MODULE DETAILS

Module 9: Machine Vision DMCMVI609

 

Nominal duration: 5 weeks (60 hours total time commitment)

This time commitment includes the structured activities, preparation reading, and attendance at each webinar, completing exercises, practical assessments and proctored assessments.

It is also expected that students spend additional time on readings, personal study, independent research and learning, practicing on remote labs and required software and working on any projects and assignments.

This module covers the fundamentals of image processing and machine vision. It covers selection and integration of various components into a professional and working system.

MODULE PURPOSE

The purpose of this module is for the participants to gain knowledge of various components of image processing and modern machine vision systems to give participants a solid foundation to work from.

MODIFICATION HISTORY Version 2.0

PRE-REQUISITE MODULES/UNIT(S)

NA

ASSESSMENT STRATEGY

METHODS OF ASSESSMENT

Assessors should gather a range of evidence that is valid, sufficient, current and authentic. Evidence can be gathered through a variety of ways including direct observation, supervisor's reports, project work, structured assessments, samples and questioning. This will include short answer questions on the knowledge content, the use of remote and virtual labs, and writing tasks to apply the learning to academic tasks.

CONDITIONS OF ASSESSMENT

 Assessors must:

  • hold the appropriate assessor competency standards as outlined in regulations; and
  • be able to demonstrate vocational competencies at least to the level being assessed; and
  • be able to demonstrate how they are continuing to develop their VET knowledge and skills as well as maintaining their industry currency and assessor competence.

Questioning techniques should not require language, literacy and numeracy skills beyond those required in this module. The candidate must have access to all tools, equipment, materials and documentation required.

The candidate must be permitted to refer to any relevant workplace procedures, product and manufacturing specifications, codes, standards, manuals and reference materials.

Assessments will be open book assessment and may be completed off-campus. Invigilation software will be used for some assessments to ensure authenticity of work completed.

Model answers must be provided for all knowledge-based assessments to ensure reliability of assessment judgements when marking is undertaken by different assessors.

SUMMARY OF LEARNING OUTCOMES

  1. Outline the basics of digital image processing
  2. Select appropriate system components
  3. Identify robot vision applications
Learning Outcome 1 Outline the basics of digital image processing
Assessment criteria

1.1.   Perform Fast Fourier Transforms (FFT) with MATLAB (or alternative)

 

1.2.   Explain common issues related to sampling theory, aliasing and resolution

 

Learning Outcome 2 Select appropriate system components
Assessment criteria

2.1.   Select machine vision hardware and software components

 

2.2.   Identify and explain lighting components and techniques

 

2.3.   Identify and explain machine vision sensors and cameras

 

Learning Outcome 3

Identify robot vision applications

Assessment criteria

3.1.   List robot vision applications (e.g. tracking lines, avoiding obstacles, identifying fiduciaries) with robot vision software

 

3.2.   Document how to apply a webcam as a robot vision sensor

 

Delivery Mode

Online and/or face-to-face

 

 

Software/Hardware Used

Software

  • MATLAB
  • RoboRealm

Hardware

  • Remote lab