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.
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.
Total contact hours: 48
Private study hours: 102
Total study hours: 150
Method of assessment
See the library reading list for this module (Canterbury)
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.
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Credit level 7. Undergraduate or postgraduate masters level module.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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