Subject description - B4M33DZO

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B4M33DZO Digital image
Roles:PV, PO, P Extent of teaching:2P+2C
Department:13133 Language of teaching:CS
Guarantors:Sýkora D. Completion:Z,ZK
Lecturers:Drbohlav O., Sýkora D. Credits:6
Tutors:Čech J., Drbohlav O., Pánek V., Sýkora D., Škoviera R. Semester:Z,L

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This course presents an overview of basic methods for digital image processing. It deals with practical techniques that have an interesting theoretical basis but are not difficult to implement. Seemingly abstract concepts from mathematical analysis, probability theory, or optimization come to life through visually engaging applications. The course focuses on fundamental principles (signal sampling and reconstruction, monadic operations, histogram, Fourier transform, convolution, linear and non-linear filtering) and more advanced editing techniques, including image stitching, deformation, registration, and segmentation. Students will practice the selected topics through six implementation tasks, which will help them learn the theoretical knowledge from the lectures and use it to solve practical problems

Course outlines:

1. Monadic Operations
2. Fourier Transform
3. Convolution
4. Linear Filtering
5. Non-linear Filtering
6. Image Editing
7. Image Deformation 1
8. Image Deformation 2
9. Image Registration 1
10. Image Registration 2
11. Image Registration 3
12. Image Segmentation 1
13. Image Segmentation 2
14. Reserved

Exercises outline:

1. Introduction to Matlab
2. Monadic Operations 1
3. Monadic Operations 2
4. Fourier Transform 1
5. Fourier Transform 2
6. Linear and Non-linear Filtering 1
7. Linear and Non-linear Filtering 2
8. Image Editing 1
9. Image Editing 2
10. Image Registration 1
11. Image Registration 2
12. Image Segmentation 1
13. Image Segmentation 2
14. Credits


1. Gonzalez R. C., Woods R. E.: Digital Image Processing (3rd Edition), Prentice Hall, 2008.
2. Goshtasby A. A.: Image Registration: Principles, Tools and Methods, Springer, 2012.
3. He J., Kim C.-S., Kuo C.-C. J.: Interactive Segmentation Techniques: Algorithms and Performance Evaluation, Springer, 2014.
4. Paris S., Kornprobst P., Tumblin J., Durand F.: Bilateral Filtering: Theory and Applications, Now Publishers, 2009.
5. Pratt W.: Digital Image Processing (3rd Edition), John Wiley, 2004.
6. Radke R. J.: Computer Vision for Visual Effects, Cambridge University Press, 2012.
7. Svoboda, T., Kybic, J., Hlaváč, V.: Image Processing, Analysis and Machine Vision. The MATLAB companion, Thomson Learning, Toronto, Canada, 2007.
8. Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine vision (3rd Edition), Thomson Learning, 2007.


It is expected that the student is familiar with calculus, linear algebra, probability and statistics to the depth taught at FEL CVUT.



digital image processing, Fourier transformation, image editing, image deformation, image segmentation, image registration

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPOI5_2018 Computer Vision and Image Processing PO 1
MPOI8_2018 Bioinformatics PO 3
BPBIO_2018 Common courses P 4
MPEK8_2021 Communication and information processing PV 3

Page updated 30.5.2024 15:51:26, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)