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
| Type | Hours |
|---|---|
| Follow-up work | 12 |
| Preparation for scheduled sessions | 12 |
| Completion of assessment task | 18 |
| Wider reading or practice | 50 |
| Supervised time in studio/workshop | 24 |
| Revision | 10 |
| Lecture | 24 |
| Total study time | 150 |
Resources & Reading list
Nixon M S and Aguado A S (2012). Feature Extraction and Image Processing.
R.C. Gonzalez, R.E. Woods (2008). Digital Image Processing.
W.K. Pratt (1991). Digital Image Processing.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Exam (2 hours) | 70% |
| Laboratory | 30% |
Referral
| Method | Percentage contribution |
|---|---|
| Exam (2 hours) | 100% |
Repeat Information
Repeat type: Internal & External