Image Analysis & Applications - EENG5610

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Spring Term 5 15 (7.5) Gareth Howells checkmark-circle


The module introduces fundamental techniques employed in image processing and pattern recognition providing an understanding of how practical pattern recognition systems may be developed able to address the inherent difficulties present in real world situations. The material is augmented with a study of biometric and security applications looking at the specific techniques employed to recognise biometric samples.


Contact hours

Total contact hours: 35
Private study hours: 115
Total study hours: 150

Method of assessment

Examination (70%)
Coursework (30%)

Indicative reading

• Fairhurst, Michael Christopher (1988) Computer vision for robotic systems: an introduction, Prentice Hall, London, New York.
• Gonzalez, Rafael C., Woods, Richard E. (2008) Digital image processing, Pearson Education, Pearson Prentice Hall, London, Upper Saddle River, N.J.
• Tarassenko, Lionel, Neural Computing Applications Forum (1998) A guide to neural computing applications, Arnold, John Wiley, London, New York.
• Forsyth, David, Ponce, Jean (2003) Computer vision: a modern approach, Prentice Hall/Pearson Education International, Upper Saddle River, N.J.
• Theodoridis, Sergios, Koutroumbas, Konstantinos (c2009) Pattern Recognition, Elsevier/Academic Press, Amsterdam, London.
• Nixon, Mark S., Aguado, Alberto S., Dawsonera (2008) Feature extraction and image processing, Academic Press, Amsterdam, London.
• Petrou, Maria, Petrou, Costas (2010) Image processing: the fundamentals, Wiley, Chichester.
• Beale, Russell, Jackson, Tom (1990) Neural computing: an introduction, Institute of Physics, Bristol

See the library reading list for this module (Canterbury)

Learning outcomes

1. An understanding of three main integrated themes:
(i) basic image processing (representation, transformation, extraction of key information from images);
(ii) image analysis (automatic interpretation of images and pattern recognition methodology) and
(iii) computational architectures for image analysis (especially neural network structures).
2. A familiarity with fundamental algorithms underpinning modern image analysis systems.
3. Experience of the requirements for implementing algorithms for image analysis.
4. A practical experience of working with typical algorithms and architectures.


  1. Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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