Digital Image Processing (BIV)
6/7. Semester
5 ECTS | 4 SWS
Written Exam 90 Minutes (K90)
Teach computers how to see: You learn how to turn pixels into structured information – from color spaces and image operators through feature engineering and segmentation to deep learning methods for object detection, with a focus on medical imaging applications.
Contents
- Representations of digital images and image volumes, colour spaces, image acquisition
- Point operators
- Local operators
- Feature engineering, object detection and object recognition
- Segmentation
- Applications of Deep Learning in image processing
- Image transformations
- Image registration
- Applications in medicine
Competencies
Students are able to
- understand and apply key concepts and methods of image processing,
- understand the use cases and limitations of the methods,
- transfer the learned methods to new applications,
- understand and classify pertinent, recent research topics.
Literature
- A. Nischwitz, M. Fischer, P. Haberäcker, G. Socher: Bildverarbeitung, Springer, 2020
- A. Torralba, P. Isola, W. Freeman: Foundations of Computer Vision, MIT Press, 2024
- D. Sundararajan: Digital Image Processing, Springer, 2017
- R.C. Gonzalez, R.E. Woods: Digital image processing, Pearson Prentice Hall, 2017
- B. Jähne: Digitale Bildverarbeitung, Springer, 2012
- K.D. Toennies: Grundlagen der Bildverarbeitung, Pearson-Verlag, 2005
- M. Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis, and Machine Vision, Addison-Wesley, 2007
- T. Lehmann, W. Oberschelp, E. Pelikan: Bildverarbeitung für die Medizin, Springer, 1997
- Handbücher der eingesetzten Programmiersprache
Lecturer
Recommended Previous Knowledge
Module Details
| Semester |
6/7 |
| Lecture Language |
German |
|
Frequency
|
Not regularly
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Ivo Wolf |
| Duration |
1 Semester |
|
Course Achievement
|
None |
|
Prerequisite for exam
|
None |
|
Exam
|
Written Exam 90 Minutes (K90) |
Weekly Hours (SWS)
| Lecture |
2 SWS |
| Exercises |
2 SWS |
| Sum |
4 SWS |
Work Load
| Lecture |
30 h |
| Lab |
30 h |
| Self Study |
30 h |
| Assignments |
30 h |
| Exam Preparation |
30 h |
| Sum |
150 h |
