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
Lecture	24
Follow-up work	12
Supervised time in studio/workshop	24
Preparation for scheduled sessions	12
Completion of assessment task	18
Revision	10
Wider reading or practice	50
Total study time	150

Resources & Reading list

R.C. Gonzalez, R.E. Woods (2008). Digital Image Processing.

Nixon M S and Aguado A S (2012). Feature Extraction and Image Processing.

W.K. Pratt (1991). Digital Image Processing.

Assessment

Summative

Method	Percentage contribution
Examination (2 hours)	70%
Laboratory	30%

Repeat

Method	Percentage contribution
Examination	100%

Referral

Method	Percentage contribution
Examination (2 hours)	100%

Repeat Information

Repeat type: Internal & External