COMP0026 Image Processing

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Image Processing



Module delivery

1819/A7P/T1/COMP0026 Postgraduate

Related deliveries

1819/A6U/T1/COMP0026 Undergraduate

Prior deliveries




FHEQ Level


FHEQ credits



Term 1

Module leader

Agapito, Lourdes


Agapito, Lourdes

Module administrator

Horslen, Caroline


This module focuses on digital image processing. It first introduces the digital image, with a description of how digital images are captured and represented. It then goes on to cover algorithms for image characterisation, manipulation, segmentation and feature extraction in direct space. The course then proceeds to cover image filtering techniques with some indication of the role and implications of Fourier space, and more advanced characterisation and feature detection techniques such as edge and corner detection, together with multi-resolution methods, treatment of colour images, template matching and optical flow techniques. The course has a strong practical component that allows students to explore a range of practical techniques by  implementing their own image processing tools using Matlab or Python

Learning outcomes

On successful completion of the module, a student will be able to:

  1. Understand (i.e., be able to describe, analyse and reason about) how digital images are represented (in the spatial and frequency domain), manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation.
  2. Implement a variety of image processing algorithms including image manipulation, segmentation, fitering, blending, feature extraction and description, edge detection, template matching and image editing.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Robotics
  • MRes Virtual Reality
  • MSc Computer Graphics, Vision and Imaging
  • MSc Robotics and Computation
  • MRes Medical Physics and Biomedical Engineering


There are no formal prerequisites.


Introduction to the digital image

  • Why digital images?
  • Digital image capture
  • Data types and 2D representation of digital images.

Characteristics of grey-level digital images

  • Discrete sampling model.
  • Noise processes.
  • Image attributes.


  • Thresholding and thresholding algorithms.
  • Performance evaluation and ROC analysis.
  • Connected components labelling.
  • Region growing and region adjacency graph (RAG).
  • Split and merge algorithms.
  • Clustering algorithms
  • Graph based methods

Image transformations

  • Grey level transformations.
  • Histogram equalization.
  • Geometric transformations.
  • Affine transformations.
  • Polynomial warps.

Morphological operation

  • Erode and dilate as max and min operators on binary images.
  • Open, close, thinning and other transforms.
  • Medial axis transform.

Image filtering

  • Fourier analysis.
  • Linear and non-linear filtering operations.
  • Image convolutions.
  • Separable convolutions.
  • Sub-sampling and interpolation as convolution operations.

Feature characterisation

  • Calculation of region properties.
  • Moment features.
  • Boundary coding line descriptors from boundary coding and from moments.
  • Multi-resolution algorithms.

Edge and corner detection

  • Edge enhancement by differentiation.
  • Effect of noise, edge detection and Canny implementation.
  • Edge detector performance evaluation.
  • Image structure tensor.
  • Relationship to image auto-correlation.
  • Characterisation and Harris corner detector.

Colour images

  • Representations of colour in digital images.
  • Colour metrics.
  • Pixel-wise (point) operations.
  • Colour invariants and Finlayson colour constancy algorithm.

Template matching and advanced topics

  • Similarity and dissimilarity matching metrics.
  • Template matching
  • Optical flow
  • Non-local means filtering
  • Poisson image editing

An indicative reading list is available via


The module is delivered through a combination of lectures, tutorials and programming exercises.


This module delivery is assessed as below:



Weight (%)



Written examination (2hrs 30mins)




Coursework 1




Coursework 2



In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.