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German original

Content-based Image and Video Analysis
(dt. Inhaltsbasierte Bild- und Videoanalyse)

Level, degree of commitment in original study programme Advanced module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (2 SWS), recitation class (2 SWS),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements		 6 CP
Course requirement: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for study course M.Sc. Computer Sciences.
Original study programme M.Sc. Informatik / Vertiefungsbereich Informatik
Duration,
frequency		 One semester,
Im Wechsel mit anderen advanced moduleen
Person in charge of the module's outline Prof. Dr. Bernd Freisleben, Dr. Markus Mühling

Contents

The lecture deals with methods for content-based image processing

and video analysis. The following topics will be covered:

  • Basics of image and video processing
  • Machine learning
  • Basics of deep neural networks (CNN, LSTM)
  • cut detection
  • image recognition
  • similarity search
  • image segmentation
  • person recognition
  • Text spotting

Qualification Goals

The learning objective of the module is to understand and be able to apply the methods necessary for the content-based analysis of image and video data. These include methods of image and moving image processing and machine learning. After visiting the module, the listeners should be able to design and implement software systems for image recognition based on Deep Learning libraries (Caffe, Tensorflow, ...). In addition, the students practice scientific working methods by training their ability to abstract as well as recognizing, formulating and solving problems.


Prerequisites

None. The competences taught in the following modules are recommended: Object-oriented Programming, Algorithms and Data Structures. In addition, programming experience in Python and C++ is recommended and basic knowledge of Linux is helpful.


Recommended Reading

  • Wird in der Veranstaltung bekanntgegeben.



Please note:

This page describes a module according to the latest valid module guide in Wintersemester 2021/22. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.