Time: Mondays and Wednesdays 10:00-11:35am
Room: ENGTR 2100
Instructor: Tal Arbel
Office Hrs: Mondays 2:00-3:00pm or by appointment
Grader: Pierre-Luc Bacon
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.
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
The course will describe a wide variety of difficult and open problems in computer vision. These include (but are not limited to): shape-from-shading, stereo vision, object recognition, face classification, active vision, image registration and alignment, and image segmentation. We will explore probabilistic solutions to these problems by studying the following:
· Standard regularization and Bayesian regularization approaches to solving ill-posed vision problems
· Sequential Bayesian methods
· Bayesian inference
· Model-fitting - parametric and non-parametric
· Active data selection
· Information theory
· Pattern recognition
· Markov Random Field (MRF) models
· Principal component analysis and eigenmodels
· David J.C. MacKay, "Information Theory, Inference, and Learning Algorithms".
Some suggested background reading:
· David A. Forsyth, Jean Ponce, Computer Vision: a Modern Approach, Prentice Hall, 2002.
· T.M. Cover and J.A. Thomas, Elements of Information Theory,
Wiley & Sons,
· Stan Z. Li, Markov Random Field Modeling in Image Analysis , Springer-Verlag, 2001.
· Assignments: 20%
· Midterm literature review: 10%
· Final project:
· Document: 30%
· Presentation: 20%
· Final test: 20%
Useful computer vision links:
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