Final Grades.  I Emailed grades, along with project comments.
If you have not received an Email, or I want to discuss grades or projects, please get back to me.

EOT/Project notes:

Office hour, this week Wednesday 3 April 3:30--5pm, DC2510.  Feel free to drop by to discuss projects.
I'll probably have another office hour on Friday as well, stay tuned...

Office hour, this week Monday 1 April 3-5pm, DC2510.

Asst#4 help.

Due date extended, to Thursday this week.
Sorry, there was a problem with my code and images below.
(Images should be typecast to "double" after reading them in.)

I'm going to give a tutorial (myself) on Monday, to help with the DP part.  Monday 5-6pm, DC2306 (AI lab).

Last lectures: I will give two more lectures next week on "object recognition".  The final week will be guest lecturees + discussion (see schedule below).  By the way, if anyone wants to give a presentation of their project (progress so far), you are welcome to take ten or fifteen minutes during the last two lectures. If you wish to do this, contact me at least a day before lecture.  Thanks.

Tutorial: Today (Wed) 5-6pm, DC2306C (AI lab).

Asst#4 posted. Here (Corrected, Sun. Mar. 17, 20:00, further corrected Fri 22 March) Due: Tues 26 March (in class).

Asst#3, Deadline extended to Friday 15 Mar, 17:00, my office (DC2510).
Another day, for those who are "a day late and an hour behind"!

Tutorial: Wed 13 March, 5-6pm, DC2306C.

Assignment #3.  *Deadline extended*  Thurs March 13 (in class).

Fourier methods for motion estimation (Lecture of March 7, 2013).  I probably confused many of you in this lecture!  I will repeat this material in Tuesday's lecture.

Tutorial (for Asst#3): Thursday, 5-6pm, DC2306C (AI lab).

Interim report.  Due: Thursday 7 March, in class.  Please submit two or three pages (hardcopy).  Briefly describe project and show some evidence of progress (eg., a sample run of your program or a part of it).  I don't need details, just some evidence that you are progressing.  The tutor and I will read the reports and get back to you with comments if necessary.  You can also visit during office hours if you would like help...

Asst#3 posted. Here (March 2nd @ midnight) Sorry for the delay!
The derivation of LSQ and mixtures for Asst#3 is a bit different than for the lines I presented in class.  I will review the derivation in Tuesday's lecture.

Will go out Friday March 1st.  *Deadline extended* Thurs March 13 (in class).

Next Tutorial: Monday March 4 (help with Asst#3).

Reading Week (and after):

Scheduling notes:

Assignment #2. Out: 10 Feb 2013.  Due: Tues 26 Feb, 13:00 (at lecture), hardcopy (written or typeset): Linear Systems and Feature Detection (tgz)  Matlab filtering code (tgz).  (Demo filtering code, needed for Q1 and Q2, run Asst#2 files in this directory.)  Feel free to experiment with filtering code.  Type "help tutorials".  Then cut and paste tutorials in a Matlab window.

Notes (Sat 9 Feb):

Office hour, Feb 8th (Friday) office hour cancelled.  Contact me by Email if necessary.

Features (corners, SIFT).  Images for corners uploaded, see below.

Books on Reserve: 
Nalwa, Marr, Szeliski, Horn, Trucco and Verri, all in Davis Center, under course number "CS484".

Matlab Demo code (filtering, Fourier):
  I've just uploaded my demo code.  In case you want to follow along. See Lecture list below.

Asst #1, Q3a.  Correction.  Please compute only the illuminant direction (I).  Do not compute the albedo (rho).  You can only determine the albedo up to a scale factor.

Project:  As I mentioned in class, I'm looking for a specific algorithm, either from a paper or from Szeliski or some other textbook.  If you cannot find a project, here are some suggestions (chapter/section #'s are listed from Szeliski's book):

Tutorial (help with Asst#1):
Mon Jan 28th, 5-6pm, DC2306C (AI lab).

Course notes added, see lectures below.

Assignment #1:
Image formation and lighting (tgz).  ZIP (Windows) version.  Out: Fri. Jan 18th.  Due Tues. Jan 29th, 13:00 (at lecture).  Note: You will need Tuesday's lecture to do Q3.

Kinect sensors:

Office hours:

Notes after second lecture:

Notes after first lecture:


Time: Winter 2013; Lectures T,Th 13:00-14:20, MC4058.  First lecture, Tues 8 January.  Office hours TBA.  Tutorial time(s) TBA.

Instructor: Richard Mann, DC2510, x33006,,

Audience: This course is intended for advanced undergraduate and beginning graduate students interested in pursuing research in AI, Vision or related areas.  Students should expect to do a fair amount of independent study, both in following the material and completing a course project.

The grades are based on a small number of assignments (4) and a project.  For the project, students will choose a vision problem or application, implement one or more algorithm(s), and prepare a final report.  The grades awarded will depend on: 1) the difficulty of the problem selected, 2) the implementation effort, 3) experimentation/testing and the quality of the written report.  The report should provide sufficient experiments and analysis, in particular, situations where the algorithm(s) work and where they fail.

Grading (CS484) 60% assignments, 40% project.  Assignments will typically have a both a written and a (small) programming component.  A moderate level of experimentation will be required on each assignment.

: Graduate students will take on a more advanced project, either from a recent publication, or vision-related topics from their thesis area.  Graduate grades will be: 40% assignments, 60% project to account for the larger project.

Presentation: This is a lecture-based course.  Material will be presented on the blackboard and supplemented with images and video.  For those who do not wish to copy notes, I will appoint a note taker who will make lectures available after each lecture.

Software: Matlab will be used for assignments, and is encouraged for the project.  Matlab is available in the Undergrad Computing Enviroment.  Matlab is an interactive language, with a C-like syntax, that is optimized for numerical computation and plotting/graphics.

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.

References:  All required material will be provided in lectures. Possible references include:

New book, well presented, lots of computational details.  Good source of algorithms for projects.

Comprehensive treatment of many areas of compuational vision. A good resource for background reading and choosing project ideas.  Noteable omission: optical flow.

Classic text.  Still recommended reading showing the origins of many vision problems.

Excellent reference on signal processing and Fourier analysis.  Very accessible to both Computer Scientists and Engineers.

I will put all of these books on reserve in the library (DC). You may also purchase Trucco and Verri's book in the University bookstore.

General Information:


(Student lecture notes are provided as a courtesy; they are not a substitute for attending lectures!)

Reference material:

Course software:

Project ideas:

Additional References (to be updated):

  • Wearcam Steve Mann's webpage on wearable computers and cameras.