Vision is a complex task that allows the determination of complex features captured from the real world. The data itself is a digitalised format of a continuous function that needs to be processed to provide information from the real world. This is applied within many fields including Robotic Vision,
Image processing of medical data, Processing of information from remote sensing applications as well as industrial/controlled environment. In a series of lectures, we will learn using application based examples the use of image processing and image analysis that allows us to discern information from images. These will form the utilisation of physical and statistical models to understand the origins of the data, which will in turn will allow us to identify and analyse features within an image. The lectures will be accompanied by a series of laboratory exercises where many of these computational models will be designed, implemented and tested in real-world scenarios.
The aims of this module are to:
 give an appreciation of the issues that arise when dealing with images from different applications, including some basic physics of imaging
 provide an understanding of the state-of-the-art methods and techniques for processing visual information
 provide application based understanding of multi-modal image registration
 give hands on experience of designing, implementing and testing image processing algorithms in realistic scenarios
On successful completion of this module, the student should be able to:
Assessed by:
1 design, implement and test advanced image processing algorithms
Continuous Assessment
(Individual effort)
2 write a detailed report on a vision project
3 demonstrate an understanding of the advanced vision methods and computational models
Continuous Assessment
(Individual effort)
Examination, Continuous
Assessment
None
Computational Vision (19339)

None
2 hrs lectures per week, laboratory sessions (1 he per week)
30
 Sessional: 1.5 hour examination (70%), continuous assessment (30%)
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Supplementary (where allowed): By examination only.
Title Author(s) Publisher, Date
1.
Introduction o Applications o Challenges
2.
Image formation o Image acquisition o Instrumentation/Noise/Characterisation
3.
Medical application o Information content of different modalities o Advanced Image Segmentation o Image registration o Image Fusion
4.
Robotic applications o Problem specific application and implementation o Feature detection
5.
Remote sensing o Example led application and implementation o Hyperspectral analysis
6.
Process (Industrial) imaging.

o Example led application and implementation o Multi-modal detection and analysis