High-Level Computer Vision


Overview
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 led to a state-of-the-art performance in this area and provide the opportunity to gather hands-on experience with these techniques.
Course Information
Semester: SS
Year: 2022
Lecture start: Wednesday April 13
Tutorial start: Monday April 25
Time:
Lecture: Wednesdays 10:00 - 12:00 (start at 10:15)
Tutorial: Mondays 10:00 - 12:00
Location: Hybrid. For more information, please kindly refer to this page: https://cms.sic.saarland/hlcvss22/2/Location
Registration: The registration phase is over. If you still hope to attend this course, please send an email to hlcv-ss22@lists.mpi-inf.mpg.de
Lecturer: Prof. Dr. Bernt Schiele
TA(s): Yaoyao Liu (email: yaoyao.liu[at]mpi-inf.mpg.de, office hour: Wednesday 9:00 AM - 10:00 AM)
Fabian Krause (email: fakr00002[at]stud.uni-saarland.de)
You may send an email to both TAs using this mailing list: hlcv-ss22@lists.mpi-inf.mpg.de
Literature:
- "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