Signal and Image Processing (ILV)

Back

Course lecturer:

FH-Prof.in DI Dr.in

 Ulla Birnbacher

image

Ing.

 Fritz Egger

image

FH-Prof. Dipl.-Ing. Dr. techn.

 Thomas Klinger , MLBT

image
Course numberB2.05270.30.120
Course codeSBVA
Curriculum2018
Semester of degree program Semester 3
Mode of delivery Presence- and Telecourse
SPPW3,0
ECTS credits4,0
Language of instruction German

The students are able to generate discrete-time signals by sampling analog signals and to determine the position of aliasing frequencies.
They are able to analyse signals in frequency domain, by applying DFT/FFT and to reduce the leakage effect by windowing.
They are able to characterize discrete, linear, time-invariant systems by the frequency response, impulse response or the system transfer function in z-domain.
They know about the different properties of discrete IIR and FIR systems and are able to design discrete filters with a desired characteristic by using MATLAB.
They are able to treat images as two-dimensional signals and to filter in the spatial domain as well as after an FFT in the frequency domain.
They can apply a DCT to images to understand the basics of the JPEG compression method.
They can segment images and apply morphological operations to objects in images.

Discrete signals in time domain (signal acquisition, sampling, aliasing)
Discrete signals in frequency domain (discrete Fourier transform, discrete cosine transform, spectrum analysis with DFT/FFT, leakage effect, windowing)
Discrete systems (FIR- and IIR systems, application of z-transform)
Discrete filters (analysis and design of FIR- and IIR filters)
Spatial filtering, morphological operations

M. Meyer, Signalverarbeitung: analoge und digitale Signale, Systeme und Filter, 6.Auflage, Vieweg + Teubner, 2011
J.H. McClellan, R.W. Schafer, M.A. Yoder; DSP First: A Multimedia
Approach; Prentice Hall, 1998.
A. Oppenheim, R.W.Schafer, Digital Signal Processing, Pearson 2015.
D. Kreß, B. Kaufhold, Signale und Systeme verstehen und vertiefen, Vieweg +Teubner Verlag, 2010
T. Klinger; Image Processing with LabVIEW and IMAQ Vision, Prentice Hall, 2003

Lecture with integrated exercises
MATLAB exercises (in groups of 20 students max)
Homework and protocols as individual tasks
Lab exercise (with LABVIEW) in groups of 18 students max.
Image processing methods as inverted class room
LabVIEW image processing exercises

MATLAB-exercises, LabVIEW exercises, lab exercise and protocols
written exam