Title:

Computer Vision (in English)

Code:POVa
Ac.Year:2018/2019
Sem:Winter
Curriculums:
ProgrammeFieldYearDuty
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group G
IT-MSC-2MGMe-Compulsory-Elective - group G
IT-MSC-2MIN-Compulsory-Elective - group I
IT-MSC-2MIS2ndElective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-Elective - group G
IT-MSC-2MSK-Elective
Language of Instruction:English
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.
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600026
 ExamsTestsExercisesLaboratoriesOther
Points:5190040
Guarantor:Zemčík Pavel, prof. Dr. Ing. (DCGM)
Lecturer:Beran Vítězslav, Ing., Ph.D. (DCGM)
Čadík Martin, doc. Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Španěl Michal, Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing. (DCGM)
Instructor:Hradiš Michal, Ing., Ph.D. (DCGM)
Juránek Roman, Ing., Ph.D. (DCGM)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
Tueexam - 1. oprava2019-01-22E10515:0016:501MIT
Tueexam - 1. oprava2019-01-22E10515:0016:502MIT
Tueexam - 1. oprava2019-01-22E10515:0016:501EIT
Tueexam - řádná2019-01-08E10416:0017:501MIT
Tueexam - řádná2019-01-08E10416:0017:502MIT
Tueexam - řádná2019-01-08E10416:0017:501EIT
Thuexam - 2. oprava2019-01-31A11212:0013:501MIT
Thuexam - 2. oprava2019-01-31A11212:0013:502MIT
Thuexam - 2. oprava2019-01-31A11212:0013:501EIT
ThulecturelecturesE10514:0015:501MITxxxx
ThulecturelecturesE10514:0015:502MITxxxx
ThulecturelecturesE10514:0015:501EITxxxx
ThulecturelecturesE10514:0015:502EITxxxx
ThulecturelecturesE10514:0015:50INTE
 
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.
Description:
  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.
Subject specific learning outcomes and competencies:
  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 competencies:
  The students will improve their teamwork skills, mathematics, and exploitation of the "C" language.
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. 3D Vision/3D Vidění (29.11. Richter FEKT slajdy)
  12. Registrace obrazu (Čadík, 6.12., 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.
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
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
Progress assessment:
  Homeworks, Mid-term test, individual project.
 

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