Multimedia Computing and Computer Vision Lab












Student Theses


Source Code / Datasets




SS 19 Multimedia II

From Multimedia Computing Lab - University of Augsburg

Registration is open on Digicampus:


Instructors: Prof. Dr. Rainer Lienhart, Dan Zecha
Time Lecture: Tue, 08:15-09:45, room 1058N and Fri, 10:00-11:30, room 1058N
Time Exercise: Fr, 12:15-13:45, room 1058N; first exercise will be on tbd
Examine: See DIGICAMPUS for date. (separate registration via STUDIS required)
Credits: 6 SWS, LP: 8
Multimedia Teilbereiche: Multimedia-Methoden, Multimedia-Anwendungen
Synopsis: This course addresses state-of-the-art computer vision algorithms that let computers see, learn, and understand image and video content. After being taught the required basics in machine learning, students will - accompanied by practical exercises - get to know the most promising techniques.

The topics of the course may be summarized as follows:

  • Machine learning
  • Implementation of algorithms using Intel IPP and MKL
  • Image/video processing
  • Media content analysis

The learned concepts will be illustrated by successful examples in practice. The accompanying exercises will contain some hands-on experiences. Towards the end of the course more advanced topics in object detection and object recognition will be addressed.


  • [04.03.2019] Page creation.

Important Comments

  • In order to be admitted to the final exam, students are required to register with the course in Digicampus and STUDIS. No additional requirements are imposed.
  • All reading notes are relevant for the exams independent on how thoroughly they have been discussed during the lecture. Thus read them carefully.


  1. M. Mitchell. Machine Learning. McGraw-Hill Science/Engineering/Math; Chapters 1-8; (
  2. Jeff Hawkins, Sandra Blakeslee. On Intelligence. B&T; Auflage: Reprint (August 2005), ISBN-13: 978-0805078534
  3. Bernd Jähne. Digital Image Processing. Springer Verlag.
  4. David A. Forsyth and Jean Ponce. Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River, New Jersey 07458.( )


All reading notes are relevant for the exams independent on how thoroughly they have been discussed in the lecture. Thus we suggest to read and study them carefully.

The slides and exercise sheets can be found online on Digicampus.