Universität Wien

052600 VU Signal and Image Processing (2025S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Fr 23.05. 09:45-11:15 Hörsaal 3, Währinger Straße 29 3.OG

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. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 04.03. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 07.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 11.03. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 14.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 18.03. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 21.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 25.03. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 28.03. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 01.04. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 04.04. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 08.04. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 11.04. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 29.04. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 02.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 06.05. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 09.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 13.05. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 16.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    Seminarraum 3, Währinger Straße 29 1.UG
    Seminarraum 8, Währinger Straße 29 1.OG
  • Tuesday 20.05. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 27.05. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 30.05. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 03.06. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 06.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 10.06. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 13.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 17.06. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Friday 20.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 24.06. 13:15 - 14:45 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
    PC-Unterrichtsraum 5, Währinger Straße 29 2.OG
  • Friday 27.06. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG

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: 1% for math test
* Two feedback sheets: 4%
* Pen & paper exam: 47.5%
* Final exam: 47.5%

In addition, you can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session. These bonus points count towards the overall points independently of the points you achieve on the assignments and the exams, i.e., they can help you pass the course.

Grading will be done according to the following scheme:

1. At least 87.5%
2. At least 75.0%
3. At least 62.5%
4. At least 50.0%

*In addition, you need at least 10% of the points on each exam to pass the course.*

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%

In each exam, a minimum of 10% of the maximum reachable points is required to pass the course.

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 time-series and 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: We 30.04.2025 08:25