Course Outline

MCH400 Image Processing and Machine Vision

Course Coordinator:David Alonso-Caneiro (dalonsocaneiro@usc.edu.au) School:School of Science, Technology and Engineering

2024Semester 1

UniSC Moreton Bay

Blended learning Most of your course is on campus but you may be able to do some components of this course online.

Please go to usc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.

What is this course about?

Description

Machine vision has recently become an integral part of the mechatronics field, enabling robots to interact with their surroundings through image analysis and interpretation. In this hands-on course, you will be introduced to the principles of image processing and machine learning methods. You will gain the knowledge required to implement vision-based algorithms using industry-standard software such as Matlab and Python. The course will cover methods that can be applied to both robotic and industrial applications.

How will this course be delivered?

Activity Hours Beginning Week Frequency
Blended learning
Learning materials – Asynchronous course content, videos, reference material 2hrs Week 1 13 times
Tutorial/Workshop 1 – On campus: Problem solving and discussion 1hr Week 2 12 times
Tutorial/Workshop 2 – On campus: Computer labs 2hrs Week 2 11 times
Seminar – On campus: Welcome and course description 1hr Week 1 Once Only

Course Topics

 

Topics may include:

  • Introduction to image processing and vision
  • Image processing techniques
  • Histograms, thresholding, and filtering
  • Image segmentation and classification
  • Edge detection and feature extraction
  • Principles of machine learning
  • Supervised training
  • Model training and interpretation
  • Robotics and Vision based applications

 

What level is this course?

400 Level (Graduate)

Demonstrating coherence and breadth or depth of knowledge and skills. Independent application of knowledge and skills in unfamiliar contexts. Meeting professional requirements and AQF descriptors for the degree. May require pre-requisites where discipline specific introductory or developing knowledge or skills is necessary. Normally undertaken in the third or fourth full-time study year of an undergraduate program.

What is the unit value of this course?

12 units

How does this course contribute to my learning?

Course Learning Outcomes On successful completion of this course, you should be able to... Graduate Qualities Mapping Completing these tasks successfully will contribute to you becoming... Professional Standard Mapping * Engineers Australia Stage 1 Professional Engineer Competency Standards
1 Demonstrate theoretical knowledge in image processing and machine vision techniques. Knowledgeable
1, 1.1, 1.2, 1.3
2 Evaluate given tasks in image processing and machine vision. Creative and critical thinker
2, 2.1, 2.2, 2.3
3 Apply suitable software-based algorithms to implement image processing and machine vision techniques. Empowered
1, 1.3, 1.4, 2, 2.1, 3, 3.2, 3.3
4 Design a solution to a given image processing task by selecting, evaluating, and developing suitable algorithms/methods. Empowered
2, 2.1, 2.2, 2.3, 3, 3.3
5 Communicate professionally using mechatronics engineering terminology and symbols conforming to industry standards and formats. Engaged
3, 3.2
6 Work collaboratively in teams to develop vision-based solution including communicating with team members, planning, and managing tasks. Engaged
3, 3.2, 3.6

* Competencies by Professional Body

CODE COMPETENCY
Engineers Australia Stage 1 Professional Engineer Competency Standards
1 Elements of competency: Knowledge and Skill Base
1.1 Knowledge and Skill Base: Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
1.2 Knowledge and Skill Base: Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.3 Knowledge and Skill Base: In-depth understanding of specialist bodies of knowledge within the engineering discipline.
1.4 Knowledge and Skill Base: Discernment of knowledge development and research directions within the engineering discipline.
2 Elements of competency: Engineering Application Ability
2.1 Engineering Application Ability: Application of established engineering methods to complex engineering problem solving.
2.2 Engineering Application Ability: Fluent application of engineering techniques, tools and resources.
2.3 Engineering Application Ability: Application of systematic engineering synthesis and design processes.
3 Elements of competency: Professional and Personal Attributes
3.2 Professional and Personal Attributes: Effective oral and written communication in professional and lay domains.
3.3 Professional and Personal Attributes: Creative, innovative and pro-active demeanour.
3.6 Professional and Personal Attributes: Effective team membership and team leadership.

Am I eligible to enrol in this course?

Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.

Pre-requisites

Enrolled in GC004, GD004, MC004, GC006, GD006, MC006, SC404, SC405, SC410 or SC411.

Co-requisites

Not applicable

Anti-requisites

Not applicable

Specific assumed prior knowledge and skills (where applicable)

Not applicable

How am I going to be assessed?

Grading Scale

Standard Grading (GRD)

High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL).

Details of early feedback on progress

First assessment will be given early to the students in week 3. The feedback on this assessment will assist students in modifying their subsequent assessments later in the semester.  

Assessment tasks

Delivery mode Task No. Assessment Product Individual or Group Weighting % What is the duration / length? When should I submit? Where should I submit it?
All 1 Artefact - Technical and Scientific, and Written Piece Individual 30%
2000 to 3000 words
Week 6 Online Assignment Submission with plagiarism check
All 2 Artefact - Technical and Scientific, and Written Piece Individual 30%
1500 words + 5-10 minutes video
Week 10 Online Assignment Submission with plagiarism check and in class
All 3 Artefact - Technical and Scientific, and Written Piece Group 40%
Project based design report accompanied with code is to be submitted with an equivalent word length of about 2500 words. Practical implementation may also be required.
Week 13 Online Assignment Submission with plagiarism check
All - Assessment Task 1:Project-Based Case Studies
Goal:
The assignment will develop your knowledge and understanding of image processing and machine vision techniques. You will solve problems and propose algorithms related to topics like images as functions, thresholding, filtering, edge detection, Hough transforms, convolution, illumination, stereo vision etc. All these topics and content are developed during the workshops.
Product: Artefact - Technical and Scientific, and Written Piece
Format:
Relevant tasks and problems to enforce understanding of the students and help in gradual development of knowledge and skills throughout the course.
Criteria:
No. Learning Outcome assessed
1
Demonstration of theoretical knowledge in image processing and machine vision techniques.
1
2
Evaluate given tasks in image processing and machine vision.
2
3
Design a solution to a given image processing task by selecting, evaluating, and developing suitable algorithms/methods.
4
4
Communicate professionally using mechatronics engineering terminology and symbols conforming to industry standards and formats.
5
Generic Skills:
Communication, Collaboration, Problem solving, Organisation, Applying technologies, Information literacy
All - Assessment Task 2:Project-Based Case Studies
Goal:
This assessment will build you skills and knowledge in developing and implementing image processing and machine vision techniques using industry standard software (e.g. MatLab, Python). You will test established codes and modify/develop machine vision codes for given problems. All these topics and content are developed during the workshops.
Product: Artefact - Technical and Scientific, and Written Piece
Format:
Working individually, you will develop and implement software codes and submit with accompanying explanation and supporting material (text, images, flowhcarts). This will involve both a report and a short video presentation.
Criteria:
No. Learning Outcome assessed
1
Demonstrate theoretical knowledge in image processing and machine vision techniques.
1
2
Evaluate given tasks in image processing and machine vision.
2
3
Apply suitable software-based algorithms to implement image processing and machine vision techniques.
3
4
Communicate professionally using mechatronics engineering terminology and symbols conforming to industry standards and formats.
5
Generic Skills:
Communication, Collaboration, Problem solving, Organisation, Applying technologies, Information literacy
All - Assessment Task 3:Project
Goal:
The design project will give you an opportunity to use the skills learnt during the course. You would need to apply vision techniques to solve a loosely defined design requirement set in real-world context.
Product: Artefact - Technical and Scientific, and Written Piece
Format:
Working in groups, you will submit your design solution with supporting material (text, images, flowcharts). Software and hardware implementation may be required to demonstrate project performance.
Criteria:
No. Learning Outcome assessed
1
Apply suitable software-based algorithms to implement image processing and machine vision techniques.
3
2
Design a solution to a given image processing task by selecting, evaluating, and developing suitable algorithms/methods.
4
3
Communicate professionally using mechatronics engineering terminology and symbols conforming to industry standards and formats.
5
4
Work collaboratively in teams to develop vision-based solution including communicating with team members, planning, and managing tasks.
6
Generic Skills:
Communication, Collaboration, Problem solving, Organisation, Applying technologies, Information literacy

Directed study hours

A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.

What resources do I need to undertake this course?

Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.

Prescribed text(s) or course reader

Please note that you need to have regular access to the resource(s) listed below. Resources may be required or recommended.

Required? Author Year Title Edition Publisher
Recommended Rafael C. Gonzalez,Richard E. Woods 2017 Digital Image Processing, Global Edition or Latest Pearson Higher Education
Recommended David Forsyth,Jean Ponce 2012 Computer Vision n/a Prentice Hall
Recommended Ravishankar Chityala,Sridevi Pudipeddi 2022 Image Processing and Acquisition Using Python n/a CRC Press
Recommended Rafael C. Gonzalez,Richard E. Woods,Steven L. Eddins 0 Digital Image Processing Using MATLAB n/a n/a

Specific requirements

Computer/laptop capable of running Python 3 or similar. The computer should have enough computing power to run MatLab if required. USB drive will be required to attach cameras and/or programming boards. In some cases webcam/external camera may also be required.

How are risks managed in this course?

Risk assessments have been conducted for the field activities being undertaken and a high level of risk has been identified. High level risk may include, boating, diving, and hot works such as welding, cutting and grinding. Where high risks exist you will be given training and advice about how to control the high level risk, however it is also your responsibility to review course material, search online, discuss with lecturers and peers and understand the health and safety risks associated with your specific course of study and to familiarise yourself with the University’s general health and safety principles by reviewing the online induction training for students, and following the instructions of the University staff.

What administrative information is relevant to this course?

Assessment: Academic Integrity

Academic integrity is the ethical standard of university participation.  It ensures that students graduate as a result of proving they are competent in their discipline.  This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.

Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person.  You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.

In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin.  This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.

Assessment: Additional Requirements

Eligibility for Supplementary Assessment

Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:

- The final mark is in the percentage range 47% to 49.4%
- The course is graded using the Standard Grading scale
- You have not failed an assessment task in the course due to academic misconduct

Assessment: Submission penalties

Late submission of assessment tasks will be penalised at the following maximum rate (the rates are cumulative):

5% (of the assessment task’s identified value) per day for the first two days from the date identified as the due date for the assessment task
10% (of the assessment task’s identified value) for the third day
20% (of the assessment task’s identified value) for the fourth day and subsequent days up to and including seven days from the date identified as the due date for the assessment task
A result of zero is awarded for an assessment task submitted seven days from the date identified as the due date for the assessment task.
Weekdays and weekends are included in the calculation of days late.

To request an extension, you must contact your Course Coordinator and supply the required documentation to negotiate an outcome.

SafeUniSC

UniSC is committed to a culture of respect and providing a safe and supportive environment for all members of our community. For immediate assistance on campus contact SafeUniSC by phone: 07 5430 1168 or using the SafeZone app. For general enquires contact the SafeUniSC team by phone 07 5456 3864 or email safe@usc.edu.au.

The SafeUniSC Specialist Service is a Student Wellbeing service that provides free and confidential support to students who may have experienced or observed behaviour that could cause fear, offence or trauma. To contact the service call 07 5430 1226 or email studentwellbeing@usc.edu.au.

Study help

For help with course-specific advice, for example what information to include in your assessment, you should first contact your tutor, then your course coordinator, if needed.

If you require additional assistance, the Learning Advisers are trained professionals who are ready to help you develop a wide range of academic skills. Visit the Learning Advisers web page for more information, or contact Student Central for further assistance: +61 7 5430 2890 or studentcentral@usc.edu.au.

Wellbeing Services

Student Wellbeing provide free and confidential counselling on a wide range of personal, academic, social and psychological matters, to foster positive mental health and wellbeing for your academic success.

To book a confidential appointment go to Student Hub, email studentwellbeing@usc.edu.au or call 07 5430 1226.

AccessAbility Services

Ability Advisers ensure equal access to all aspects of university life. If your studies are affected by a disability, learning disorder mental health issue, injury or illness, or you are a primary carer for someone with a disability or who is considered frail and aged, AccessAbility Services can provide access to appropriate reasonable adjustments and practical advice about the support and facilities available to you throughout the University.

To book a confidential appointment go to Student Hub, email AccessAbility@usc.edu.au or call 07 5430 2890.

Links to relevant University policy and procedures

For more information on Academic Learning & Teaching categories including:

  • Assessment: Courses and Coursework Programs
  • Review of Assessment and Final Grades
  • Supplementary Assessment
  • Central Examinations
  • Deferred Examinations
  • Student Conduct
  • Students with a Disability

For more information, visit https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching

Student Charter

UniSC is committed to excellence in teaching, research and engagement in an environment that is inclusive, inspiring, safe and respectful. The Student Charter sets out what students can expect from the University, and what in turn is expected of students, to achieve these outcomes.

General Enquiries

  • In person:
    • UniSC Sunshine Coast - Student Central, Ground Floor, Building C, 90 Sippy Downs Drive, Sippy Downs
    • UniSC Moreton Bay - Service Centre, Ground Floor, Foundation Building, Gympie Road, Petrie
    • UniSC SouthBank - Student Central, Building A4 (SW1), 52 Merivale Street, South Brisbane
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  • Email:studentcentral@usc.edu.au