Faculty of Information Technology, BUT

Course details

Computer Vision (in English)

POVa Acad. year 2019/2020 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

Bartl Vojtěch, Ing. (DCGM FIT BUT)
Behúň Kamil, Ing. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT)
Sochor Jakub, Ing. (DCGM FIT BUT)
Špaňhel Jakub, Ing. (DCGM FIT BUT)

News


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.

Why is the course taught

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

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

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.

Progress assessment

Homeworks, Mid-term test, individual project.

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Tuelecturelectures A112 13:0014:50 1EIT 1MIT 2EIT 2MIT INTE xx

Course inclusion in study plans

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