This course is an introduction to fundamental and advanced topics in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications.
This schedule is preliminary and subject to change as the term evolves.
| # | Date | Topic | Reading | Assignments |
|---|---|---|---|---|
| 1 | Sep 5 | Introduction pdf | ||
| Image Processing | ||||
| 2 | Sep 10 | Image Processing I pdf | Szeliski 3.2, 3.5 | HW1 Out |
| 3 | Sep 12 | Image Processing II pdf | Szeliski 3.4 | |
| Cameras and Physics | ||||
| 4 | Sep 17 | Image Formation | Szeliski 2.1, 2.2, 2.3 | |
| 5 | Sep 19 | Image Sensing | Szeliski 2.1, 2.2, 2.3 | |
| 6 | Sep 24 | Radiometry and Reflectance | Szeliski 2.2 | HW1 Due, HW2 Out |
| Recognition and Matching | ||||
| 7 | Sep 26 | Grouping: Edges and Boundaries pdf | Szeliski 4.1, 4.2, 4.3 | |
| 8 | Oct 1 | Feature Descriptors pdf | ||
| 9 | Oct 3 | Matching and Image Alignment pdf | ||
| 10 | Oct 8 | Learning-based Vision I pdf | Goodfellow 6, 6.5 | HW2 Due, HW3 Out |
| 11 | Oct 10 | Learning-based Vision II pdf | ||
| 12 | Oct 15 | Object Recognition pdf | ||
| 13 | Oct 17 | Object Recognition pdf | ||
| 3D Vision | ||||
| 14 | Oct 22 | Stereo pdf | HW3 Due, HW4 Out | |
| 15 | Oct 24 | Geometry and Structure from Motion pdf | ||
| 16 | Oct 29 | Photometric Stereo pdf | ||
| 17 | Oct 31 | Learning 3D pdf | Project Proposal Due | |
| - | Nov 5 | Academic Holiday | ||
| Video | ||||
| 18 | Nov 7 | Motion and Optical Flow pdf | HW4 Due | |
| 19 | Nov 12 | Object Tracking pdf | HW5 Out | |
| 20 | Nov 14 | Activity Recognition pdf | ||
| 21 | Nov 19 | Vision and Sound pdf | ||
| - | Nov 21 | Academic Holiday (Thanksgiving) | ||
| Frontiers | ||||
| 22 | Nov 26 | Self-supervised Learning pdf | HW5 Due | |
| 23 | Nov 28 | Negative Results pdf | ||
| 24 | Dec 3 | Bias and Ethics pdf | ||
| 25 | Dec 5 | Project Presentations slides | ||
| 26 | Dec 10 | Project Presentations slides | Project Report Due | |
We do not require a textbook. However, you may find the following books are useful resources:
We gratefully acknowledge several instructors for course material and slides: Shree Nayar, Antonio Torralba, William Freeman, Deva Ramanan, Kristen Grauman, Alyosha Efros, James Hays, Fei-Fei Li, Jia Deng.