htw saar Piktogramm QR-encoded URL
Back to Main Page Choose Module Version:
XML-Code

flag


Classic Image Processing Algorithms with LabVIEW

Module name (EN):
Name of module in study programme. It should be precise and clear.
Classic Image Processing Algorithms with LabVIEW
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Biomedical Engineering, Bachelor, SO 01.10.2025
Module code: BMT2592.KBL
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.
2V+2U (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.
5
Semester: 6
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam 120 min. (50%), Project work (50%) with paper (20-30 pages)

[updated 21.04.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BMT2592.KBL Biomedical Engineering, Bachelor, SO 01.10.2025 , semester 6, optional course
E2514 Electrical Engineering and Information Technology, Bachelor, ASPO 01.10.2018 , semester 6, optional course
Workload:
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):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Michael Kleer
Lecturer:
Marc Quirin, M.Sc.


[updated 05.02.2026]
Learning outcomes:
After successfully completing this module students will be able to solve classic image processing problems in a structured manner. They will have acquired the skills to process 2D images using LabVIEW software. Students will be able to explain theoretical methods using examples. They will be able to differentiate and interpret images based on their characteristics. This enables them to solve image processing problems methodically.

[updated 21.04.2026]
Module content:
1. Concepts for solving a classic image processing problem
1.1 Selection criteria for an image processing system
1.2 Possible computations
1.3 Image processing chain
 
2. Algorithms for the image processing chain
2.1 Mathematical tools
2.2 Preprocessing
2.2.1 Color models
2.2.2 Transformation functions
2.2.3 Local operators
2.2.4 Global operator (FFT)
 
2.3 Morphological operators
2.4 Segmentation
2.6. Extracting characteristics
2.7 Classification
 
3. Solutions and strategies for image processing tasks

[updated 21.04.2026]
Teaching methods/Media:
Blackboard, lecture notes, LabVIEW

[updated 21.04.2026]
Recommended or required reading:
Tönnies Klaus D.: Grundlagen der Bildverarbeitung, Addison-Wesley Verlag, 2005
Jähne B.: Digitale Bildverarbeitung. Springer, 5. Edition, 2002
Haberäcker Peter: Digitale Bildverarbeitung, Carl Hanser Verlag München Wien, 1987

[updated 21.04.2026]
 
[Wed Apr 29 10:05:26 CEST 2026, CKEY=ekbml, BKEY=bmt4, CID=BMT2592.KBL, LANGUAGE=en, DATE=29.04.2026]