Professors :
Marc Pollefeys, Siyu Tang, Fisher Yu
Teaching assistant :
- CVG:
- VLG:
Lectures :
Exercises :
Catalog :
Computer Vision (following Tomaso Poggio, MIT): Computer Vision, formerly an almost esoteric corner of research and regarded as a field of research still in its infancy, has emerged to a key discipline in computer science. Vision companies have emerged and commercial applications become available, ranging from industrial inspection and measurements to security database search, surveillance, multimedia, and computer interfaces. Computer Vision is still far from being a solved problem, and most exciting developments, discoveries, and applications still lie ahead of us. Understanding the principles of vision has implications far beyond engineering, since visual perception is one of the key modules of human intelligence.
Important: course is managed through moodle :
All the materials for lectures and exercises will be posted on moodle. You will also have to submit the exercises there. If you have questions, a forum is available. You can also ask private questions to TAs through the moodle system. Please, don't email questions unless it's very urgent.
Course Objectives :
The objectives of this course are: - To introduce the fundamental problems of computer vision. - To introduce the main concepts and techniques used to solve those. - To enable participants to implement solutions for reasonably complex problems. - To enable participants to make sense of the computer vision literature.
Course Topics :
Introduction and pinhole model, Feature extraction, Optical flow, Deep learning for CV: BP/MLP/CNN/RNN/Transformer/GCN, Image recognition (part-based models, BoW, sliding window, CNN-based), Image segmentation (k-means, Markov Random Fields, Graph Cuts, CNN-based), Object detection, Object tracking, Camera models and calibration, Multi-view geometry and SfM, Model fitting, Stereo and MVS.
Target Audience :
The target audience of this course are Master's Degree students who are interested in getting a basic understanding of computer vision.
Requirements :
Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, basic programming skills in Python.
Some useful links :
- The Computer Vision Homepage
- CVonline
- Middlebury Stereo Vision Page
- VLFeat SIFT package for MATLAB
- STUD-IDES
- Course Notes
- Computer Vision: Algorithms and Applications