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

Prerequisites
EEE40017 Machine Vision

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

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024

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)
Face to Face Contact (Phasing out)
Lecture
2.00 12 weeks 24
Face to Face Contact (Phasing out)
Workshop
3.00 2 weeks 6
Face to Face Contact (Phasing out)
Science Laboratory
3.00 10 weeks 30
Unspecified Learning Activities (Phasing out)
Independent Learning
7.50 12 weeks 90
TOTAL150

Assessment

Type Task Weighting ULO's
Project PresentationGroup 30 - 50% 2,3,4,5 
Project ReportIndividual/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.