Machine Vision
Overview
This unit of study aims to provide you with a solid founding of the fundamentals of digital image processing for machine vision applications. Building on digital signal processing theory, you will attain learn how to apply 2D image filtering, sampling, and transformation techniques to solve common machine vision and image processing problems.
Requisites
Rule
EEE20006 Circuits & Electronics 1
OR
EEE20002 Circuits and Systems
AND
MTH20014 Matrices, Vector Calculus and Complex Analysis
OR
MTH20010 Statistics and Computation for Engineering
OR
MTH20007 Engineering Mathematics 3A *
OR
MTH20004 Engineering Mathematics 3A
OR
MTH20005 Engineering Mathematics 3B *
OR
MTH20017 Mathematical Methods and Statistics for Engineering
02-November-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Appreciate time, spatial and frequency domain representations and properties of signals and signal processing systems (K1,K2,K3,S1)
- Analyse and apply sampling and digital quantification mechanisms in image processing (K2,K3,S1.S2)
- Apply transformation methods to the analysis and enhancement of image data and the analysis and design of image filters in the spatial and frequency domains (K2,K3,S1,S2)
- Appreciate two-dimensional visual geometry, camera projection models and associated techniques for camera calibration (K2.K3.S1.S2.S3)
- Apply software tools to the design and evaluation of machine vision systems (K2.K3.S1.S2.S3)
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| On-campus Lecture |
2.00 | 12 weeks | 24 |
| On-campus Class |
3.00 | 5 weeks | 15 |
| On-campus Class |
1.00 | 6 weeks | 6 |
| Unspecified Activities Independent Learning |
7.50 | 12 weeks | 90 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Project Presentation | Group | 30 - 50% | 2,3,4,5 |
| Project Report | Individual/Group | 50 - 70% | 1,2,3,4,5 |
Content
- Review of discrete versus continuous systems
- Fundamentals of digital filter design
- 2D image filtering and sampling (spatial and frequency domains)
- Applications of 2D image filtering to image contrast enhancement, edge detection and contour analysis
- Introduction to 2D image segmentation
- 2D image geometry and camera calibration
- Advanced topics for machine vision systems
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.