You can use our forum to find teammates at this link.
Groups of 3 are strongly recommended!
In the first tutorial session, you will receive all necessary information about how to register your team using the CMS webpage.
Materials and videos for this course will be available at this link.
High Level Computer Vision
This course will cover essential techniques for high-level computer vision. These techniques facilitate semantic interpretation of visual data, as it is required for a broad range of applications like robotics, driver assistance, multi-media retrieval, surveillance etc. In this area, the recognition and detection of objects, activities and visual categories have seen dramatic progress over the last decade. We will discuss the methods that have lead to state-of-the-art performance in this area and provide the opportunity to gather hands-on experience with these techniques.
Lecture start: Wednesday April 14
Tutorial start: Monday April 19
Time and Location:
lecture: Wednesdays 10:00 - 12:00 (start at 10:15) Will be held over zoom
tutorial: Mondays 10:00 - 12:00 Will be held over zoom
Lecturer(s): Prof. Dr. Bernt Schiele
TA(s): Farzaneh Rezaeianaran
- "Computer Vision: Algorithms and Applications" by Richard Szeliski (in particular chapter on image formation)
- "Probabilistic Topic Models" by Mark Steyvers, Tom Griffiths
- Mikolajcyk, Schmid: A Performance Evaluation of Local Descriptors, TPAMI, 2005
- Boiman, Shechtman, Irani: A Performance Evaluation of Local Descriptors, CVPR, 2008
- Gehler, Nowozin: On feature combination for multi class object classification, ICCV, 2009
- Krizhevsky, Sutskever, Hinton: ImageNet Classification with Deep Convolutional Networks, NIPS, 2012
- "Pattern recognition and machine learning" by Christopher M. Bishop
- "Computer vision" by David A. Forsyth and Jean Ponce