Universität Wien FIND
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

052600 VU Signal and Image Processing (2020W)

Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 50 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Depending on the current situation of the ongoing pandemic, lectures will be held either in-person in the lecture hall or online via Big Blue Button sessions. Information on the lecture format will be announced on the Moodle homepage of the course.

Dienstag 06.10. 11:30 - 13:00 Digital
Mittwoch 07.10. 15:00 - 16:30 Digital
Dienstag 13.10. 11:30 - 13:00 Digital
Mittwoch 14.10. 15:00 - 16:30 Digital
Dienstag 20.10. 11:30 - 13:00 Digital
Mittwoch 21.10. 15:00 - 16:30 Digital
Dienstag 27.10. 11:30 - 13:00 Digital
Mittwoch 28.10. 15:00 - 16:30 Digital
Dienstag 03.11. 11:30 - 13:00 Digital
Mittwoch 04.11. 15:00 - 16:30 Digital
Dienstag 10.11. 11:30 - 13:00 Digital
Mittwoch 11.11. 15:00 - 16:30 Digital
Dienstag 17.11. 11:30 - 13:00 Digital
Mittwoch 18.11. 15:00 - 16:30 Digital
Dienstag 24.11. 11:30 - 13:00 Digital
Dienstag 01.12. 11:30 - 13:00 Digital
Mittwoch 02.12. 15:00 - 16:30 Digital
Mittwoch 09.12. 15:00 - 16:30 Digital
Dienstag 15.12. 11:30 - 13:00 Digital
Mittwoch 16.12. 15:00 - 16:30 Digital
Dienstag 12.01. 11:30 - 13:00 Digital
Mittwoch 13.01. 15:00 - 16:30 Digital
Dienstag 19.01. 11:30 - 13:00 Digital
Mittwoch 20.01. 15:00 - 16:30 Digital
Dienstag 26.01. 11:30 - 13:00 Digital
Mittwoch 27.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG


Ziele, Inhalte und Methode der Lehrveranstaltung

Algorithms for data analysis are often based on the assumption of independent and identically distributed (i.i.d) data. The world, however, often violates the first "i", i.e., it generates data with a rich spatial and temporal structure such as time-series and images. Representing, understanding, and processing this structure is the domain of signal processing. As such, a firm grasp of signal processing is essential to understand structure in data and design systems that exploit this structure.

In the first part of this course, we will approach signal processing from the perspective of linear time-invariant (LTI) systems, i.e., we will consider signals as outputs of LTI-systems [1]. This approach will lead us to study the discrete(-time) Fourier transform (D(T)FT) and its applications, including sampling and filter design. In the second part of the course, we will study several variants and extensions of the Fourier transform, including the Hilbert-, Discrete Cosine- and Wavelet transforms. In the third part of the course, we will take an alternative approach to signal processing and consider signals as realizations of stationary stochastic processes [2]. This will lead us to the field of stochastic spectral analysis. We will conclude the course with an introduction to information theory and compression algorithms, e.g., the Lempel-Ziv-Welch (LZW) algorithm that is used in data formats such as ZIP and TIFF.

The lectures are complemented by tutorials, pen & paper exercises and coding assignments on simulated and experimental data to foster a deeper understanding of the topics covered in the lectures.

Due to the ongoing pandemic, we will adopt a mixed lecture format that complements pre-recorded video lectures with live (offline or online) review sessions. New video lectures and tutorials will be made available on Moodle on an ongoing basis. These videos form the basis for the review sessions, which will either be held in-person in the lecture hall or online via Big Blue Button sessions during the official lecture times. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Assignments: 51%
Two reaction sheets: 4%
Midterm: 20%
Final: 25%

By answering questions about the content of the video lectures prior to the in-class review sessions, it is possible to earn bonus points that count towards the total number of points.

Mindestanforderungen und Beurteilungsmaßstab

Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
Recommended prerequisites: NUM

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


The major goals of this course include:
* Understanding the theory of signals and linear time-invariant systems.
* Becoming familiar with spectral transformations and data compression algorithms.
* Being able to implement common transformations in Python and applying them to time-series and images.


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

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 12.11.2020 08:08