Stud.IP Uni Oldenburg
University of Oldenburg
29.05.2022 14:51:56
inf018 - Media Processing
Department of Computing Science 6 KP
Module components Semester courses Wintersemester 2021/2022 Examination
Lecture
  • Unlimited access 2.01.018 - Medienverarbeitung Show lecturers
    • Prof. Dr. techn. Susanne Boll-Westermann
    • Dr.-Ing. Larbi Abdenebaoui

    Tuesday: 10:15 - 11:45, weekly (from 19/10/21), V, Location: A04 5-516, V03 0-D001, A01 0-006 (+1 more)
    Wednesday: 10:15 - 11:45, weekly (from 20/10/21), PR, Location: A04 5-516, V03 0-D001, (Online)
    Dates on Friday. 25.02.22 10:00 - 14:00, Tuesday. 01.03.22 12:00 - 13:00, Location: ((OFFIS, O47))

Project
  • Unlimited access 2.01.018 - Medienverarbeitung Show lecturers
    • Prof. Dr. techn. Susanne Boll-Westermann
    • Dr.-Ing. Larbi Abdenebaoui

    Tuesday: 10:15 - 11:45, weekly (from 19/10/21), V, Location: A04 5-516, V03 0-D001, A01 0-006 (+1 more)
    Wednesday: 10:15 - 11:45, weekly (from 20/10/21), PR, Location: A04 5-516, V03 0-D001, (Online)
    Dates on Friday. 25.02.22 10:00 - 14:00, Tuesday. 01.03.22 12:00 - 13:00, Location: ((OFFIS, O47))

Notes for the module
Reference text
Useful previus knowlodge: Solid programming skills in Java and/or C++, practical informatics. Interest in media processing
 
Time of examination
Die Vorstellung des praktischen Projektes an einem Projekttag aller Kleingruppen findet direkt im Anschluss an die Vorlesungszeit statt. Die mündliche Prüfung findet in den ersten beiden Wochen nach Ende der Vorlesungszeit statt. Etwaige Nachprüfungen finden am Ende der vorlesungsfreien Zeit statt. Der genaue Zeitplan kann den Webseiten der Abteilung sowie den Angaben im Lernmanagementsystem Stud.IP entnommen werden.
Module examination
Project and oral exam
Skills to be acquired in this module

The students can explain the basics of image processing and know which algorithms exist for the basic tasks in this field and how these are applied.

Students can explain the basics of machine learning for image processing and know the essential properties of Convolutional Neural Networks and how they are designed, trained and applied.

The students can apply basic methods of image processing they learned in the lecture to solve simple problems.

**Professional compentence:**The students
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  can name basic characteristics of digital media
- can describe basic procedures for image enhancement, feature extraction, feature description, image analysis and image comprehension,
- can describe the basic machine learning methods for image processing and how they work.

**Methodological competence:** The students
- can recognize and evaluate image properties and decide for suitable image processing methods.
- can select existing software packages for simple image processing problems, as well as use and customize them for their specific task.

- can implement simple image and media processing functions in a higher programming language (e.g., Python).
- can apply basic Deep Learning methods to solve a concrete media processing problem.
 

**Social competence** The students:

- can plan, implement, and document a software project in team work.

- can present the results of their work to an audience and adequately respond to criticism and questions.


**Self competence** The students:

-can accept and learn from mistakes made during the process of implementation.


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