Course details

Computer Vision (in English)

POVa Acad. year 2021/2022 Winter semester 5 credits

Current academic year

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.

Guarantor

Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)

Course coordinator

Hradiš Michal, Ing., Ph.D. (DCGM)

Language of instruction

English

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 51 pts final exam
  • 9 pts mid-term test
  • 40 pts projects

Department

Department of Computer Graphics and Multimedia (UPGM)

Lecturer

Beran Vítězslav, doc. Ing., Ph.D. (DCGM)
Čadík Martin, doc. Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Najman Pavel, Ing. (DCGM)
Šolony Marek, Ing., Ph.D. (DCGM)
Španěl Michal, doc. Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)

Instructor

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

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.
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

  • 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
  • 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
  • Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3
  • IEEE Multimedia, IEEE, USA - série časopisů - různé články
  • Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928

Fundamental literature

  • Horn, B.K.P.: Robot Vision, McGraw-Hill, 1988, ISBN 0-07-030349-5
  • Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3 
  • Russ, J.C.: The IMAGE PROCESSING Handbook, CRC Press, 1995, ISBN 0-8493-2532-3
  • Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X

Syllabus of lectures

  1. Introduction, motivation and applications (Zemčík 24.9. slides, slides, highlights)
  2. Basic principles of supervised machine learning - AdaBoost (Zemčík 1.10. slides-cz, slides-en)
  3. Hough Transform, RHT, RANSAC (Hradiš, 8.10. slides1, slides2, slides2-en)
  4. Object Detection (Juránek, 15.10. slides-en)
  5. Clustering, statistical methods (Španěl 22.10. slides)
  6. Segmentation, colour analysis, histogram analysis (Španěl 29.10. slides, supplementary)
  7. Texture analysis, texture feature extraction (Čadík 5.11. slides)
  8. Image Registration (Čadík, 12.11., slides)
  9. Test, Invariant Image Regions (Beran, 19.11. slides)
  10. Convolutional Neural Networks (Hradiš, 26.11. slides)
  11. 3D Computer Vision - Stereo(Šolony, 3.12. slides)
  12. 3D Computer Vision - SLAM (Šolony,  3.12.  slides)
  13. Acceleration of Processing in Computer Vision (Zemčík, 17.12.)

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.

Course inclusion in study plans

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