Image Analysis with Security Applications - DIGM8440

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Module delivery information

This module is not currently running in 2021 to 2022.


Fundamentals of Image Processing
General introduction to digital image processing; image acquisition, quantisation and representation; Affine transforms; image enhancement techniques: contrast manipulation, binarisation, noise removal (spatial and frequency domain); edge detection techniques; image segmentation: edge-based, region- based, watershed; Hough transform; image feature extraction; advanced image processing: morphological operations, colour image processing, various image transforms (Fourier, wavelet, etc).

Fundamentals of Pattern Recognition
Patterns and pattern classification, and the role of classification in a variety of application scenarios, including security and biometrics. Basic concepts: pattern descriptors, pattern classes; invariance and normalisation. Feature-based analysis. Texture analysis. The classification problem and formal approaches. Basic decision theory and the Bayesian classifier. Cost and risk and their relationship; rejection margin and error-rate trade-off. Canonical forms of classifier description. Estimation of class- conditional distributions; bivariate and multivariate analysis. Euclidean and Mahalanobis distance metrics and minimum distance classifiers. Parametric and non-parametric classification strategies. Linear discriminant analysis. Clustering approaches, and relationship between classifier realisations. Practical case studies. Introduction to non-classical techniques such as neural network classification.

Security Applications and Image Analysis
Signature authentication and analysis, Digital watermarking, Content hidden in Images and Video, Steganography. Image forensics.

Implementation Essentials
Programming and data analysis using MatLab and other software tools as appropriate. Introduction to practical work using MatLab. Students not familiar with Matlab programming will be provided with appropriate introductory material before this lecture.


Contact hours

Total contact hours: 48
Private study hours: 102
Total study hours: 150

Method of assessment

Workshops (50%)
Test (50%)

Indicative reading

• Gonzalez, Rafael C. and Woods, Richard E., Digital Image Processing. 3rd edition. London: Pearson Education. ISBN 978-0131687288
• Solomon Chris and Breckon T. Fundamentals of Digital Image Processing: A Practical approach with examples in Matlab. 2011. Wiley-Blackwell. ISBN 978-0470844731
• Webb, Andrew and Copsey, Keith D. Statistical Pattern Recognition. 3rd edition. Wiley. 2011. ISBN 978-0470682289
• Duda, Richard O. and Hart, Peter E. and Stork, David G. Pattern Classification. 2nd edition. Wiley-Interscience. 2000. ISBN 978-0471056690
• Sencar, Husrev T. and Mamon N. (Eds.). Digital Image Forensics: There is more to a picture than meets the eye. Springer. 2013. ISBN 978-1461407560
• Fridrich J. Steganography in digital media: Principles, Algorithms and Applications. Cambridge Univ Press. 2009. ISBN 978-0521190190
• Gonzalez, Rafael C. and Woods, Richard E., and Eddins, Steven L. Digital Image Processing using Matlab. 2nd edition. Gatesmark Publishing. 2009. ISBN 978-

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1. Understand fundamental techniques for processing of digital images.
2. Understand the fundamentals of pattern classification systems, with particular reference to analysis of images.
3. Understand how image analysis relates to security applications.

The intended generic learning outcomes. On successfully completing the module students will be able to:
1. Demonstrate ability in generating, analysing, presenting and interpreting data.
2. Learn to use ICT.
3. Develop core key skills, such as effective learning, critical thinking and time management.


  1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  2. The named convenor is the convenor for the current academic session.
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