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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: Summer Semester
Year: 2025
Lecture: Wednesdays 10:00 AM - 12:00 PM (start at 10:15 AM)
Tutorial: Mondays 10:00 AM - 12:00 PM (start at 10:15 AM)
Location: E1.5 002
First Lecture: Wednesday April 9, 2025
First Tutorial: Monday April 14, 2025
Registration
Please register for the course in the CMS. If there are any issues, please write to hlcv-ss25@mpi-inf.mpg.de.
Lecturer: Prof. Dr. Bernt Schiele
TAs: Amin Parchami, Nhi Pham, Sophia Wiedmann, Mostafa Abdelgawad
Office Hour: Tuesday 10:00 AM - 11:00 AM, E1.4 629
Contacting TAs: hlcv-ss25@mpi-inf.mpg.de (please only use this email ID for all email communication with the TAs) or the Forum.
Literature:
- "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