Subject description - B4M33DZO

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B4M33DZO Digital image
Roles:PO, P, PV Extent of teaching:2P+2C
Department:13133 Language of teaching:CS
Guarantors:Hlaváč V. Completion:Z,ZK
Lecturers:Hlaváč V., Škoviera R. Credits:6
Tutors:Fiala D., Hlaváč V., Škoviera R., Škovierová J. Semester:Z


The subject teaches how to represent the two-dimensional image in a computer, how to process it and interpret it. The first part of the subject deals with the image as with the signal without interpretation. Image acquisition, linear and nonlinear preprocessing methods and image compression will be explicated. In the second part, image segmentation and registration methods will be taught. Studied topics will be practiced on practical examples in order to obtain also practical skills.

Study targets:

The subject teaches how to represent the two-dimensional image in a computer, how to process it and interpret it.

Course outlines:

1. Digital image processing vs. computer vision. Role of interpretation. Objects in images. Digital image. Concepts.
2. Physical foundation of images. Image acquisition from geometric and radiometric point of view.
3. Brightness and geometric transformations, interpolation.
4. Fourier transform. Derivation of the sampling theorem. Frequency filtration of images. Image restauration.
5. Processing in the spatial domain. Convolution. Correlation. Noise filtration. Homomorphic filtration.
6. Edge detection. Multiscale image processing. Canny detector.
7. Principal component analysis. Wavelets transformation.
8. Color images and processing of color images.
9. Image compression. Video compression.
10. Mathematical morphology.
11. Image segmentation - thresholding, k-means, EM algorithm.
12. Image segmentation - mean shift, seek for the optimal graph cut.
13. Registration of images and of objects in images.

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.: Image Processing, Analysis and Machine vision, 4th edition, Thomson Learning, Toronto, Canada, 2015, 912p., ISBN-10: 1133593607.
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, image interpretation, image segmentation, image registration

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPOI8_2018 Bioinformatics PO 3
MPEK8_2021 Communication and information processing PV 3
BPBIO_2018 Common courses P 5
MPOI5_2018 Computer Vision and Image Processing PO 1
MPOI8_2016 Bioinformatics PO 3
MPOI5_2016 Computer Vision and Image Processing PO 1

Page updated 9.4.2021 19:52:33, semester: Z/2020-1, L/2021-2, L/2020-1, Z,L/2022-3, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)