Subject description - AD4M33DZO

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AD4M33DZO Digital image
Roles:PO, V Extent of teaching:14KP+6KC
Department:13133 Language of teaching:CS
Guarantors:  Completion:Z,ZK
Lecturers:  Credits:6
Tutors:  Semester:Z


First, the subject teaches how to process two-dimensional image as a signal without interpretation. Image acquisition, linear and nonlinear preprocessing methods and image compression will be studied. Second, image segmentation and registration methods will be taught. Studied topics will be practised on practical examples in order to obtain also practical skills.

Course outlines:

1. Digital image processing vs. computer vision. Objects in images. Digital image. Distance transform. Brightness histogram.
2. Physical foundation of images. Image acquisition from geometric and radiometric point of view.
3. Processing in the spatial domain. Convolution. Correlation. Noise filtration. Linear and nonlinear methods.
4. Fourier transform. Derivation of the sampling theorem. Frequency filtration of images. Image restauration.
5. Brightness and geometric transformations, interpolation. Registration I.
6. Edge detection. Multiscale image processing.
7. Color images and processing of color images.
8. Segmentation I.
9. Segmentation II.
10. Registration II.
11. Image compression.
12. Mathematical morphology.
13. Reserve.

Exercises outline:

1. MATLAB. Homework 1 assignment (image acquisition).
2. Constultations. Solving the homework.
3. Constultations. Solving the homework.
4. Constultations. Solving the homework.
5. Homework 1 handover. Homework 2 assignment (Fourier transformation).
6. Constultations. Solving the homework.
7. Constultations. Solving the homework.
8. Constultations. Solving the homework.
9. Homework 2 handover. Homework 3 assignment (image segmentation).
10. Constultations. Solving the homework.
11. Constultations. Solving the homework.
12. Consultations. Homework 3 handover.
13. Written test. Presentation of several best student homeworks.


1. Šonka, M., Hlaváč, V., Boyle, R.D.: Image processing, analysis and machine vision. 3. vydání, Thomson Learning, Toronto, Canada, 2007.
2. Svoboda, T., Kybic, J., Hlaváč, V.: Image processing, analysis and
machine vision. The MATLAB companion, Thomson Learning, Toronto, Canada, 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, image acquisition, Fourier transformation

Subject is included into these academic programs:

Program Branch Role Recommended semester
MKEEM1 Technological Systems V 1
MKEEM5 Economy and Management of Electrical Engineering V 1
MKEEM4 Economy and Management of Power Engineering V 1
MKEEM3 Electrical Power Engineering V 1
MKEEM2 Electrical Machines, Apparatus and Drives V 1
MKKME1 Wireless Communication V 1
MKKME5 Systems of Communication V 1
MKKME4 Networks of Electronic Communication V 1
MKKME3 Electronics V 1
MKKME2 Multimedia Technology V 1
MKOI3 Computer Vision and Image Processing PO 1
MKKYR4 Aerospace Systems V 1
MKKYR1 Robotics V 1
MKKYR3 Systems and Control V 1
MKKYR2 Sensors and Instrumentation V 1

Page updated 10.7.2020 17:51:49, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)