Faculty of Information Technology, BUT

Course details

Computer Vision (in English)

POVa Acad. year 2018/2019 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

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

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 24.12.2018, it means "not defined yet".

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

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" language.

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.

Study literature

  • 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

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. Úvod, základy, motivace a aplikace/Introduction, motivation and applications (Hradiš 20.9. slajdy, slajdy, highlights)
  2. Základní principy klasifikace s učitelem - AdaBoost/Basic principles of machine learning with teacher - AdaBoost  (Zemčík 27.9. slajdy-cz, slides-en)
  3. Shlukování, statistické metody/Clustering, statistical methods (Španěl 4.10. slajdy)
  4. Segmentace, analýza barev, analýza histogramu/Segmentation, colour analysis, histogram analysis (Španěl 11.10. slajdy1, slajdy2, slajdy3)
  5. Segmentace,  analýza barev/Segmentation, Colour Analysis, ... finishing (Španěl), Object Detection - Trees (Juránek, 18.10. slajdy-en)
  6. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images (Čadík 25.10. slajdy)
  7. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing (Hradiš, 1.11. slajdy1slajdy2, slajdy2-en)
  8. Invariantní Oblasi Obrazu/Invariant Image Regions (Beran, 8.11. slajdy)
  9. Test, Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 15.11. slajdy )
  10. Konvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging II (Hradiš, 22.11. slajdy )
  11. Registrace obrazu (Čadík, 29.11., slajdy)
  12. 3D Vision/3D Vidění (6.12. Richter FEKT slajdy)
  13. Akcelerace zpracování obrazu, závěr (Zemčík, 13.12.)

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
Monlecture2019-01-07 M103 15:0017:50Project presentations
Tueexam2019-01-22 E105 15:0016:50 1EIT 1MIT 2MIT 1. oprava
Tueexam2019-01-08 E104 16:0017:50 1EIT 1MIT 2MIT řádná
Wedlecture2019-01-09 M103 15:0017:50Project presentations
Thuexam2019-01-31 A112 12:0013:50 1EIT 1MIT 2MIT 2. oprava
Thulecturelectures E105 14:0015:50 1EIT 1MIT 2EIT 2MIT INTE xx

Course inclusion in study plans

  • Programme IT-MSC-2, field MBI, MBS, MMM, MSK, any year of study, Elective
  • Programme IT-MSC-2, field MGM, MGMe, MPV, any year of study, Compulsory-Elective group G
  • Programme IT-MSC-2, field MIN, any year of study, Compulsory-Elective group I
  • Programme IT-MSC-2, field MIS, 2nd year of study, Elective
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