Course details

Computer Vision (in English)

POVa Acad. year 2019/2020 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,

hello, currently you can see all the terms in which you can achieve "points" in the course. The timing of the terms, however, will be updated after we discuss them with you and if you see date 24.12.2019, it means "not defined yet".

Pavel Zemčík

This course is instructed in English, and it is intended for incoming Erasmus+ students, too.

Guarantor

Language of instruction

English

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 exam, 9 half-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. Úvod, základy, motivace a aplikace/Introduction, motivation and applications (Zemčík 24.9. slidesslajdyhighlights)
  2. Základní principy klasifikace s učitelem - AdaBoost/Basic principles of machine learning with teacher - AdaBoost  (Zemčík 1.10. slajdy-czslides-en)
  3. Detekce objektů - stromy/Object Detection - Trees, Random Forests (Juránek, 8.10. slajdy-en)
  4. Shlukování, statistické metody/Clustering, statistical methods (Španěl 15.10. slajdy)
  5. Segmentace, analýza barev, analýza histogramu/Segmentation, colour analysis, histogram analysis (Španěl 22.10. slajdysupp. material)
  6. Object Detection - Trees, Random Forests (Juránek, 8.10. slajdy-en)
  7. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images (Čadík 29.10. slajdy)
  8. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing (Hradiš, 5.11. slajdy1,  slajdy2slajdy2-en)
  9. Invariantní oblasti obrazu/Invariant Image Regions (Beran, 12.11. slajdy)
  10. Test, Konvoluční neuronové sítě a tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 19.11. slajdy )
  11. Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging II (Hradiš, 26.11. slajdy )
  12. Registrace obrazu (Čadík, 3.12., slajdy)
  13. ZRUŠENO/CANCELLED: 3D počítačové vidění/3D Computer Vision (10.12. Richter FEKT slajdy. older Czech slides I, older Czech slides II))
  14. Akcelerace výpočtů v počítačovém vidění/Acceleration of Computing in Computer Vision (Zemčík, 17.12.)
  15. 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.

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
Tueexam2020-01-07 E104 13:0014:50 1EIT 1MIT 2EIT 2MIT INTE 1st term
Tueexam2020-01-28 E105 13:0014:50 1EIT 1MIT 2EIT 2MIT INTE 3rd term
Tuelecturelectures A112 13:0014:50 1EIT 1MIT 2EIT 2MIT INTE xx
Thuexam2020-01-16 E104 15:0016:50 1EIT 1MIT 2EIT 2MIT INTE 2nd term

Course inclusion in study plans

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