Course Coordinator:David Alonso-Caneiro (dalonsocaneiro@usc.edu.au) School:School of Science, Technology and Engineering
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.
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.
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 |
Topics may include:
400 Level (Graduate)
12 units
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 |
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. |
Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.
Enrolled in GC004, GD004, MC004, GC006, GD006, MC006, SC404, SC405, SC410 or SC411.
Not applicable
Not applicable
Not applicable
Standard Grading (GRD)
High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL). |
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.
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. |
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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. |
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Criteria: |
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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. |
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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. |
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Criteria: |
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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. |
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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. |
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Criteria: |
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Generic Skills: | Communication, Collaboration, Problem solving, Organisation, Applying technologies, Information literacy |
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.
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.
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 |
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.
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.
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
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.
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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.
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