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
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
- What segmentation is and how to do segmentation in digital images
- How to extract features from images
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- How computers can process digital images
- The relation to signal processing and other fields
- How images can be digitised and stored in computers
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 |
|---|---|
| Wider reading or practice | 50 |
| Preparation for scheduled sessions | 12 |
| Supervised time in studio/workshop | 24 |
| Completion of assessment task | 18 |
| Revision | 10 |
| Lecture | 24 |
| Follow-up work | 12 |
| Total study time | 150 |
Resources & Reading list
Textbooks
R.C. Gonzalez, R.E. Woods (2008). Digital Image Processing. Pearson International Edition.
Nixon M S and Aguado A S (2012). Feature Extraction and Image Processing. Academic Press.
W.K. Pratt (1991). Digital Image Processing. John Wiley.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Final Assessment | 70% |
| Continuous Assessment | 30% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat Information
Repeat type: Internal & External