Course details

Computer Vision (in English)

POVa Acad. year 2021/2022 Winter semester 5 credits

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.

News

Dear students,

I would like to welcome you in the scholl year 2021/2022. The teaching starts on Friday 24.9.2021 in room G202 and I do look forward to see you there. In the breakdown of the terms of the course, you will find all the evaluated activities and please, mind that the tmíming of some of them is not yet clear so if you see the date 31.12.2021, it means "not yet clear".

Best regards

Guarantor

Deputy Guarantor

Hradiš Michal, Ing. (FIT BUT)

Language of instruction

English

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 exam, 9 mid-term test, 40 projects

Department

Lecturer

Instructor

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

Subject specific learning outcomes and competences

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.

Generic learning outcomes and competences

The students will improve their teamwork skills, mathematics, and exploitation of the "C", C++, and other languages.

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.

Why is the course taught

The goal of the course eduation is to invoke interest among students about the principles, tasks, and methods of computer vision. The scope of the course cannot reach the broad area of computer vision but brings at least "bits"of it and shows also results and direction of research at FIT.

Study literature

  • 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: 978-9386858146
  • Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928

Fundamental literature

  • 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

Syllabus of lectures


  1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zemčík 24.9. slidesslajdyhighlights)
  2. Basic principles of machine learning with teacher - AdaBoost/Základní principy klasifikace s učitelem - AdaBoost (Zemčík 1.10. slajdy-czslides-en)
  3. Object Detection - Trees, Random Forests/Detekce objektů - Stromy, "Random Forests" (Juránek, 8.10. slajdy-en)
  4. Hough Transform, RHT, RANSAC, Time Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování časových sekvencí (Hradiš, 15.10. slajdy1,  slajdy2slajdy2-en)
  5. Clustering, statistical methods/Shlukování, statistické metody (Španěl 22.10. slajdy)
  6. Segmentation, colour analysis, histogram analysis/Segmentace, analýza barev, analýza histogramu (Španěl 29.10. slajdy1slajdy2slajdy3)
  7. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 5.11. slajdy)
  8. Image Registration/Registrace obrazu (Čadík, 12.11., slajdy)
  9. Test, Invariant Image Regions/Invariantní oblasti obrazu (Beran, 19.11. slajdy)
  10. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 26.11. slajdy )
  11. 3D Computer Vision - Stereo/3D počítačové vidění - stereo (3.12. Šolony + guest/host Richter FEKT slajdy)
  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.)

    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.

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Frilecturelectures G202 08:0009:50 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx

Course inclusion in study plans

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