COMP SCI 4022/4122/7022 Computer Vision
An introduction to the theory and applications of computer vision.
The online calendar entry for the undergraduate version of this course is available
here, for the Honours version
here, and for the Postgraduate version
The course has no formal prerequisites, but a knowledge of mathematics as may be gained from first year mathematics is assumed.
Over the last 40 years, researchers in artificial intelligence have endeavoured to develop computers with the capacity to "see" the world around them. This course aims to convey the nature of some of the fundamental problems in vision, and to explain a variety of techniques used to overcome them. Vision is a rapidly evolving area of computer science, and new and emerging approaches to these problems are discussed along with more "classical" techniques. Several assignments will be given to enable the student to gain practical experience in applying these techniques.
Various vision problems are considered, including: feature detection in images, e.g. edge detection, and the accumulation of edge data to form lines; recovery of 3D shape from images, e.g. the use of a stereo image pair to derive 3D surface information; forming image mosaics; video surveillance techniques, e.g. tracking objects in video; motion detection in video images, e.g. counting number of moving objects in a video; recognising and classifying objects in images, e.g. searching a video for a particular object.
Consult the webpage of the current offering for the list of references. Copies of lecture slides will be made available, as well as links to useful references.
There will be 3 assignments worth 100% of the total mark, and no exam. Each assigment will require the student to implement and assess a particular algorithm, and to write a short conference-style paper about it.