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: 2023
Lecture start: Wednesday April 12
Tutorial start: Monday April 17
Time:
Lecture: Wednesdays 10:00 - 12:00 (start at 10:15)
Tutorial: Mondays 10:00 - 12:00
Location: E 1.4 024
exception on Monday May 8: E 1.4 021
exception on Wednesday May 10: E 1.5 029
Registration: Please register in the CMS page of the course: https://cms.sic.saarland/hlcvss23/
Lecturer: Prof. Dr. Bernt Schiele
TA(s): Sukrut Rao (email: sukrut.rao[at]mpi-inf.mpg.de, office hour: Wednesday 9:00 AM - 10:00 AM, E1.4 024)
Soumava Paul (email: spaul[at]mpi-inf.mpg.de)
Amin Parchami-Araghi (email: mparcham[at]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