Multimedia Computing and Computer Vision Lab

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SS 12: Multimedia II: Media Mining

From Multimedia Computing Lab - University of Augsburg


Registration is open now! LectureReg

Overview

Instructors: Prof. Dr. Rainer Lienhart, Fabian Richter
Time Lecture: Wed, 10:00-11:30 and Fri, 10:00-11:30, room 1055 N
Time Exercise: Fri, 12:15-13:45, room 1057 N;
Please register with LectureReg
Examine: Wed, 01.08.12, 10:00 - 12:00 Uhr, 2045N
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
  • 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.

News

  • [01.03.2012] Page creation.
  • [01.08.2012] You can inspect your exams on Monday, August 6, 2012 at 11:00 in room 1020N.

Important Comments

  • In order to be admitted to the final exam, students are required to register with the course in LectureReg 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.

Literature

  1. M. Mitchell. Machine Learning. McGraw-Hill Science/Engineering/Math; Chapters 1-8; (http://www-2.cs.cmu.edu/~tom/mlbook.html)
  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.( http://www.cs.berkeley.edu/~daf/book.html )

Online Material

Date Content Slides Exercise Supplementary material
18.04.2012 1-01 Introduction PDF

Machine Learning
20.04.2012 2-01 Introduction to Machine Learning PDF PDF ZIP
25.04.2012 2-02 Concept Learning PDF PDF
2/4.05.2012 2-03 Decision Tree Learning PDF PDF
9/11.05.2012 2-04 Artificial Neural Networks PDF PDF
16.05.2012 2-05 Evaluating Hypotheses PDF PDF
18/23.05.2012 2-06 Bayesian Learning PDF PDF ZIP1, ZIP2, ZIP3, ZIP4, Code, DLLs
30.05.2012 2-07 Computational Learning Theory PDF PDF
01.06.2012 2-08 Instance Based Learning PDF PDF

Data Reduction
06.06.2012 3-01 Quantization PDF PDF
08./13.06.2012 3-02 Dimensionality Reduction Techniques PDF PDF

Computer Vision
xx./xx.xx. 4-01 Feature Detectors PDF PDF
xx./xx.xx. 4-02 Feature Descriptors and Feature Matching PDF PDF
xx.xx 4-03 ObjectDetection
  • Neutral Networks (Rowley)
  • Cascade of Boosted Classifiers (Viola)
  • Pictorial Structures (Felzenszwalb)
PDF
PDF
Object Detection with Discriminatively Trained Part Based Models
xx./xx.xx. 4-04 Image Search with pLSA PDF
PDF
xx.xx. Q & A