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The University of Southampton

ELEC6213 Image Processing

Module Overview

This module is useful to introduce: - Image processing and its relation to signal processing. - Image transformations for filtering, coding and etc. - Histogram processing algorithms to enhance image qualities and visibility. - Theories analysing and understanding images using feature extraction, segmentation, and texture modelling. - Linear and nonlinear methods for shape registration, noise reduction and restoration. - Image classification and object recognition. - Edge detection

Aims and Objectives

Module Aims

To provide an overview of image processing methodologies

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • How images can be digitised and stored in computers
  • How computers can process digital images
  • The relation to signal processing and other fields
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • How to use features to classify images for recognition
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • How to do linear and nonlinear filtering on images
  • How to extract features from images
  • What segmentation is and how to do segmentation in digital images


- Overview [1] - Image acquisition and sampling theory [1] - Image transformations [2]: Fourier, Discrete Cosine and Wavelet - Histogram processing and linear filtering [1] - Point processing and operations [1] - Calculus of variations and Lagrange miltipliers [2] - Active contours [4]: Kass Model and Level Set formulation - Geodesic Active contours [2] - Shape Registeration [1] - Image noise reduction[1] - Anisotropic Diffusion [1] - Image Restoration [3]: Wiener Filter and total variation - Shape description [3] - Image Classifcation and Recognition[1]

Learning and Teaching

Completion of assessment task18
Supervised time in studio/workshop24
Follow-up work12
Preparation for scheduled sessions12
Wider reading or practice50
Total study time150

Resources & Reading list

Nixon M S and Aguado A S (2012). Feature Extraction and Image Processing. 

W.K. Pratt (1991). Digital Image Processing. 

R.C. Gonzalez, R.E. Woods (2008). Digital Image Processing. 



MethodPercentage contribution
Examination  (2 hours) 70%
Laboratory 30%


MethodPercentage contribution
Examination 100%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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