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CS 516 — Content-based Image and Video Analysis
(dt. Inhaltsbasierte Bild- und Videoanalyse)

Level, degree of commitment Specialization 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(s): 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 examination (individual examination) or written examination
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
Duration,
frequency		 One semester,
Im Wechsel mit anderen specialization 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

Students will

  • understand and are able to apply the necessary methods of content-based analysis of image and video data; this includes methods of image and moving image processing and machine learning,
  • can design software systems for image recognition and implement them based on Deep Learning libraries (Caffe, Tensorflow, ...),
  • are able to apply scientific working methods in independently identifying, 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.


Applicability

Module imported from M.Sc. Computer Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science

When studying B.Sc. Data Science, this module can be attended in the study area Free Compulsory Elective Modules.

The module is assigned to Computer Science. Further information on eligibility can be found in the description of the study area.


Recommended Reading

  • Will be announced in the course.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. 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.