052600 VU Signal and Image Processing (2022W)
Continuous assessment of course work
Labels
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).
- Registration is open from We 14.09.2022 09:00 to We 21.09.2022 09:00
- Deregistration possible until Fr 14.10.2022 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
If not communicated otherwise, lectures will be held in-person in the specified lecture hall.
Tuesday
04.10.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
05.10.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
11.10.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
12.10.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
18.10.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
19.10.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
25.10.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
08.11.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
09.11.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
15.11.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
16.11.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
22.11.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
23.11.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
29.11.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
30.11.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
06.12.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
07.12.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
13.12.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
14.12.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
10.01.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
11.01.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
17.01.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
18.01.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
24.01.
11:30 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
25.01.
15:00 - 16:30
Hörsaal 2, Währinger Straße 29 2.OG
Tuesday
31.01.
11:30 - 13:00
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.
Assessment and permitted materials
There will be four assignments (two pen & paper assignments and two Pythong coding exercises), one mid-term exam, and one final exam. The various assignments and exams count towards the final grade as follows:* Assignments: 51%
* Two feedback sheets: 4%
* Midterm: 20%
* Final: 25%In addition, you can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session.
* Two feedback sheets: 4%
* Midterm: 20%
* Final: 25%In addition, you can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session.
Minimum requirements and assessment criteria
Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
Recommended prerequisites: NUMGrading will be done according to the following scheme:1. At least 87.5%
2. At least 75.0%
3. At least 62.5%
4. At least 50.0%In addition, you need at least 10% of the points *on each assignment and on each exam* to pass the course.
Recommended prerequisites: NUMGrading will be done according to the following scheme:1. At least 87.5%
2. At least 75.0%
3. At least 62.5%
4. At least 50.0%In addition, you need at least 10% of the points *on each assignment and on each exam* to pass the course.
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.
* 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.
4. Boaz Porat, Digital Processing of Random Signals, Dover Publications, 2008.
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.
4. Boaz Porat, Digital Processing of Random Signals, Dover Publications, 2008.
Association in the course directory
Last modified: We 30.11.2022 13:08