COMPGI14 - Machine Vision

This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Code COMPGI14 (Also taught as: COMPM054 Machine Vision)
Year MSc
Prerequisites Successful completion of an appropriate Computer Science, Mathematics, or other Physical Science or Engineering undergraduate programme with sufficient mathematical and programming content, plus some familiarity with digital imaging and digital image processing.
Term 1
Taught By Gabriel Brostow(100%)
Aims The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.
Learning Outcomes To be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, super-resolution, object recognition, tracking and 3D model building.