Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2020/2021

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.

Nabízen zahradničním studentům

Všech fakult

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

Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146
Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X
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
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, elective
    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, elective
    branch MBS , any year of study, winter semester, 5 credits, elective
    branch MIN , any year of study, winter semester, 5 credits, compulsory-optional
    branch MMM , any year of study, winter semester, 5 credits, elective

  • Programme MITAI Master's

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

  • 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, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  1. Introduction, motivation and applications (Zemčík 24.9. slidesslajdyhighlights)
  2. Basic principles of machine learning with teacher - AdaBoost  (Zemčík 1.10. slajdy-czslides-en)
  3. Object Detection - Trees, Random Forests (Juránek, 8.10. slajdy-en)
  4. Hough Transform, RHT, RANSAC, Time Sequence Processing (Hradiš, 15.10. slajdy1,  slajdy2slajdy2-en)
  5. Clustering, statistical methods (Španěl 22.10. slajdy)
  6. Segmentation, colour analysis, histogram analysis (Španěl 29.10. slajdy1slajdy2)
  7. Analysis and Feature Extraction from Images (Čadík 5.11. slajdy)
  8. Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 12.11. slajdy, video)
  9. Test, Invariant Image Regions (Beran, 19.11. slajdy)
  10. Image Registration (Čadík, 26.11., slajdy)
  11. 3D Computer Vision - Stereo (3.12. Najman, slajdy)
  12. 3D Computer Vision -SLAM (10.12. Šolony, slajdy)
  13. Acceleration of Processing in Computer Vision (Zemčík, 17.12., slajdy)
  14. NOTE: The topics and dates are just FYI, not guaranteed,  and will be continuously updated.

Project

26 hours, compulsory

Teacher / Lecturer

Syllabus

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