Universität Wien FIND

052600 VU Signal and Image Processing (2019S)

Continuous assessment of course work

Registration/Deregistration

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 05.03. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 06.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 13.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 19.03. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 20.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 26.03. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 27.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 02.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 03.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 09.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 10.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 30.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Tuesday 07.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 08.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 14.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 15.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 21.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 22.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 28.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 29.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 04.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 05.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 12.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 18.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 19.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 25.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
Wednesday 26.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Spatial signals 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. While many of the techniques we are covering can be explained in 1D (and for didactic reasons we will fall back to 1D a number of times) there is a fundamental difference when we need to create models of processing for more-than-1D signals. While most of "image" processing is really focused on 2D images, it is important to me that we keep the 3-dimensional nature of the world we live in in mind from day one. Hence, I am presenting a course, which is mostly an image processing course, but with some topics that are necessary to properly deal with 3D images.

* linear time-invariant systems (LTI)
* Fourier transform
* sampling + convolution
* DFT + FFT
* basic image transformations, some mathematical basics
* image restoration and reconstruction (denoising)
* wavelets and multi-resolution
* 3D scalar data / projection, rendering
* vectors and tensors

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 transforms and being able to apply them to 2D images and 3D data
* understanding of implementations in Python
* use of rendering methods for understanding 3D data

Reading list

Rafael C. Gonzales, Richard E. Woods Digital Image Processing 4th edition, Addison-Wesley, 2018.
Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, 3rd Edition, Pearson, 2010

Association in the course directory

Module: SIP AKM IMI

Last modified: Tu 05.03.2019 11:47