Lecturer: Dr John Daugman Taken by: Part II Prerequisite course:
Mathematical Methods for CS; Probability 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 for visual
coding and analysis; interpretation of surfaces, solids, and
shapes; data fusion; probabilistic classifiers; visual inference
and learning; and face recognition. - Goals of computer vision;
why they are so difficult. How images are formed, and the ill-
posed problem of making 3D inferences from them about objects
and their - Image sensing, pixel arrays, CCD cameras. Image
coding and information measures. Elementary operations on image
arrays. - Biological visual mechanisms from retina to cortex.
Photoreceptor sampli