Image Processing (in English)
FIT-ZPOeAcad. year: 2019/2020
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
Learning outcomes of the course unit
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.
Programming language C, basic knowledge of computer graphics, mathematical
analysis and linear algebra.
- recommended prerequisite
Recommended optional programme components
Recommended or required reading
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
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Mid-term test, individual project.
Language of instruction
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.
Type of course unit
26 hours, optionally
Teacher / Lecturer
- Introduction, representation of image, linear filtration (6. 2. 2020 Beran slides, slides, slides, demo)
- Point image transforms (13. 2. 2020 Beran slides, demo.zip)
- Image acquisition (20. 2. 2020 Zemčík slides) - will be "moved to another date 18.2."
- Discrete image transforms, FFT, relationship with filtering (Zemčík 27. 2. 2020 slajdy a slides)
- Image distortion, types of noise, optimal filtration (5. 3. 2020 Španěl slides)
- Edge detection, segmentation (12. 3. 2020 Beran slides, examples)
- DCT, Wavelets (19. 3. 2020 Bařina slides)
- Watermarks (26. 3. 2020 Zemčík slides, demo)
- Test, project status presentation, mathematical morphology (2. 4. 2020 Beran slides)
- Green Thursday - lecture cancelled (9. 4. 2020)
- Resampling, warping, morphing (16. 4. 2020 Beran slides)
- Lecture from industry, motion analysis, conclusion (23. 4. 2020 Beran slides)
26 hours, compulsory
Teacher / Lecturer
Individually assigned project for the whole duration of the course.
eLearning: currently opened course