Course details

Computer Vision (in English)

POVa Acad. year 2022/2023 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.

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
  • 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

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/Úvod, základy, motivace a aplikace (Zemčík 23.9.)
  2. Basic principles of machine learning with teacher - AdaBoost/Základní principy klasifikace s učitelem - AdaBoost (Zemčík 30.9.)
  3. Hough Transform, RHT, RANSAC, Time Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování časových sekvencí (Hradiš, 7.10.)
  4. Object Detection - Trees, Random Forests/Detekce objektů - Stromy, "Random Forests" (Juránek, 14.10.)
  5. Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models / Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 21.10.)
  6. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 4.11.)
  7. Image Registration/Registrace obrazu (Čadík, 11.11.)
  8. Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 18.11.)
  9. Test, Invariant Image Regions/Invariantní oblasti obrazu (Beran, 25.11.)
  10. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 2.12.)
  11. 3D Computer Vision - Stereo/3D počítačové vidění - stereo, SLAM (9.12. Šolony)
  12. Acceleration of Processing in Computer Vision/Akcelerace výpočtů v počítačovém vidění (Zemčík, 16.12.)

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

DayTypeWeeksRoomStartEndCapacityLect.grpGroupsInfo
Mon exam 2023-01-02 E104 09:0010:50řádná
Thu exam 2023-02-02 M104 M105 16:0017:502. oprava
Fri exam 2022-11-25 E104 08:0008:30Test
Fri lecture 2., 13. of lectures E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Zemčík
Fri lecture 3., 11. of lectures E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Hradiš
Fri lecture 5., 7. of lectures E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Španěl
Fri lecture 8., 9. of lectures E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Čadík
Fri lecture 2022-09-23 E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Hradiš, Zemčík
Fri lecture 2022-10-14 E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Juránek
Fri lecture 2022-11-25 E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Beran
Fri lecture 2022-12-09 E104 08:0009:509999 1EIT 1MIT 2EIT 2MIT INTE NCPS NVIZ xx Šolony
Fri exam 2023-01-27 A112 13:0014:501. oprava

Course inclusion in study plans

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