Universität Wien
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052600 VU Signal and Image Processing (2021S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Due to the ongoing pandemic, all lectures will be held online until further notice.

  • Tuesday 02.03. 15:00 - 16:30 Digital
  • Wednesday 03.03. 15:00 - 16:30 Digital
  • Tuesday 09.03. 15:00 - 16:30 Digital
  • Wednesday 10.03. 15:00 - 16:30 Digital
  • Tuesday 16.03. 15:00 - 16:30 Digital
  • Wednesday 17.03. 15:00 - 16:30 Digital
  • Tuesday 23.03. 15:00 - 16:30 Digital
  • Wednesday 24.03. 15:00 - 16:30 Digital
  • Tuesday 13.04. 15:00 - 16:30 Digital
  • Wednesday 14.04. 15:00 - 16:30 Digital
  • Tuesday 20.04. 15:00 - 16:30 Digital
  • Wednesday 21.04. 15:00 - 16:30 Digital
  • Tuesday 27.04. 15:00 - 16:30 Digital
  • Wednesday 28.04. 15:00 - 16:30 Digital
  • Tuesday 04.05. 15:00 - 16:30 Digital
  • Wednesday 05.05. 15:00 - 16:30 Digital
  • Tuesday 11.05. 15:00 - 16:30 Digital
  • Wednesday 12.05. 15:00 - 16:30 Digital
  • Tuesday 18.05. 15:00 - 16:30 Digital
  • Wednesday 19.05. 15:00 - 16:30 Digital
  • Wednesday 26.05. 15:00 - 16:30 Digital
  • Tuesday 01.06. 15:00 - 16:30 Digital
  • Wednesday 02.06. 15:00 - 16:30 Digital
  • Tuesday 08.06. 15:00 - 16:30 Digital
  • Wednesday 09.06. 15:00 - 16:30 Digital
  • Tuesday 15.06. 15:00 - 16:30 Digital
  • Wednesday 16.06. 15:00 - 16:30 Digital
  • Tuesday 22.06. 15:00 - 16:30 Digital
  • Wednesday 23.06. 15:00 - 16:30 Digital
  • Tuesday 29.06. 15:00 - 16:30 Digital
  • Wednesday 30.06. 15:00 - 16:30 Digital

Information

Aims, contents and method of the course

Signals with temporal or spatial structure are ubiquitous. Whether they are 1D signals, as in audio or time-dependent measurement/sensors, whether these are 2D photographs and LIDAR images or whether they are 3D medical volumes, climate models, or computational fluid studies; these signals are everywhere. In this course, we study the mathematical foundations of signal processing and learn how to implement and apply commonly used algorithms in image analysis. Topics we cover include linear time-invariant (LTI) systems, convolution, the Fourier transform and its extensions, sampling and filtering of signals, spectral analysis, information theoretic coding and wavelet analysis.

Assessment and permitted materials

Assignments: 51%
2xReaction sheets: 4%
Midterm: 20%
Final: 25%

Minimum requirements and assessment criteria

Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
recommended prerequesites: NUM

The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%

Examination topics

The major goals of this course include:
* understanding the mathematical foundations of signal processing
* being able to implement commonly used signal processing algorithms in Python
* applying signal processing algorithms to image analysis

Reading list

1. Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, 3rd Edition, Pearson, 2010
2. Rafael C. Gonzales, Richard E. Woods Digital Image Processing 4th edition, Addison-Wesley, 2018.
3. Donald B. Percival, Andrew T. Walden, Spectral Analysis for Physical Applications, Cambridge University Press, 1993

Association in the course directory

Module: SIP AKM IMI

Last modified: Fr 12.05.2023 00:13