Image Processing
- Faculty
Faculty of Engineering and Computer Science
- Version
Version 1 of 27.11.2025.
- Module identifier
11B0538
- Module level
Bachelor
- Language of instruction
German
- ECTS credit points and grading
5.0
- Module frequency
irregular
- Duration
1 semester
- Brief description
The image processing module begins with an introduction to the representation of image data. It then goes on to explain different types of image representation. The procedure for improving and filtering image data is demonstrated. Finally, the extraction of symbolic information from pixel-oriented image data is discussed.
- Teaching and learning outcomes
- Introduction
- Image representation and storage
- Transformations
- Local image enhancement
- Linear image filtering
- Morphological image filtering
- Feature extraction and classification
- Selected topics in image processing
- Overall workload
The total workload for the module is 150 hours (see also "ECTS credit points and grading").
- Teaching and learning methods
Lecturer based learning Workload hours Type of teaching Media implementation Concretization 45 Lecture - 15 Laboratory activity - Lecturer independent learning Workload hours Type of teaching Media implementation Concretization 20 Preparation/follow-up for course work - 20 Study of literature - 30 Exam preparation - 20 Other -
- Graded examination
- Written examination or
- Project Report, written or
- oral exam
- Ungraded exam
- Field work / Experimental work
- Exam duration and scope
Graded examination performance:
Project report (written): approx. 10–15 pages; explanation: approx. 20 minutes
Oral examination: see general section of the examination regulations
Written examination: see study regulations
Ungraded examination performance:Experimental work: Experiment: approx. 5 experiments in total
- Recommended prior knowledge
The module requires programming skills and mathematical knowledge (especially vector and matrix calculus).
- Knowledge Broadening
Students who have successfully completed this module will have a basic understanding of image data representation, know how to extract information, and be familiar with basic image processing algorithms.
- Knowledge deepening
Students are familiar with the steps involved in image processing, from pixel representation to the extraction of knowledge from images using selected algorithms.
- Knowledge Understanding
Students are able to implement simple image processing algorithms in programmes and combine them with each other. This enables them to solve simple image processing tasks in practice.
- Literature
W. Burger und M. J. Burge: Digitale Bildverarbeitung - Eine Einführung mit Java und ImageJ. 3. Auflage, Springer-Verlag, 2015. R. C. Gonzalez, R. E. Woods: Digital Image Processing. Pearson International, 2008. B. Jähne: Digitale Bildverarbeitung. Springer, 2005. Pierre Soille: Morphological Image Analysis - Principles and Applications. Second Edition. Springer, 2004. R. Klette, P. Zamperoni: Handbook of Image Processing Operators. John Wiley & Son Ltd, 1996. P. A. Henning: Taschenbuch Multimedia. Fachbuchverlag Leipzig, 2001.
- Applicability in study programs
- Electrical Engineering in Practical Networks (dual)
- Electrical Engineering in Practical Networks (dual) B.Sc. (01.03.2026)
- Computer Science and Media Applications
- Computer Science and Media Applications B.Sc. (01.09.2025)
- Computer Science and Computer Engineering
- Computer Science and Computer Engineering B.Sc. (01.09.2025)
- Electrical Engineering
- Electrical Engineering B.Sc. (01.09.2025)
- Person responsible for the module
- Weinhardt, Markus
- Teachers
- Weinhardt, Markus