Letzte Änderung : 26.01.2025 00:21:13   


Output

Code: 102810
Module title: Image Processing
Version: 1.0 (07/2007)
Last update: 28.09.2022 08:15:11
Person responsible for content: Prof. Dr. rer. nat. Bischoff, Stefan
s.bischoff@hszg.de

Offered in:Mechatronics (M.Eng.) valid from class 2019

Semester according to timetable: WiSe (winter semester)
Module level:Master
Duration:1 semester
Language of Instruction:English
Place where the module will be offered:Zittau

ECTS Credits: 5
Student workload (in hours): 150

Number of hours of teaching:
total
subdivided into
4
2
Lecture
2
Seminar/Exercise
0
Laboratory work
0
Other
Self study time (in hours):
sum
subdivided into
105
45
Preparation of contact hours
30
Preparation of exam
30
Other

Learning and teaching methods:Lecture, seminar and practical work with computer


Exam(s)
Assessment Major written exam 150 min 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 Goals
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.

Prerequisites: Competencies from the modules
Basics of computer science, object-oriented programming
(without proof requirement)
Optional: 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