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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Digital image

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Code Completion Credits Range Language
BE4M33DZO Z,ZK 6 2p+2c
Corequisite:
Safety in Electrical Engineering for a master´s degree (BEEZM)
The course cannot be taken simultaneously with:
Digital image (AE4M33DZO)
Digital image (A4M33DZO)
Digital image (B4M33DZO)
The course is a substitute for:
Digital image (AE4M33DZO)
Digital image (A4M33DZO)
Digital image (B4M33DZO)
Lecturer:
Václav Hlaváč (guarantor), Radoslav Škoviera
Tutor:
Václav Hlaváč (guarantor), Jan Stria, Radoslav Škoviera
Supervisor:
Department of Cybernetics
Synopsis:

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.

Requirements:

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

Syllabus of lectures:

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.

Syllabus of tutorials:

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.

Study Objective:

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

Study materials:

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.

Note:
Further information:
http://cw.fel.cvut.cz/wiki/courses/be4m33dzo/start
Time-table for winter semester 2018/2019:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomKN:E-132
Škoviera R.
Stria J.

18:00–19:30
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Fri
roomKN:E-301
Hlaváč V.
Škoviera R.

09:15–10:45
(lecture parallel1)
Karlovo nám.
Šrámkova posluchárna K9
Thu
Fri
Time-table for summer semester 2018/2019:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-05-23
For updated information see http://bilakniha.cvut.cz/en/predmet4684806.html