Time: MWF 11:35-12:25pm
Room: Wong 1030
Instructor: Tal Arbel
Teaching Assistant: TBA
Office: MC-425
Office Hrs: Mondays 2:30-3:30pm or by appointment
Phone: 398-8204
Course description:
Computer vision has always been concerned with solving ill-posed problems. For example, given an image of a scene, we are asked to determine the underlying structures that generated it. The problem is difficult in that information is lost when the three dimensional world is projected onto a two dimensional image. As a result, different scenes could give rise to the same image. To illustrate the point, consider the example below. On the left, you can see a toothpaste tube placed on a table. On the right is one particular 2D image of it acquired from a camera at a particularly unfortunate viewpoint - facing the top of the tube. Note that several different objects could give rise to that same image. Identifying the toothpaste tube from that image is difficult even for humans.
In this course, we explore the conjecture that the world is uncertain and
therefore should be described through the language of probabilities. We will
describe several standard problems in computer vision and explore
probabilistic inference methods for their solution. The goal is to provide the
student with the necessary tools to be able to apply these techniques to other
computer vision research problems and to many other areas.
Background requirements
This course is designed for graduate students pursuing interests in the areas of computer vision, robot vision and artificial intelligence (e.g. machine learning, decision-making). It is not meant as an introductory course in computer vision and, as such, does not provide a broad overview of the field. That being said, we will describe and address a wide range of problems in computer vision, but the particular focus will be on solutions based on probability and information theory.
The course assumes some mathematical background in probability and statistics, linear algebra, and calculus. Students should be familiar with basic techniques in image processing and optimization. Finally, students should be comfortable programming in Matlab.
Topics to be covered: (subject to change)
The course will describe a wide variety of difficult and open problems in computer vision. These include (but are not limited to): optical flow estimation, shape-from-shading, stereo vision, object recognition, pose estimation, active vision, image registration and alignment, and face recognition. We will explore probabilistic solutions to these problems by studying the following:
Course schedule (tentative)
Reading material
Readings will include journal and conference papers in computer vision as well as sections from the following textbook:
Some suggested background reading:
Grading: (subject to change)
Useful computer vision links:
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 www.mcgill.ca/integrity for more information).