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
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
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 |
---|---|
Preparation for scheduled sessions | 12 |
Follow-up work | 12 |
Wider reading or practice | 50 |
Completion of assessment task | 18 |
Revision | 10 |
Supervised time in studio/workshop | 24 |
Lecture | 24 |
Total study time | 150 |
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
Method | Percentage contribution |
---|---|
Continuous Assessment | 30% |
Final Assessment | 70% |
Repeat
Method | Percentage contribution |
---|---|
Set Task | 100% |
Referral
Method | Percentage contribution |
---|---|
Set Task | 100% |
Repeat Information
Repeat type: Internal & External