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Signal and Image Processing

Module name (EN):
Name of module in study programme. It should be precise and clear.
Signal and Image Processing
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Mechatronics and Sensor Technology, Master, ASPO 01.10.2011
Module code: MST.SIG
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
Semester: 9
Mandatory course: yes
Language of instruction:
Written exam 150 min.

[updated 01.10.2020]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

MTM.SIG (P231-0015) Mechatronics, Master, ASPO 01.04.2020 , semester 2, mandatory course
MST.SIG (P231-0015) Mechatronics and Sensor Technology, Master, ASPO 01.04.2016 , semester 2, mandatory course
MST.SIG (P231-0015) Mechatronics and Sensor Technology, Master, ASPO 01.10.2011 , semester 9, mandatory course
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
Recommended as prerequisite for:

[updated 11.07.2012]
Module coordinator:
Prof. Dr. Michael Kleer
Lecturer: Prof. Dr. Michael Kleer

[updated 28.01.2010]
Learning outcomes:
After successfully completing this course, students will be able to apply system theory to image processing problems. They will be familiar with the hardware and software used for image processing, in detail, and understand how they work together based on examples. Students will be able to identify and understand quality assurance tasks in the broadest sense of the term and implement them. Practical application is the main focus here.

[updated 01.10.2020]
Module content:
1. One-dimensional signals in the time domain, mathematical description, representation of the associated spectra, explanation of the filter process, transition to discrete signals and to discrete spectra, sampling, FFT
2. Two-dimensional signals, extending the mathematical theory
3. Images as two-dimensional signals in the spatial domain, simple key figures for images, quantifying and rasterizing images,
4. Storing and reproducing images and related compression methods
5. Discrete image processing algorithms im the spatial domain
6. Image processing algorithms in the frequency domain

[updated 01.10.2020]
Recommended or required reading:
Will be announced in the course.

[updated 01.10.2020]
[Mon Mar  4 06:22:30 CET 2024, CKEY=zmsuba, BKEY=ystm, CID=MST.SIG, LANGUAGE=en, DATE=04.03.2024]