Course details

Image Processing

ZPO Acad. year 2021/2022 Summer semester 5 credits

Current academic year

Introduction to image processing, image acquiring, point and discrete image transforms, linear image filtering, image distortions, types of noise, optimal image filtering, non-linear image filtering, watermarks, edge detection, segmentation, motion analysis, loseless and lossy image compression

Guarantor

Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)

Course coordinator

Beran Vítězslav, doc. Ing., Ph.D. (DCGM)

Language of instruction

Czech, English

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 51 pts final exam (45 pts written part, 6 pts test part)
  • 10 pts mid-term test (8 pts written part, 2 pts test part)
  • 39 pts projects

Department

Department of Computer Graphics and Multimedia (UPGM)

Lecturer

Bařina David, Ing., Ph.D. (DCGM)
Beran Vítězslav, doc. Ing., Ph.D. (DCGM)
Španěl Michal, doc. Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)

Instructor

Beran Vítězslav, doc. Ing., Ph.D. (DCGM)

Subject specific learning outcomes and competences

The students will get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). They will learn how to apply such knowledge on real examples of image processing tasks. They will also get acquainted with "higher" imaging algorithms. Finally, they will learn how to practically program image processing applications through projects.
Students will improve their teamwork skills and in programming.

Learning objectives

To get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). To learn how to apply such knowledge on real examples of image processing tasks. To get acquainted with "higher" imaging algorithms. To learn kow to practically program image processing applications through projects.

Recommended prerequisites

Prerequisite knowledge and skills

The C programming language and fundamentals of computer graphics.

Study literature

  • Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
  • IEEE Multimedia, IEEE, USA - série časopisů - různé články
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1
  • Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, OReilly 2008, ISBN: 978-0596516130

Fundamental literature

  • Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1

Syllabus of lectures

Stream: 

https://youtube.com/playlist?list=PL_eb8wrKJwYsqP7ZDP-psxSzFbbMwKDwm

  1. Introduction, representation of image, linear filtration (11. 2. Zemčík slides, slides, slides, demo)
  2. Image acquisition (18. 2. Zemčík slides)
  3. Point image transforms (25. 2. Beran slides, demo.zip)
  4. Discrete image transforms, FFT, relationship with filtering (4. 3. Zemčík slajdy a slides)
  5. DCT, Wavelets (11. 3. Bařina slides)
  6. Image distortion, types of noise, optimal filtration (18. 3. Španěl slides)
  7. Edge detection, segmentation (25. 3. Beran slides, examples)
  8. Resampling, warping, morphing (1. 4. Zemčík slides)
  9. Test, Project status presentation, mathematical morphology (8. 4. Beran slides)
  10. Good Friday - lecture cancelled (15. 4.)
  11. Watermarks (22. 4. Zemčík slides, demo)
  12. Motion analysis (29. 4. Beran + industry guest)
  13. Conclusion (6. 5. Zemčík/Beran slides)

Syllabus - others, projects and individual work of students

  1. Individually assigned project for the whole duration of the course.

Progress assessment

Mid-term test, project (homeworks and individual project).

Controlled instruction

Mid-term test, project (homeworks and individual project).

Course inclusion in study plans

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