Clayton First semester 2008 (Day)
This unit aims to develop an understanding of methods for extracting useful information (eg 3-D structure; object size, motion, shape, location and identity, etc) from images. It will allow students to understand how to construct Computer vision systems for robotics, surveillance, medical imaging, and related application areas.
To understand camera models
To learn to apply geometry and photometry to image analysis.
To understand the basic principles of laser scanners.
To understand the elements of the human visual system and perception.
To learn to implement low level vision processes (linear filtering, edge detection, texture, multi view geometry, stereopsis, structure from motion, optic flow).
To learn to implement midlevel vision (segmentation and clustering, model fitting, tracking).
To learn to implement high-level vision (model-based vision, surfaces and outlines, graphs, range data, templates and classifiers, learning methods).
To complete programming exercises (C, MatLab for example).
Continuous assessment: 30%
Examination: (3 hours) 70%Students must achieve a mark of 45% in each of these two components to achieve an overall pass grade.
2 hours lectures, 4 hours laboratory and practice classes and 6 hours of private study per week
ECE4711, ECE4712, ECE5076, ECE5711, ECE5712