Signal and Image Processing

The course is a substitute for:

Signals and Images Processing (X383ZS)

Lecturer:

Tutor:

Supervisor:

Department of Measurement

Synopsis:

The course presents basics of digital signal processing (sampling and

reconstruction, DFT and FFT, digital filters, stochastic signal processing)

with emphasis on images as an important case of 2D signals. Apart from

classic linear techniques also applicable nonlinear techniques are

mentioned. Image compression.

Requirements:

Syllabus of lectures:

1. Types of signals. Frequency spectrum of sampled signals. Signals

reconstruction

2. FT of discrete signals and Discrete Fourier Transform (DFT)

3. FFT and use of DFT for spectrum analysis of periodic signals

4. Digital filters - definition, types, properties

5. Design methods o FIR and IIR filters

6. Stochastic signals: amplitude description, correlation functions and

their use

7. Power spectral density. Estimation of signals under noise level

8. Image as signal. Image formation. Geometry and radiometry

9. Image preprocessing and restauration

10. Image compression, lossless and loosy methods

11. Image segmentation as a way to extract object

12. Mathematical morphology

13. Hardware for capturing, processing and displaying the image

14. Image applications, how to develop them, software tools

Syllabus of tutorials:

1. FFT (analysis of periodical signals, leakage, windows) (Matlab)

2. Digital filters and their use (Matlab)

3. Correlation functions and finding power spectral density (PSD) using

FFT (Matlab)

4. Measurement of PSD using filtration or FFT spectral analyzer

5. Measurement of parameters of stochastic signals, application of cross

correlation

6. Experimental verification of the sampling theorem, aliasing

7. Using digital filtration to suppress disturbances

8. Project: Images, filtration, segmentation, measurements (tools:

Neurocheck, Matlab)

9. Agreement to project specification

10. Project solution

11. Project solution

12. Project solution

13. Defense of theproject results to instructors

14. Credit

Study Objective:

Study materials:

[1] Oppenheim A. B., Schafer R. W.: Discrete-Time Signal Processing.

Prentice Hall, Englewood Cliffs, N.Y. 1989

[2] Gonzales R. C., Woods R. E.: Digital Image Processing. Addison-Wesley,

1992

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: