Course details

Computer Vision (in English)

POVa Acad. year 2020/2021 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,

terms and their evaluation (points) are just indicative and at this moment, the exact timing is not available in general. If you see 24.12.2020, it means "time not set yet".



Best regards



Pavel Zemčík

Guarantor

Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT)

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 half-term test, 40 projects

Department

Department of Computer Graphics and Multimedia (DCGM FIT BUT)

Lecturer

Beran Vítězslav, Ing., Ph.D. (DCGM FIT BUT)
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
Najman Pavel, Ing. (DCGM FIT BUT)
Šolony Marek, Ing., PhD. (DCGM FIT BUT)
Španěl Michal, Ing., Ph.D. (DCGM FIT BUT)
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT)

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 (Zemčík 24.9. slides, slajdy, highlights)
  2. Basic principles of machine learning with teacher - AdaBoost  (Zemčík 1.10. slajdy-cz, slides-en)
  3. Object Detection - Trees, Random Forests (Juránek, 8.10. slajdy-en)
  4. Hough Transform, RHT, RANSAC, Time Sequence Processing (Hradiš, 15.10. slajdy1,  slajdy2, slajdy2-en)
  5. Clustering, statistical methods (Španěl 22.10. slajdy)
  6. Segmentation, colour analysis, histogram analysis (Španěl 29.10. slajdy1, slajdy2, slajdy3)
  7. Analysis and Feature Extraction from Images (Čadík 5.11. slajdy)
  8. Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 12.11. slajdy)
  9. Test, Invariant Image Regions (Beran, 19.11. slajdy)
  10. Image Registration (Čadík, 26.11., slajdy)
  11. 3D Computer Vision - Stereo (3.12. Najman + guest Richter FEKT slajdy)
  12. 3D Computer Vision -SLAM (10.12. Šolony)
  13. Acceleration of Processing in Computer Vision (Zemčík, 17.12.)

    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
Thulecturelectures E105 13:0014:50 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx
Thulecture3., 4. of lectures E105v 13:0014:50TM, MST

Course inclusion in study plans

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