Title:

Computer Vision

Code:POVa
Ac.Year:2017/2018
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MGR-1HMGH-Recommended
IT-MSC-2MBI-Elective
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group G
IT-MSC-2MIN-Compulsory-Elective - group I
IT-MSC-2MIS2ndElective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-Elective - group G
IT-MSC-2MSK-Elective
Language: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 24.12.2017, 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/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600026
 ExaminationTestsExercisesLaboratoriesOther
Points:5190040
Guarantee: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:Bartl Vojtěch, Ing., DCGM
Behúň Kamil, Ing., DCGM
Hradiš Michal, Ing., Ph.D., DCGM
Juránek Roman, Ing., Ph.D., DCGM
Sochor Jakub, Ing., DCGM
Špaňhel Jakub, Ing., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
Monexam - 2. oprava2018-01-29E10412:0013:501MIT
Monexam - 2. oprava2018-01-29E10412:0013:502MIT
Monexam - 2. oprava2018-01-29E10412:0013:50INTE
Tueexam - řádná2018-01-02A11213:0014:501MIT
Tueexam - řádná2018-01-02A11213:0014:502MIT
Tueexam - řádná2018-01-02A11213:0014:50INTE
Tueexam - 1. oprava2018-01-16E10414:0015:501MIT
Tueexam - 1. oprava2018-01-16E10414:0015:502MIT
Tueexam - 1. oprava2018-01-16E10414:0015:50INTE
ThuKonzultace projektů2017-11-30L22013:0015:50
ThulecturelecturesD020716:0017:501MITxxxx
ThulecturelecturesD020716:0017:502MITxxxx
ThulecturelecturesD020716:0017:50INTE
Thulecture - Náhradní rozvrh kvůli DOD2017-11-02A11316:0017:501MITxxxx
Thulecture - Náhradní rozvrh kvůli DOD2017-11-02A11316:0017:502MITxxxx
 
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 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.
Syllabus of lectures:
 
  1. Úvod, základy, motivace a aplikace/Introduction, motivation and applications (Zemčík 18.9. slajdyslajdyhighlights)
  2. 28.9. přednáška není/no lecture :-(
  3. Základní principy klasifikace s učitelem - AdaBoost/Basic principles of machine learning with teacher - AdaBoost  (Zemčík 5.10. slajdy-czslajdy-en)
  4. Shlukování, statistické metody/Clustering, statistical methods (Španěl 12.10. slajdy)
  5. Segmentace, analýza barev, analýza histogramu/Segmentation, colour analysis, histogram analysis (Španěl 19.10. slajdy1slajdy2slajdy3)
  6. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images (Čadík 26.10. slajdy)
  7. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing (Hradiš, 2.11. slajdy1,  slajdy2slajdy2-en)
  8. Segmentace,  analýza barev/Segmentation, Colour Analysis, ... finishing (Španěl), Object Detection - Trees (Juránek, 9.11. slajdy1slajdy2)
  9. Test, Invariantní Oblasi Obrazu/Invariant Image Regions (Beran, 16.11. slajdy)
  10. KOnvoluční neuronové sítě a Tagování obrazu/Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 23.11. slajdy )
  11. 3D Vision/3D Vidění (30.11. ??? slajdy)
  12. Registrace obrazu (Čadík, 7.12., slajdy)
  13. Akcelerace zpracování obrazu, závěr (Zemčík???, 14.12.)
  14. 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 (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.