next up previous contents
Next: Database Theory Up: Lent Term 2004: Part Previous: Comparative Architectures   Contents

Computer Vision

Lecturer: Dr J.G. Daugman

No. of lectures: 16

Prerequisite courses: Continuous Mathematics, Probability


Aims


The aims of this course are to introduce the principles, models and applications of computer vision, as well as some mechanisms used in biological visual systems that may inspire design of artificial ones. The course will cover: image formation, structure, and coding; edge and feature detection; neural operators for image analysis; texture, colour, stereo, and motion; wavelet methods in vision; interpretation of surfaces, solids, and shapes; data fusion; visual inference and learning; and approaches to face recognition.


Lectures

Objectives


At the end of the course students should

Recommended book


* Shapiro, L. & Stockman, G. (2001). Computer Vision. Prentice Hall.



next up previous contents
Next: Database Theory Up: Lent Term 2004: Part Previous: Comparative Architectures   Contents
Christine Northeast
Thu Sep 4 15:29:01 BST 2003