This page is located in archive. Go to the latest version of this course pages. Go the latest version of this page.

Schedule Upload system Discussion forum

BE4M33DZO – Digital image

Course Objective

The course teaches how to represent, process and interpret 2D image in a computer. The first part of the course will be focused on image processing taken similarly as in signal processing, i.e. without interpretation. We will explain image acquisition, linear and non-linear pre-processing and image compression. In the second part, we will teach students the segmentation and registration methods for 2D images. The gained knowledges will be applied to practical examples in exercises, so that students will gain a practical experience with the topic.

Required prior knowledge

It is assumed that students of this course have a working knowledge of mathematical analysis, linear algebra, probability theory and statistics. In addition, basic programming skills, mainly in MATLAB, are expected. This master subject should not repeat the knowledge, which was taught in the Open informatics study program in bachelor studies. The subject would be too shallow otherwise.

It could happen that some students did not study the topics, which are considered a prerequisite of the subject Autonomous robotics. They have to study or refresh their knowledge on their own. Some other knowledge/skills might be useful in the subject labs.

I offer students the aid to refresh their knowledge by providing them presentations related to the topic.

Author Presentation and the link to it
V. Hlaváč Probability and statistics, rehearsal
V. Hlaváč Least squares

Lectures: Wednesday 9:15-10:45, room KN:E-301

Lecturer: Václav Hlaváč, Radoslav Škoviera (exceptionally, when Václav Hlaváč travels or is sick).

Work load: 2 h lecture + 2 h exercises/labs + 5 h home work per week.

Slides for lectures are available in English on http://people.ciirc.cvut.cz/~hlavac/TeachPresEn/ and in Czech on http://people.ciirc.cvut.cz/~hlavac/TeachPresCz/. I usually improve the slides, when I am preparing for a particular lecture.

Week Date Topic Notes
1. 25.09.2019 Computer vision. Objects in image. Interpretation. Digital image, concepts. Brightness transforms.
2. 02.10.2019 Physical image formation and acquisition - geometric and radiometric point of view. Lab 1, Brightness trans.
3. 09.10.2019 Geometric transforms. Interpolation. Dynamic programming.
4. 16.10.2019 Spatial domain image processing. Convolution, correlation. Noise filtration. Lab 2, seam carving, dyn. programming
5. 23.10.2019 Fourier transform. Sampling theorem. Frequency filtering.
6. 30.10.2019 Image restoration. Edge detection. Scale space. Canny detector. Interest points/regions detection.. Lab 3, HDR
7. 06.11.2019 Image segmentation - Thresholding, K-means, EM algorithm.
8. 13.11.2019 Image segmentation - Mean shift, graph-based segmentation, grab-cut. Lab 4, segmentation
9. 20.11.2019 Principal component analysis. Wavelets.
10. 27.11.2019 Image and object registration. Lab 5, registration
11. 04.12.2019 Mathematical morphology for binary images.
12. 11.12.2019 Mathematical morphology for grayscale images. Lab 6, image restoration
13. 18.12.2019 Color images and their processing.
14. 08.01.2020 Image compression, video compression.


Instructors: Radoslav Škoviera (vedoucí cvičení), Dominik Fiala, Júlia Škovierová.

Details about laboratory and seminars could be found in section labs.

Examination and its evaluation

  • Only students who obtained the credit for their lab activity are eligible for the examination.
  • The examination consists of two parts, written and oral exams. The written part checks the global orientation of the student in the subject matter. Students typically answers six questions, which are randomly selected from the list of questions. Questions may be sligtly changed till the end of December. The written exam lasts 30 minutes. The written part of the exam yields 30 points at maximum.
  • The oral part of the exam follows the written part after the written part is correctd by the teacher. The oral part is a discussion of a student and the teacher about a scientific paper of student's choice. The paper has to be from a respected scientific journal, which cannot be older than five years. The paper has to have relation to the subject and be written in English. The priviledge to choose the paper gives the student the oportunity to bring the discussion to the area he has a deeper knowledge. Students come to the exam with a printed version of the paper with her/his hand written notes made while reading the paper.
  • The list of journals from which the paper can be selected: IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transaction on Medical Imaging, International Journal on Computer Vision, Medical Image Analysis. The Czech Government pays to its universities the electronic access to papers. Use https://dialog.cvut.cz/
  • Oral part of exam follows after correcting tests (written part).
  • The examination mark is given by the sum of points. Labs (max. 40 points), written part (max. 30 points) and oral exam (max. 30 points).
  • The maximal number of points is 100. Examination results: A 100-90 points, B 89-80 points, C 79-70 points, D 69-60 points, E 59-50 points, F < 50 points.


  • Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine vision, 3rd edition, Thomson Learning, Toronto, Canada, 2007. Up to 10 volumes are available in the library of the Center for Machine Perception (Dept. of Cybernetics FEE). Should you wish to borrow this book, please contact Ms Hana Pokorná.
  • Svoboda T., Kybic J., Hlaváč V.: Image Processing, Analysis and Machine Vision – A MATLAB Companion. Thomson, Toronto, Canada, 1 edition, 2007. If interested to borrow this book, please proceed as above.
  • Szeliski R.: Computer Vision: Algorithms and Application, Springer, Berlin, 2010. 812 p. The book draft is freely available for download
  • Karu Z.Z.: Signals and Systems Made Ridiculously Easy, ZiZi Press, Cambridge, MA, USA, 2001, (scan). I recommend this thin book to students who did not attend a signal processing course and also to everyone who likes to learn by reading a less formal book.
courses/be4m33dzo/start.txt · Last modified: 2019/09/14 21:03 by hlavac