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Image Processing and Pattern Recognition

Module name (EN):
Name of module in study programme. It should be precise and clear.
Image Processing and Pattern Recognition
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Electrical Engineering, Master, ASPO 01.10.2005
Module code: E902
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.
3V+1U (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: 9
Mandatory course: yes
Language of instruction:
English/German
Assessment:
Oral examination, project work

[updated 12.03.2010]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

E902 Electrical Engineering, Master, ASPO 01.10.2005 , semester 9, mandatory 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.-Ing. Dietmar Brück
Lecturer:
Prof. Dr.-Ing. Dietmar Brück


[updated 13.03.2018]
Learning outcomes:
The module Image Processing and Pattern Recognition aims to teach students how systems theory can be applied to tackle problems in image processing. Students will acquire the skills needed to assess the interaction of hardware and software components in image processing systems. After completing this course, students will have the skills required to select image processing and pattern recognition techniques for specific practical applications and to deploy suitable methods of image information extraction.
As the module includes authentic applications drawn from the field of quality assurance, students will be taught the practically relevant criteria that need to be fulfilled in such cases.

[updated 12.03.2010]
Module content:
1.Overview of image processing algorithms
2.Review of camera types, lighting, frame grabbers, system software
3.Pattern recognition, neural networks
4.Robot vision
5.Special research-based applications: Contour tracking, surface inspection,  
  integrity checking, security issues, analysis of moving images in medical  
  technology

[updated 12.03.2010]
Teaching methods/Media:
Lecture notes, overhead transparencies, video projector, PC, CD

[updated 12.03.2010]
Recommended or required reading:
At the beginning of the course, students will be issued with a CD containing all the teaching material used in this module. The CD also contains a complete and regularly updated list of recommended reading materials.

[updated 12.03.2010]
 
[Sat Dec 28 23:55:57 CET 2024, CKEY=ebum, BKEY=em, CID=E902, LANGUAGE=en, DATE=28.12.2024]