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

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
Revision	10
Completion of assessment task	3
Wider reading or practice	65
Lecture	36
Preparation for scheduled sessions	18
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.

Haykin (2003). Signals and Systems.

Schaum's Outline of Signals and Systems.

Mandal and Asif (2007). Continuous and Discrete Time Signals and Systems.

Assessment

Summative

Method	Percentage contribution
Final Assessment	100%

Repeat

Method	Percentage contribution
Set Task	100%

Referral

Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: ELEC2220