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
(7. 2. 2019 Zemčík slides, slides, demo)
- Cancelled, will be replaced some other time: Image acquisition
(14. 2. 2019 Zemčík? slides)
- Discrete image transforms, FFT, relationship with filtering (Zemčík 21. 2. 2019 slajdy a slides)
- Point image transforms
(28. 2. 2019 Beran slides, demo.zip)
- Edge detection, segmentation
(7. 3. 2019 Beran slides, examples)
- Resampling, warping, morphing (14. 3. 2019 Zemčík slides)
- DCT, Wavelets (21. 3. 2019 Bařina slides)
- Watermarks (28. 3. 2019 Mlích slides, demo)
- Test + project status presentation (4. 4. 2019 Beran)
- Image distortion, types of noise, optimal filtration (11. 4. 2019 Španěl slides)
- no lecture - Easter (working day, project consultations possible depending on interest 18. 4. 2019)
- Project preparations (25. 4. 2019 Beran)
- Matematical morphology, motion analysis, conclusion (2.5. Španěl slides)
26 hours, compulsory
Teacher / Lecturer
Individually assigned project for the whole duration of the course.
eLearning: currently opened course