COMP-558 Fall 2017: Fundamentals of Computer Vision
Schedule: Wednesday/Friday, 10:05 am - 11:25 am.
Class Room: ENGMC 103
Office: ENGMC 420
Office Hrs: Wednesday, 12:30 to 13:30 or by appointment.
E-mail: siddiqi "at" cim "dot" mcgill "dot" ca
Email: babak.samari "at" mail "dot" mcgill "dot" ca
Office Hrs: TBD
Email: ozan.ciga "at" mail "dot" mcgill "dot" ca
Office Hrs: Thursday, 15:30 to 17:30, TR 3090
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 Matlab as well as a solid theoretical grasp of computer
science algorithms and data structures. Familiarity with other
languages such as Java and C will be an asset. This computer science
background will be necessary to carry out assignments which involve
the design and implementation of computer vision techniques.
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.
McGILL Policy Statements on Academic Integrity
McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (see honest and integrity for more information). 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.
Use of Text-matching software: Instructors who may adopt the use of text-matching software to verify the originality of students' written course work must register for use of the software with the ICS Service Desk and must inform their students before the drop/add deadline, in writing, of the use of text-matching software in a course.
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.
Every student should familiarize themselves with McGill's policies on academic integrity, which are detailed here .
They should read the handbook on student rights and responsibilities which is available here.
Any suspected cases of plagiarism or cheating will be reported to the office of the Dean.
Older Stuff from past versions of this course.
The course is undergoing significant revision this term and newly prepared material will be provided via the mycourses page. The material below is provided mostly for historical purposes.
Student prepared course notes
The following is a list of student prepared course notes, summarizing
aspects of the material presented in past versions of the class, or in reference
material and texts. It is provided here, in uneditted form.
Sample past Student Projects
The following are links to some of the final projects from a previous year. This is from past iterations of the course and is provided for historical purposes only.