Universität Wien FIND

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052600 VU Signal and Image Processing (2021S)

Continuous assessment of course work

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: We 10.03.2021 08:08