CS 787 - Computational Vision

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.


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Summary

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.

Prerequisites

There are no formal prerequisites for this course, however, it is advisable to have some exposure to numerical computation, especially linear algebra (eg., CS370), and some basic programming experience. Programming will be done in Matlab.
Note: this course is open to undergraduates, with permission of the instructor.

Marking (tentative)

References

All required material will be provided in lectures. Possible references include:
Comprehensive treatment of most areas of vision. A good resource
for background reading and choosing project ideas.
I will put all of these books on reserve in the library. You may also purchase Trucco and Verri's book in the University bookstore.

Lecture Schedule (preliminary)

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



Assignments

Assignment text (PDF), complete package (tar, zipped).

Due: Wed, 13 Oct, 2004, 5:00pm


Notes:
All students registered for the course, grad and undergrad, should have accounts here.  If you don't have course accounts please visit MC3011 (bring your "Watcard").

Assignment text (PDF), complete package (tar, zipped).
Due: Wed, 10 Nov, 2004, 5:00pm

Notes:

Assignment text (PDF), complete package (tar, zipped).
Due: Wed, 24 Nov, 2004, 5:00pm
Notes:




Additional Reference Material


Resources