Universität Wien

052600 VU Signal and Image Processing (2020W)

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

URL: http://vda.univie.ac.at/Teaching/SIP/
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. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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.

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

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

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%

Examination topics

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.

Reading list

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.

Association in the course directory

Last modified: Fr 12.05.2023 00:13