ELEC3218 Signal and Image Processing
Module Overview
Signal processing is an essential part of human life and of modern industrial systems. As humans we see and hear and process signals. This is the same in electronic systems: we sense and then process signals. We need to be able to understand these signals, sometimes to interpret them, sometimes to filter them and sometimes to develop systems to process them automatically. That is what this module is about, and we shall apply the processes to images and to music in continuous and discrete systems.
Aims and Objectives
Module Aims
This module aims at introducing the theoretical tools necessary to process images and time signals in the continuous and discrete case.
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Apply signal processing techniques to understand and analyse 1-dimensional and 2-dimensional signals
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Demonstrate knowledge and understanding of frequency domain analysis and synthesis. Be able to use basic techniques to process 1-dimensional signals. Be able to implement standard approaches to process 2-dimensional images
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Apply signal and image processing in research and industrial environments
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Understand the basic approaches in a technology fundamental to perception of signals
Syllabus
- Statistical signal processing [rm1] - Human audiovisual system - Continuous Fourier analysis, Fourier transform (FT). Amplitude and power spectrums for - periodic and non-periodic signals. Power spectral analysis and cosine transform (CT) - Convolution, correlation and Fourier Transform - Analogue filter design - Sampling and aliasing - Discrete signal analysis and z transforms - Discrete FT (Fast FT – FFT; Discrete CT - DCT) Digital filters and their design (FIR and IIR) - Random signals - Adaptive filtering - 2D FTs (difference between 2D and 1D, 2D DCT) - Mpeg (music) and jpeg (image) coding - Point and group image operators (and convolution) - Edge detection in images - Image shape extraction (Template matching, HT and correlation) - Shape extraction by evolution - Image filtering - Image restoration
Learning and Teaching
| Type | Hours |
|---|---|
| Wider reading or practice | 65 |
| Completion of assessment task | 3 |
| Lecture | 36 |
| Preparation for scheduled sessions | 18 |
| Revision | 10 |
| Follow-up work | 18 |
| Total study time | 150 |
Resources & Reading list
Nixon and Aguado (2012). Feature Extraction & Image Processing for Computer Vision,.
Oppenheim and Schafer (2013). Discrete-Time Signal Processing.
Schaum's Outline of Signals and Systems.
Haykin (2003). Signals and Systems.
Mandal and Asif (2007). Continuous and Discrete Time Signals and Systems.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Examination (2 hours) | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat Information
Repeat type: Internal & External
Linked modules
Pre-requisite: ELEC2220