The University of Southampton
Courses

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 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
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

Syllabus

- 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

TypeHours
Lecture24
Preparation for scheduled sessions12
Wider reading or practice50
Follow-up work12
Revision10
Supervised time in studio/workshop24
Completion of assessment task18
Total study time150

Resources & Reading list

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

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

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

Assessment

Summative

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

Referral

MethodPercentage contribution
Exam  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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