Overview

This course will cover the essentials of computer vision. It is a graduate-level course of interest to anyone seeking to process image or camera information, or to acquire a general background in issues related to real-world perception and computational geometry.

Activities

The course involves three types of activities:

• Interactive classroom sessions, where students together with the instructor explore the basic mathematical foundations behind a range of popular algorithms. Some of the sessions will take the form of traditional-style teaching, whereas others will be dedicated to brainstorming on challenging open problems.
• Homework assignments will provide an opportunity to deepen the problem solving skills acquired in class.
• A major research-style programming assignment that seeks to enable students to develop computer vision software, while deepening their understanding of the relation of mathematical calculus and the "real world."

Prerequisites

This is an introductory graduate level course. Familiarity with basic statistical concepts (Bayes rule, PDFs, projective geometry, Kalman filters, continuous distributions...) will be helpful for this course, as will be hands-on experience with software development in C or C++ and Matlab. Intro tutorials will be given in Matlab and in the vision library OpenCV. But the most important prerequisite will be creativity and enthusiasm.