COMP-558 Winter 2012: Fundamentals of Computer Vision

Schedule

Instructor: Kaleem Siddiqi
Schedule: Monday/Wednesday, 13:05 pm. - 14:25 pm.
Class Room: ENGTR 2100
Office: ENGMC 420
Office Hrs: Wednesday, 13:30 pm. - 14:30 pm. (or by appt.)
E-mail: siddiqi "at" cim "dot" mcgill "dot" ca
TA: Emmanuel Piuze-Phaneuf
Email: emmanuel.piuze-phaneuf "at" mail "dot" mcgill "dot" ca
Office Hrs: Tuesdays 13:30 - 15:30, Trottier 3060
Emmanuel's COMP 558 web page .

Overview

Computer vision involves the development of machine algorithms which have the potential to mimic a biological organism's ability to ``see''. Though the sense of vision is immediate for most people, the complexity of the task that the human visual system accomplishes is enormous. In general, the inference of properties of the three-dimensional world from two-dimensional images is very challenging.

Nevertheless, this has been a rich area for investigation for the past several years. To date the field has advanced to the point where a core of algorithms and techniques have been developed for specific visual tasks in constrained settings, with a solid mathematical foundation. Several international research laboratories now exist and applications of computer vision techniques in industry, robotics, and bio-medicine abound. This course seeks to present the fundamentals of computer vision at an advanced undergraduate/beginning graduate level.

The mathematical background for the course includes calculus and linear algebra. Fundamentals of other areas, such as linear systems and partial differential equations, will be covered as necessary. It will also be assumed that students have practical experience with programming in a Unix environment using C, as well as a solid theoretical grasp of computer science algorithms and data structures. This computer science background will be necessary to carry out assignments and projects which involve the design and implementation of computer vision techniques.


Calendar


News

  • Welcome to the COMP 558 Winter 2012 web page. Check here for regular updates.
  • The course pack has been ordered and should be available at the McGill bookstore (basement level) later this week.
  • Assignment 1 is now available here. It is due on Feb 9th, not Feb 14th as erroneously stated on the PDF file!!
  • Please note that Emmanuel's office hours are cancelled today, January 31st, because he is not feeling well. We will schedule extra hours as soon as he feels better.
  • There will be no class on Monday Feb. 27th. Assignment 2 will be posted before study break in case you wish to get started on it. Please don't forget to submit a 1 page project proposal so that we can evaluate it and get back to you.
  • Assignment 2 is now available here. Note that in the textbook, Figure 9.3, alpha=tau is the angle in the XY plane between the project ion the vector P onto the XY plane (dashed diagonal line) and the X axis and sigma=beta is the angle between the vector P and the Z axis. The presentation in the text is a bit confusing on this point.
  • Assignment 3 is now available here.
  • The inclass midterm will be held on Monday April 2nd during class hours.
  • A review sheet detailing topics on the midterm is available here.
  • Midterm marks are now posted on webct. A few students still need to take a "make-up" midterm so I would prefer not to discuss your marks with you until after Wednesday. Class average on this midterm was a respectable 77.

  • Content

    The course will cover a number of topics ranging from low level to high level vision, with a focus on both the mathematical formulation of vision tasks, and the development and implementation of algorithms to solve them. Lecture topics, subject to revision, are listed below.
  • biological vision and early vision
  • linear systems and convolution
  • image formation and features
  • projective geometry and camera modeling
  • shape from shading and texture
  • stereo vision
  • motion analysis and optical flow
  • object representation and recognition
  • pdes, level set and variational methods

  • Student prepared course notes

    The following is a list of student prepared course notes, summarizing aspects of the material presented in class, or in reference material and texts. It is provided here, in uneditted form.
  • Biological Vision (pdf) James Yap
  • Biological Vision (pdf) Louis Simard
  • Linear Systems and Convolution (pdf) Simon Wong
  • Shape From Shading (pdf) Craig Jerusalim
  • Shape From Shading (pdf) Mani Ghasemlou
  • Stereo (pdf) Andrew Chang
  • Stereo (pdf) Daniel Scheidig
  • Optical Flow (pdf) Irina Kezele
  • Motion (doc.gz) (pdf) Karl Nyberg
  • Motion (html) Malvika Rao
  • Object Representation (pdf) Olivier Dubois
  • Recognition (pdf) Mathieu Lamarre

  • Useful Links

  • The Computer Vision Home Page
  • Annotated Computer Vision Bibliography
  • CVonline Compendium of Computer Vision

  • Prerequisites

  • MATH-222 (Calculus III)
  • MATH-223 (Linear Algebra)
  • COMP-206 (Programming Techniques)
  • COMP-360 (Algorithm Design Techniques)

  • Text Books

  • Introductory Techniques for 3D Computer Vision'', by Emanuele Trucco and Alessandro Verri, Prentice-Hall, 1998. ON RESERVE (Selected chapters will be made available in a course pack).
  • "Three-Dimensional Computer Vision: A Geometric Viewpoint'', by Olivier Faugeras, MIT Press, 1996. ON RESERVE
  • "Computer Vision: A Modern Approach'', by David Forsyth and Jean Ponce, 2003. ON RESERVE
  • "Computer Vision: Algorithms and Applications'', by Richard Szeliski, Springer, 2010. PDF available here.

  • Evaluation

  • Assignments: 40%
  • Course Project: 30%
  • Mid-term Examination: 30%
  •        Student Guide to Avoid Plagiarism


    Sample past Student Projects

    The following are links to some of the final projects from a previous year, in cases students made their presentations or material available over the web.
  • Marr & Poggio's Cooperative Stereopsis (ps.gz). Mani Ghasemlou
  • Automatic Camera Tracking From An Uncalibrated Motion Sequence. Toulouse de Margerie
  • The Perception of Apparent Motion. Craig Jerusalim
  • SURF: Speeded-up Robust Features. Anqi Xu and Gaurav Namit
  • Hair Modeling using Generalized Helicoids. Emmanuel Piuze. (Also see the Computer Graphics Forum paper , which appeared later.)
  • Bayesian Video Matting Using Motion Based Segmentation Dante Denigris and Collm Elliott