Course Outline
Fundamentals of Computer Vision      COMP 558
Fall 2010

              
Instructor:    Professor Michael Langer
Office:        McConnell Engineering, rm. 329
Tel:             514-398-3740
Email:          langer@cim.mcgill.ca
Office Hours:           MW 1-2 pm
Teaching Assistants (T.A.):
        Florian Shkurti       florian@cim.mcgill.ca
        Chatavut Viriyasuthee     pvirie@cim.mcgill.ca
Office (both):                                ENGMC 411
Office Hours:                     (by appointment)

Overview

Computer vision involves the development of algorithms and software that 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 in fact enormous. The field of computer vision has grown steadily over the past few decades, and has advanced to the point where a set of core algorithms and techniques now exist for solving specific vision problems in constrained settings. 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.   Students will become familiar with the basic theoretical and practical tools of computer vision, which would be needed to carry out research or to find employment in this field.

Course Contents

The course in Fall 2010 will consist of three parts. Lectures will cover most of the specific topics below.

Part 1:   Image Formation
Part 2:  Image Analysis 
Part 3:   3D Vision

Official Course Description from McGill Calendar

Biological vision, edge detection, projective geometry and camera modeling, shape from shading and texture, stereo vision, optical flow, motion analysis, object representation, object recognition, graph theoretic methods, high level vision, applications.

Note: this description applies to previous version of the course taught by Prof. Kaleem Siddiqi. While there is a large overlap between these topics and those covered this year, there are significant differences as well. This year, in particular, we will not be covering object recognition, differential geometry of surfaces. These topics will be covered in Prof. Siddiqi's 7xx course offered in the Winter 2011.

Prerequisites

Students should have a solid background in basic Calculus, Linear Algebra, Algorithms and Data structures, and Programming.    The official prerequisites for the course are  COMP 206COMP 360MATH 222,  MATH 223 (or equivalent). It is strongly recommended that students only take this course if they received A grades in MATH 222 and 223 (or equivalent).

Students who do not have these prerequisites may still be allowed to take the course, but only with the permission of the instructor.  
 
Lecture Notes, Readings, Textbooks

The material covered in the lectures will be available in the form of Lecture Notes written by the instructor and posted as PDFs on the course web page, or as handouts.  Readings also will be made available typically in electronic form.  

There is no required textbook for the course. There are, however, several good texts out there and students are encouraged to consult with them. See for example Richard Szeliski's online book Computer Vision: Algorithms and Applications

 There are also textbooks available on reserve in the Schulich Science and Engineering library.
  • ''Introductory Techniques for 3D Computer Vision'', by Emanuele Trucco and Alessandro Verri, Prentice-Hall, 1998.   
  • ``Three-Dimensional Computer Vision: A Geometric Viewpoint'', by Olivier Faugeras, MIT Press, 1996.  
  • ``Robot Vision'', by Berthold Horn, MIT Press/McGraw-Hill, 1986. 
  • ``A Guided Tour of Computer Vision'', by V. Nalwa, Addison-Wesley, 1993.
  • ``Computer Vision: A Modern Approach'', by David Forsyth and Jean Ponce, 2003. 
  • Evaluation

    B.Sc. students must achieve a grade of 55 (C) to pass this course.   M.Sc. and Ph.D. students must achieve a grade of 65 (B-).
    The midterm will take place during a lecture slot and will be 80 minutes long.  It will occur approximately midway through the semester and will cover approximately the first third of the lecture material.    The Final Exam will take place during the Final Exam period and will cover the remainder of the lecture material. 

    In accord with McGill University's Charter of Students' Rights, students in this course have the right to submit in English or in French any written work that is to be graded.


    Academic Integrity

    McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures. See here for more information