Letzte Änderung : 12.10.2026 14:55:12
| Studiengänge >> Mechatronics for Manufacturing 2026 B.Eng. >> Image Processing |
| Code: | 214950 |
| Module title: | Image Processing |
| Version: | 1.0 (04/2016) |
| Last update: | 08.10.2026 |
| Responsible person: | Prof. Dr. rer. nat. Bischoff, Stefan s.bischoff@hszg.de |
| Offered in 4 study courses: | Automation and Mechatronics (B.Eng.) valid from the class 2021 (elective core module (specialization)) | Electrical Engineering (B.Eng.) valid from the class 2024 (elective core module (specialization)) | Electrical Engineering (B.Eng.) valid from the class 2026 (elective core module (specialization)) | Mechatronics for Manufacturing (B.Eng.) valid from the class 2026 (compulsory module) |
| Semester according to time table: | SoSe+WiSe (summer and winter semester) |
| Module level: | Bachelor/Diplom |
| Duration: | 1 semester |
| Status: | compulsory module |
| Place where the module will be offered: | Zittau |
| Language of Instruction: | English |
| Workload* in | SCH ** | |||||||||||||||||||||||||
| hours | ECTS Credits |
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| * | Overall workload per module (1 ECTS credit corresponds to a workload of 30 hours) |
| ** | One semester credit hour (SCH) corresponds to a workload / class meeting of 45 minutes per week in a semester |
| Self study time in hours | ||||
| Learning and teaching methods: | Lecture, seminar and practical work with computer |
| Exam(s) | |||
| Assessment | Major examination (written report) | 100.0% | |
| Syllabus plan/Content: |
This course provides a general introduction to the fundamental techniques of computer vision and image processing and illustrates their practical application. The main topics are: - Image acquisition and representation - Preprocessing methods: transformations of pixel brightness and geometry, camera calibration, local operators - Video and audio compression - Image segmentation: thresholding, edge-based and region-based segmentation, Hough transformation, template matching, motion segmentation, optical flow - Feature extraction: color, texture and shape descriptors; Principal Component Analysis (PCA) - Classification: prototypes, cluster analysis, statistical methods, classifiers - Teachable image evaluation: supervised and non-supervised learning, neural networks, Support-Vector-Machines (SVM) - Multi-sensor technology: depth sensors, photogrammetry, 3D scene reconstruction Overview of current practical application areas: visual quality inspection, robotics, medical diagnosis, video conference systems, biometry, security |
| Learning Outcomes: | |
| Subject-specific skills and competences: | After completing the module, students are able to use an image processing system for typical applications - to specify, - to integrate into machines and processes and - to build it (i.e. to select and program suitable components for it) and - Evaluate components |
| Generic competences (Personal and key skills): | The students - discuss in small teams the procedure for solving the project-specific tasks within the framework of a document and create the plan for the project. (Teamwork and communication skills) Presentation of results - defending your own solution approaches. Results-oriented action and determination when solving engineering tasks. |
| Pre-requisites: | Competencies from the modules Basics of computer science, object-oriented programming (without proof requirement |
| Optional pre-requisites: | Programming knowledge in Python and handling of the image processing library OpenCV |
| Literature: | Bernd Jähne, Digital Image Processing, Springer, 5th edition (April 29, 2002), ISBN: 3540677542 Gary Bradski, Adrian Kaehler, Learning OpenCV – Computer Vision with the OpenCV Library, O’Reilly Media, 2008, ISBN: 978-0-596-51613-0 Howse J., Minichino J.: Learning OpenCV 5 Computer Vision with Python. 2024 |