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 May 6
Tutorial start: Monday May 11
lecture: Wednesdays 10:00 - 12:00 (start at 10:15)
tutorial: Mondays 10:00 - 12:00
announced on the email list
Mailing List: send an email with your matriculation number and full name to firstname.lastname@example.org with [hlcv-subscribe] in the subject.
Exam: October 27 / 28
Registration: send an email to email@example.com with [hlcv-exam registration] in the subject.
TA(s): Rakshith Shetty (office hour: Tuesdays 15:30 -17:30 E 1.4 room 628)
- "Computer Vision: Algorithms and Applications" by Richard Szeliski (in particular chapter on image formation)
- 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