Course details

Image Processing (in English)

ZPOe 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

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, 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


Programming language C, basic knowledge of computer graphics, mathematical
analysis and linear algebra.

Study 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_eb8wrKJwYtQRrRioYZG4hMTBK1Nx_gF

  1. Introduction, representation of image, linear filtration (10. 2. Zemčík slides, slides, slides, demo)
  2. Image acquisition (17. 2. Zemčík slides)
  3. Point image transforms (24. 2. Beran slides, demo.zip)
  4. Discrete image transforms, FFT, relationship with filtering (3. 3. Zemčík slajdy a slides)
  5. Lecture cancelled (10. 3.)
  6. Image distortion, types of noise, optimal filtration (17. 3. Španěl slides)
  7. Edge detection, segmentation (24. 3. Beran slides, examples)
  8. Resampling, warping, morphing (31. 3. Zemčík slides)
  9. Test, Project status presentation, mathematical morphology (7. 4. Beran slides)
  10. DCT, Wavelets (14. 4. Bařina slides)
  11. Watermarks (21. 4. Zemčík slides, demo)
  12. Motion analysis (28. 4. Beran)
  13. Conclusion (5. 5. Zemčík/Beran slides)

Syllabus - others, projects and individual work of students

Individually assigned project for the whole duration of the course.

Progress assessment

Mid-term test, individual project.

Controlled instruction

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

Course inclusion in study plans

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