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Computer Vision

Module name (EN):
Name of module in study programme. It should be precise and clear.
Computer Vision
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Bachelor, ASPO 01.10.2011
Module code: PIBWI83
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.
4V (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:
Project work

[updated 19.02.2018]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

KI692 Computer Science and Communication Systems, Bachelor, ASPO 01.10.2014 , semester 6, optional course, technical
KIB-CVIS (P221-0069) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2021 , semester 6, optional course, technical
KIB-CVIS (P221-0069) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2022 , semester 6, optional course, technical
MST.CVI (P221-0069) Mechatronics and Sensor Technology, Bachelor, ASPO 01.10.2012 , semester 6, optional course, technical
MST.CVI (P221-0069) Mechatronics and Sensor Technology, Bachelor, ASPO 01.10.2019 , semester 6, optional course, technical
MST.CVI (P221-0069) Mechatronics and Sensor Technology, Bachelor, ASPO 01.10.2020 , semester 6, optional course, technical
PIBWI83 Applied Informatics, Bachelor, ASPO 01.10.2011 , semester 6, optional course, informatics specific
PIB-CVIS (P221-0069) Applied Informatics, Bachelor, ASPO 01.10.2022 , semester 4, optional course, informatics specific
MST.CVI (P221-0069) Mechatronics and Sensor Technology, Bachelor, ASPO 01.10.2011 , semester 6, optional course, technical
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):
PIB125 Mathematics 1
PIB215 Mathematics 2
PIB315 Mathematics 3


[updated 27.03.2013]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Gerald Kroisandt
Lecturer:
Dipl.-Math. Dimitri Ovrutskiy
Prof. Dr. Barbara Grabowski


[updated 27.03.2013]
Lab:
Applied Mathematics, Statistics, and eLearning (5306)
Learning outcomes:
After successfully completing this module, students will be able to explain and apply image processing algorithms such as noise reduction and deblurring. They will be familiar with the design of digital filters. They will be able to manipulate images without using image editing software.
 
In addition, they will also be able to apply methods that can detect moving objects in a film, reconstruct 3D information based on images and improve the quality of 2D images. Students will learn how robots _see_.


[updated 19.02.2018]
Module content:
* Digitization of analog images
* Image transformations (e.g. linear filters, math. Morphology, diffusion filters, wavelet shrinkage, deblurring)
* Color perception and color spaces
* Image editing
* Feature extraction (edges, corners, lines and circles)
* Segmentation
* Extraction of 3D information
* Object detection

[updated 19.02.2018]
Teaching methods/Media:
100% of the lecture will take place in the PC lab AMSEL "Angewandte Mathematik, Statistik und eLearning". Computer-supported practical case studies will be worked through using the algorithms taught in this module.
  
In addition, the eLearning system MathCoach (AMSEL PC laboratory 5306) will be used.


[updated 24.02.2018]
Recommended or required reading:
R.C. Gonzalez, R.e. Woods: Digital Image Processing, Addison-Wesley, SE 2002
K.R. Castelman: Digital Image Procesing, Prentice Hall, 1996
R.Jain, R.Kasturi, B.G. Schunck: Machine Vision, McGraw, 1995
E.Trucco, A. Verri: Introductory Techniques for 3-D Computer Vision, Prentice Hall,1995
R.Klette, K.Schlüns, A.Koschan: Computer Vision:Three-Dimensional Data from Images, Springer, 1998


[updated 19.02.2018]
Module offered in:
SS 2020, SS 2019, SS 2018, SS 2017, SS 2016, ... 
[Thu Nov 21 14:55:18 CET 2024, CKEY=kca, BKEY=pi, CID=PIBWI83, LANGUAGE=en, DATE=21.11.2024]