Course detail
Image Processing (in English)
FIT-ZPOeAcad. year: 2021/2022
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
Language of instruction
Number of ECTS credits
Mode of study
Offered to foreign students
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 programming.
Prerequisites
Programming language C, basic knowledge of computer graphics, mathematical
analysis and linear algebra.
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
- Introduction, representation of image
- Linear filtration
- Image acquisition
- Discrete image transforms, FFT, relationship with filtering
- Point image transforms
- Edge detection, segmentation
- Resampling, warping, morphing
- DCT, Wavelets
- Watermarks
- Image distortion, types of noise
- Optimal filtration
- Mathematical Morphology
- Motion analysis, conclusion
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Mid-term test, project (homeworks and individual project).
Recommended optional programme components
Prerequisites and corequisites
- recommended prerequisite
Computer Graphics
Basic literature
Recommended reading
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
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Stream: https://youtube.com/playlist?list=PL_eb8wrKJwYtQRrRioYZG4hMTBK1Nx_gF
- Introduction, representation of image, linear filtration (10. 2. Zemčík slides, slides, slides, demo)
- Image acquisition (17. 2. Zemčík slides)
- Point image transforms (24. 2. Beran slides, demo.zip)
- Discrete image transforms, FFT, relationship with filtering (3. 3. Zemčík slajdy a slides)
- Lecture cancelled (10. 3.)
- Image distortion, types of noise, optimal filtration (17. 3. Španěl slides)
- Edge detection, segmentation (24. 3. Beran slides, examples)
- Resampling, warping, morphing (31. 3. Zemčík slides)
- Test, Project status presentation, mathematical morphology (7. 4. Beran slides)
- DCT, Wavelets (14. 4. Bařina slides)
- Watermarks (21. 4. Zemčík slides, demo)
- Motion analysis (28. 4. Beran)
- Conclusion (5. 5. Zemčík/Beran slides)
Project
Teacher / Lecturer
Syllabus