Universität Wien FIND

Teaching at the University of Vienna will take place in the form of remote learning until the end of the semester. Exams basically take place digitally as well. Further information about remote learning

From the end of May onwards, individual exams that cannot be held online will be taking place within the framework of limited exam operation on site at exam centres. You consent to the changed mode of assessment when registering for the exam/course. All information about the exams at the exam centres

052600 VU Signal and Image Processing (2019W)

Continuous assessment of course work

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 01.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 02.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 08.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 09.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 15.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 16.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 22.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 23.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 29.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 30.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 05.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 06.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 12.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 13.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 19.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 20.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 26.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 27.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 03.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 04.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 10.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 11.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 17.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 07.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 08.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 14.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 15.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 21.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 22.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Tuesday 28.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Wednesday 29.01. 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

Teilnahmevorraussetzungen: StEOP, PR2, MG2, THI, MOD, ADS
Empfohlene Teilnahmevoraussetzung: 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 Matlab
* 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

Last modified: We 19.02.2020 09:27