Image Analysis & Applications - EENG5610

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 5 15 (7.5) Sanaul Hoque 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: 33
Private study hours: 117
Total study hours: 150

Method of assessment

Examination (80%)
Coursework (20%)

Indicative reading

• Fairhurst, Michael Christopher (1988) Computer vision for robotic systems: an introduction, Prentice Hall, London, New York.
• Solomon, Chris (2011) Fundamentals of digital image processing : a practical approach with examples in Matlab. Wiley-Blackwell.
• Duda, Richard O.; Hart, Peter E.; Stork, David G. (2000) Pattern Classification, John Wiley and Sons.
• Picton, Phil. (2000) Neural Networks. 2nd edition. Palgrave, Basingstoke.
• Graupe, Daniel. (2019) Principles of Artificial Neural Networks – Basic Designs to Deep Learning. 4th Edn. World Scientific Publishing.
• Jain, Anil, Ross, Arun, Nandakumar, Karthik. (2011). Introduction to Biometrics. Springer.
• Jain, Anil, Flynn, Patrick, Ross, Arun (eds.). (2008). Handbook of Biometrics. Springer.

See the library reading list for this module (Canterbury)

Learning outcomes

1. Have a knowledge of the main methods of three principal integrated themes:
(i) 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. Knowledge and critical understanding of algorithms underpinning modern image analysis systems.
3. Have experience and critical understanding of the requirements for implementing algorithms for image analysis.
4. Have practical experience of working with a range of 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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