CSC2503: Foundations of
Computational Vision (Fall 2018)
About the Course
This course offers an in-depth, graduate-level introduction to computer vision. Topics covered include the following: (1) Camera system geometry, geometric transformations, multi-view geometry, projective and metric reconstructions. (2) Image acquisition, scene lighting and reflectance models. (3) The robust estimation of edges, lines, and regions. (4) Image matching and the estimation of motion in image sequences. (5) Advanced topics in visual inference, including Markov random fields and deep learning for computer vision.
All course materials (lecture slides, course notes, research papers, tutorial notes, assignments) will be available in the course dropbox.
In addition to the lecture slides and course notes, students are expected to read selected chapters from Simon Prince’s computer vision book and (much less frequently) Forsyth and Ponce’s computer vision book (1st edition). I am placing a third book, by Rick Szeliski, on dropbox as well. I will not be assigning chapters from it but it's a nice book to read if you want to get a more application-oriented view of computer vision.
Students taking the course are expected to be comfortable with elementary probability and statistics, linear algebra, elementary geometry, and vector calculus (including partial differentiation). It is also assumed that students are comfortable with programming and will be expected to rapidly learn to use Matlab or analogous python-based tools.
There will be 3 assignments given during the term for a total weight of 55%. Each assignment includes a programming portion (in matlab) and a theory portion (written report).
The two-hour final exam will be worth 45% of the course grade.
The tentative schedule for the assignments is as follows:
Weight distribution for the assignments will be 18.3% each.
Late policy: there will be a 10% marks deduction for each day late, for up to three days. Deductions begin immediately after the due date & time (ie. after 11:59pm on the due date). For example, an assignment submitted late but less then 24hrs after the due date will receive a 10% deduction. Assignments submitted more than 72 hours late will not be accepted.
How to make the most out of this course
Cheating on assignments has very serious repercussions for the students involved, far beyond simply getting a zero on their assignment.
All students taking this course should be aware of the following:
Tentative Lecture Calendar
Sept 19: Lecture 1
Sept 26: Lecture 2
Fundamentals of model fitting (continued): Least squares, total least squares, robust estimation
Oct 3: Lecture 3
Geometric computer vision I: perspective projection, homogeneous coordinates, camera models & transformations, homographies, two-view geometry & the fundamental matrix
Oct 10: Lecture 4
Geometric computer vision II: structure from motion
Oct 17: Lecture 5
Image filtering: linear filters, Fourier transforms, pyramids & multi-scale image transforms, robust filters
Oct 24: Lecture 6
Oct 31: Lecture 7
Nov 14: Lecture 8
Motion analysis II: tracking
Guest lecture by
Prof. David Fleet
Nov 21: Lecture 9
Advanced Image Inference I: Deep learning for computer vision
Guest lecture by
Dr. Marakand Tapaswi
Nov 28: Lecture 10
Advanced Image Inference II: Markov random fields