Title:

Computer Vision (in English)

Code:POVa
Ac.Year:2019/2020
Sem:Winter
Curriculums:
ProgrammeField/
Specialization
YearDuty
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
MITAINADE-Elective
MITAINBIO-Elective
MITAINCPS-Compulsory
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC-Elective
MITAINIDE-Elective
MITAINISD-Elective
MITAINISY-Elective
MITAINMAL-Elective
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Compulsory
Language of Instruction:English
News:
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: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.Groups
TuelecturelecturesA112 13:0014:501EIT 1MIT 2EIT 2MIT INTE xx
 
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
  2. Klasifikace s učitelem a detekce - AdaBoost/Classification with Teacher and Detection - AdaBoost  
  3. Shlukování, statistické metody/Clustering, Statistical Methods 
  4. Segmentace, analýza barev, analýza histogramu/Segmentation, Colour Analysis, Histogram Analysis
  5. Detekce objektů - náhodné stromy/Object Detection - Random Trees
  6. Analýza a extrakce příznaků z textur/Analysis and Feature Extraction from Images
  7. Hough transform, RHT, RANSAC, zpracování časových sekvencí/Time Sequence Processing
  8. Invariantní oblasti obrazu/Invariant Image Regions
  9. Konvoluční neuronové sítě a automatické tagování obrazu I/Convolutional Neural Networks and Automatic Image Tagging I
  10. Konvoluční neuronové sítě a autmoatické tagování obrazu II/Convolutional Neural Networks and Automatic Image Tagging II 
  11. Registrace obrazu/Image Registration
  12. 3D strojové vidění/3D Machine Vision
  13. Akcelerace zpracování obrazu a vidění, závěr/Acceleration of Image Processing and Vision, Conclusions
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:
 
  • Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X
  • Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • IEEE Multimedia, IEEE, USA - série časopisů - různé články
  • Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Study literature:
 
  • Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X
  • Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146
  • Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Progress assessment:
  Homeworks, Mid-term test, individual project.
 

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