Fall 2004
Department of Computer Science
University of Waterloo
Instructor: Richard Mann, DC2510, x3006, mannr@uwaterloo.ca
Lectures: Mondays 2:30-5:00, Room DC3313.
First class: Monday 13 Sept, 2004.
Important Note: Please try
to send
all course material from a
University of Waterloo account (ie., SSH into a teaching or
research machine and send from there). If you don't have a
Waterloo account yet, please contact the MFCF help
center (MC3011). In the meantime you can use the account you gave
in the first class. Also please don't send any
messages with "zip" or "gif" attachments (send "tif" or "jpg" instead,
or refer me to a URL). Those are common filetypes in Microsoft
viruses and are removed by my SPAM filter.
Computational vision is concerned with the automatic processing of image and video data for scene reconstruction, object recognition, navigation, and activity detection.
The objective of this course is to provide a concise treatment of some fundamental problems in computational vision. This course will focus on a core set of problems where efficient and robust algorithms can be applied. Students will be required to implement several algorithms using real datasets. In addition to providing practical approaches and algorithms, this course will provide the foundation required to pursue research computational vision.
A concise treatment, focussing on geometric approaches to vision.
The course closely follows this book.
Comprehensive treatment of most areas of vision. A good resource
for background reading and choosing project ideas.
Lecture |
Topic |
References |
1. (Sep 13) |
Vision Overview; Image formation | Horn Ch2, Trucco and Verri Ch2 Related link: - Combining images of varying exposure (S. Mann, Toronto) |
2. (Sep 20) |
Lighting and reflectance models Application: Photometric stereo |
Horn Ch10 Related links: - Lightness and shading illusions (E. Adelson, MIT) - Shape from shading (M. Langer, McGill) |
3. (Sep 27) |
Filtering: Linear systems, Fourier theory Note: did not cover discrete case (finite size images, sampling). This will be covered in Lecture #4. |
Horn Ch6, Castleman Ch 10 |
4. (Oct 4) |
Edge and Feature detection. Application: good features to track (corners) |
Trucco and Verri Ch4 |
(Oct 11) |
*no
class* (Thanksgiving holiday) |
|
5. (Oct 18) |
Parameterized features and model fitting. Application: fitting lines to data using robust and mixture models. |
|
6. (Oct 25) |
Optical flow Application: mixture models for optical flow |
Trucco and Verri Ch8, Horn Ch12 References: - Mixture models for optical flow (A. Jepson, Toronto) - Optical Snow |
7. (Nov 1) |
Structure from motion |
Trucco and Verri Ch8 |
8. (Nov 8) |
Stereo (Baseline case) |
Trucco and Verri Ch7; Faugeras Sect 6.6 |
9. (Nov 15) | Stereo (Epipolar geometry) Application: 3d scene reconstruction |
Trucco and Verri Ch7; Faugeras Ch7 |
10. (Nov 22) |
Object Recognition: View-based approaches. Application: PCA for object recognition |
Trucco and Verri Ch9, |
11. (Nov 29) |
Object Recognition: Model-based approaches. Application: Object search |
Trucco and Verri Ch9 |
12. (Dec 6) |
Computational perceivers. Applications: Scene labelling, perception of scene dynamics |
Assignment text (PDF), complete package (tar, zipped).
Due: Wed, 13 Oct, 2004, 5:00pm
Notes:
- You will need to install the following course software (tar, zipped) in your home directory.
- Oops! You need the following additional files (put in cs787-f04-software/matlab/ directory): pgmRead.m, pgmReadHeader.m, pgmWrite.m
Due: Wed, 10 Nov, 2004, 5:00pm
Notes:
Due: Wed, 24 Nov, 2004, 5:00pm
Notes: