Course details

Image Processing (in English)

ZPOe Acad. year 2023/2024 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 (written part)
  • 10 pts mid-term test (written 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)
Nosko Svetozár, 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)

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.
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 exploitation of "C" language.

Recommended prerequisites

Prerequisite knowledge and skills


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

Study literature

  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1
  • Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, OReilly 2008, ISBN: 978-0596516130
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
  • IEEE Multimedia, IEEE, USA - series of journals- various articles

Syllabus of lectures

  1. Introduction, representation of image, linear filtration.
  2. Image acquisition.
  3. Discrete image transforms, FFT, relationship with filtering. 
  4. Point image transforms.
  5. Edge detection, segmentation.
  6. Resampling, warping, morphing.
  7. DCT, Wavelets.
  8. Watermarks.
  9. Test + project status presentation.
  10. Image distortion, types of noise, optimal filtration.
  11. Project consultations.
  12. Project preparations.
  13. Matematical morphology, motion analysis, conclusion.

Syllabus - others, projects and individual work of students

Individually assigned project for the whole duration of the course.

Progress assessment

  • Mid-term exam - 10 points.
  • Individual project - 39 points.
  • Final exam - 51 points.


Course inclusion in study plans

Back to top