Machine perception is a rapidly developing exciting field with a wealth
of applications available as well as those still to come. This course
will cover in depth the mathematics and basic techniques of computer
vision which are widely used in a broad spectrum of modern applications.
If you have ever wondered what kind of methods devices like Google
glasses, Robotic vehicles, Panorama stitching, Photo editing software,
etc., use, this course will address that curiosity and more. At the end
of this course, the student is expected to have a grasp in the following
topics: (i) Basic image processing techniques, (ii) Image derivatives
and edges, (iii) Model fitting, (iv) Local descriptors, (v) Stereo
vision, (vi) Subspace methods for recognition, (vii) Object detection,
(viii) Object recognition, (ix) Basics of motion. The course is composed
of (i) the lectures in which we will cover the relevant theory and (ii)
exercises in which the students will implement the basic techniques and
solidify the theory.