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

Course coordinator

Language of instruction

English

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 pts exam, 9 pts mid-term test, 40 pts projects

Department

Lecturer

Instructor

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.

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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