Signals and Images Processing

The course is a substitute for:

Signal and Image Processing (383ZS)

Lecturer:

Miloš Sedláček (gar.), Václav Hlaváč

Tutor:

Miloš Sedláček (gar.), Václav Hlaváč, Tomáš Svoboda

Supervisor:

Department of Measurement

Synopsis:

A more detailed information about signal part and evaluation of exam can be found on http://measure.feld.cvut.cz/groups/edu/english/XE383ZS/index.html.

The course consists of two parts, which guarantee Department of Measurement and Department of Cybernetics. Students will gain basic knowledge of digital signal processing in the first part (sampling and reconstruction of signals, DFT and FFT, digital filters and processing of stochastic signals). The second part is devoted to images as a practically important example of 2D signals. Image formation and linear and nonlinear methods of image pre-processing and compression are explained. Emphasis is laid on practical applications.

Requirements:

Syllabus of lectures:

1. Sampling theorem and signal reconstruction.

2. Fourier transform of discrete signals.

3. Fast Fourier transform and spectrum analysis.

4. Digital filters - types, properties.

5. Digital filter design.

6. Stochastic signals - amplitude distribution and correlation functions.

7. Stochastic signals - power spectral density.

8. Introduction to images. Digital image and its properties.

9. Formation of an image, geometrical and radiometrical view.

10. Geometrical, brightness and linear integral transformations.

11. Filtration of noise.

12. Edge detections.

13. Mathematical morphology.

14. Image compression.

Syllabus of tutorials:

1. Discrete Fourier Transform (computer laboratory, MATLAB).

2. Digital filters (computer laboratory, MATLAB).

3. Correlation filtration (computer laboratory, MATLAB).

4. Sampling theorem and aliasing (laboratory).

5. Measurement of the power spectral density of noise (laboratory).

6. Suppression of periodic disturbance using digital filters (laboratory).

7. Using cross - correlation function for measurement of velocity (model of a conveyor-belt).

8. Task 1 - formation of image, radiometry, geometrical transformations.

9. Task 1 - completion.

10. Task 2 - noise filtration and edge detection.

11. Task 2 - completion.

12. Task 3 - mathematical morphology.

13. Task 3 - completion.

14. Task 4 - image compression.

Study Objective:

Study materials:

1. V.d.Enden, A., Verhoeckx, A.M.: Discrete-time Signal Processing. Prentice Hall, 1989

2. Bendat, J.S., Piersol, A.G.: Engineering Applications of Correlation and Spectral Analysis. J. Wiley, 1980

3. Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison - Wesley, 1992

4. Šonka, M., Hlaváč, V., Boyle, R.D.: Image processing, analysis and

machine vision. 3. vydání, Thomson Learning, Toronto, Canada, 2007

5. Svoboda, T., Kybic, J., Hlaváč, V.: Image processing, analysis and

machine vision. The MATLAB Companion, Thomson Learning, Toronto, Canada,

2007

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

	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	roomT2:A4-203a Sedláček M. Hlaváč V. 09:15–10:45 (lecture parallel1) Dejvice Učebna
Fri	room Svoboda T. Petříček T. 12:45–14:15 (lecture parallel1 parallel nr.101) roomT2:A3-412 Sedláček M. 12:45–14:15 (lecture parallel1 parallel nr.101) Dejvice Laborator
Thu
Fri

The course is a part of the following study plans:

• Cybernetics and Measurements- structured studies (compulsory course)
• Softwarové inženýrství (elective specialized course)
• Web a multimedia (elective specialized course)
• Manažerská informatika (elective specialized course)
• Inteligentní systémy (elective specialized course)