Module overview
The challenge of computer vision is to develop a computer based system with the capabilities of the human eye-brain system. It is therefore primarily concerned with the problem of capturing and making sense of digital images. The field draws heavily on many subjects including digital image processing, artificial intelligence, computer graphics and psychology.
This course will explore some of the basic principles and techniques from these areas which are currently being used in real-world computer vision systems and the research and development of new systems.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Current approaches to basic image processing and computer vision
- Human and computer vision systems
- Current approaches to image formation and image modelling
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Develop and evaluate solutions to problems in computer vision
- Analyse and design a range of algorithms for image processing and computer vision
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Implement basic image processing algorithms
Syllabus
- The human eye-brain system as a model for computer vision
- Image formation: sampling theorem, Fourier transform and Fourier analysis
- Image models
- Basic image processing: Sampling and quantisation, Brightness and colour, Histogram operations, Filters and convolution, Frequency domain processing
- Edge detection
- Boundary and line extraction
- Building machines that see: constraints, robustness, invariance and repeatability
- Fundamentals of machine-learning: classification and clustering
- Understanding covariance, eigendecomposition and PCA
- Feature extraction
- Interest point detection
- Segmentation
- 2-D Shape representation
- Local features
- Image matching
- Large-scale image search and feature indexing
- Understanding image data and performing classification and recognition
- 3D vision systems
- Recovering depth from multiple views
- Practical examples, including: biometric systems (recognising people), industrial computer vision, etc.
Learning and Teaching
| Type | Hours |
|---|---|
| Lecture | 24 |
| Preparation for scheduled sessions | 12 |
| Tutorial | 12 |
| Revision | 10 |
| Wider reading or practice | 55 |
| Completion of assessment task | 25 |
| Follow-up work | 12 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Sonka, Hlavac & Boyle (2008). Image Processing, Analysis and Machine Vision. PWS Publishing.
Stockman and Shapiro (2001). Computer Vision. Prentice Hall.
Nixon, M.S. and Aguado, A.S. (2012). Feature Extraction & Image Processing. Academic Press.
Gonzalez et al (2008). Digital Image Processing. Pearson.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Final Assessment | 60% |
| Continuous Assessment | 40% |
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