Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2019/2020

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.

Supervisor

prof. Dr. Ing. Pavel Zemčík

Department

Department of Computer Graphics and Multimedia (UPGM)

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

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
Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
IEEE Multimedia, IEEE, USA - série časopisů - různé články
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

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Homeworks, Mid-term test, individual project.

Language of instruction

English

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, winter semester, 5 credits, optional
    branch MPV , any year of study, winter semester, 5 credits, compulsory-optional
    branch MGM , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme IT-MGR-2 Master's

    branch MGMe , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme IT-MGR-2 Master's

    branch MSK , any year of study, winter semester, 5 credits, optional
    branch MBS , any year of study, winter semester, 5 credits, optional
    branch MIN , any year of study, winter semester, 5 credits, compulsory-optional
    branch MMM , any year of study, winter semester, 5 credits, optional

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 5 credits, optional
    specialization NBIO , any year of study, winter semester, 5 credits, optional
    specialization NGRI , any year of study, winter semester, 5 credits, optional
    specialization NNET , any year of study, winter semester, 5 credits, optional
    specialization NVIZ , any year of study, winter semester, 5 credits, compulsory
    specialization NCPS , any year of study, winter semester, 5 credits, compulsory
    specialization NSEC , any year of study, winter semester, 5 credits, optional
    specialization NEMB , any year of study, winter semester, 5 credits, optional
    specialization NHPC , any year of study, winter semester, 5 credits, optional
    specialization NISD , any year of study, winter semester, 5 credits, optional
    specialization NIDE , any year of study, winter semester, 5 credits, optional
    specialization NISY , any year of study, winter semester, 5 credits, optional
    specialization NMAL , any year of study, winter semester, 5 credits, optional
    specialization NMAT , any year of study, winter semester, 5 credits, optional
    specialization NSEN , any year of study, winter semester, 5 credits, optional
    specialization NVER , any year of study, winter semester, 5 credits, optional
    specialization NSPE , any year of study, winter semester, 5 credits, optional

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

  • Programme IT-MGR-2 Master's

    branch MIS , 2. year of study, winter semester, 5 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. Dr. Ing. Pavel Zemčík

Syllabus

  1. Úvod, základy, motivace a aplikace/Introduction, motivation and applications (Zemčík 24.9. slajdy, slajdy, highlights)
  2. Základní principy klasifikace s učitelem - AdaBoost/Basic principles of machine learning with teacher - AdaBoost  (Zemčík 1.10. slajdy-cz, slides-en)
  3. Shlukování, statistické metody/Clustering, statistical methods (Španěl 8.10. slajdy)
  4. Segmentace, analýza barev, analýza histogramu/Segmentation, colour analysis, histogram analysis I (Španěl 15.10. slajdy1, slajdy2, slajdy3)
  5. Segmentace,  analýza barev/Segmentation, Colour Analysis, ... II (Španěl), Object Detection - Trees (Juránek, 22.10. slajdy-en)
  6. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images (Čadík 29.10. slajdy)
  7. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing (Hradiš, 5.11. slajdy1,  slajdy2, slajdy2-en)
  8. Invariantní oblasti obrazu/Invariant Image Regions (Beran, 12.11. slajdy)
  9. Test, Konvoluční neuronové sítě a tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 19.11. slajdy )
  10. Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging II (Hradiš, 26.11. slajdy )
  11. Registrace obrazu (Čadík, 3.12., slajdy)
  12. 3D počítačové vidění/3D Computer Vision (10.12. Richter FEKT slajdy)
  13. Akcelerace výpočtů v počítačovém vidění/Acceleration of Computing in Computer Vision (Zemčík, 17.12.)

    14. 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.

Projects

26 hours, compulsory

Teacher / Lecturer

prof. Dr. Ing. Pavel Zemčík

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