Clustering, statistical methods (Španěl 22.10. slides)
  6. Segmentation, colour analysis, histogram analysis (Španěl 29.10. slides, supplementary)
  7. Texture analysis, texture feature extraction (Čadík 5.11. slides)
  8. Image Registration (Čadík, 12.11., slides)
  9. Test, Invariant Image Regions (Beran, 19.11. slides)
  10. Convolutional Neural Networks (Hradiš, 26.11. slides)
  11. 3D Computer Vision - Stereo(Šolony, 3.12. slides)
  12. 3D Computer Vision - SLAM (Šolony,  3.12.  slides)
  13. Acceleration of Processing in Computer Vision (Zemčík, 17.12.)

Project

26 hod., compulsory

Teacher / Lecturer

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

Syllabus

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