|Delivery length / details
|20 x 1 Hour Lectures
|2 x 2 Hour Workshops
|Assessment length / details
|2 Hours Written Exam
|2 Hours Supplementary Exam Will take the same form, under the terms of the Department's policy.
On successful completion of this module students should be able to:
1. Express a consolidated and extended understanding and knowledge of Computer Vision techniques.
2. Compare, critically evaluate and discuss competing methods.
3. Explain the problems, techniques and difficulties associated with the different areas of Computer Vision.
The module will introduce the subject of Computer Vision in the context of robotics, in particular mobile and industrial robotics. It will start with low-level vision such as edge detection, feature detection, and segmentation. Intermediate vision will describe various techniques to infer 3 dimensional information from images. Some high-level techniques will be introduced
Edges and features: The image as landscape, edge detection, feature detection and representation, appearance as feature.
Motion: The video as a 3D dataset, feature tracking, background subtraction, modelling motion and change.
Objects: Grouping features, grouping motion, modelling variability. Learning models.
3D: Shape from X (shading, defocus, occlusion, photometric stereo). Multiview techniques (binocular, structure from motion). Direct 3D capture techniques (lidar, sonar).
|Application of Number
|Computer vision involves higher level mathematical concepts
|Exam writing develops written communication skills
|Improving own Learning and Performance
|Independent learning is necessary to complete the module
|Information and communications technology is intrinsic to computer science.
|Personal Development and Career planning
|There is a substantial demand for computer vision expertise.
|Problem solving is intrinsic to computing in general.
|This is a research driven module; research skills will be exercised throughout
|Subject Specific Skills
|This module will require individual rather than team work.
This module is at CQFW Level 6