Skip to Main Content
Catalogue Link
<a href="/ords/r/curriculum/catalogue/home" class="t-Header-logo-link"><span class="apex-logo-text">Handbook</span></a>
Hidden Page Items
New
8
New
HE Unit Template
New
Digital Imaging and Artificial Intelligence
New
ENGIN3405 - Digital Imaging and Artificial Intelligence
New
20240200
New
New
New
New
Course
New
Unit
New
New
1.2
New
New
2026-09-10
New
2026-09-10
New
Engineering
New
New
New
New
Institute of Innovation, Science & Sustainability
New
Engineering
New
Undergraduate
New
15
New
New
.125
New
030101
New
Manufacturing Engineering
New
030101 - Manufacturing Engineering
New
<a href="http://www.federation.edu.au/locate/unit/engin3405">View unit fees</a>
New
Graded (e.g. HD, D, C, etc)
New
7
New
Bachelor Degree
New
New
New
New
New
1
New
New
New
New
New
false
New
New
New
<p>The course introduces students to the advanced level knowledge and understanding of digital imaging and artificial intelligence. The students will learn about the historical development of artificial intelligence and image processing technologies and appreciate their use in current industrial environments. Students will further learn about the diversity of artificial intelligence, image processing and their applications. Through such learning, students will gain practical skills in developing various algorithms and building software based models to be implemented in physical mechatronic systems.</p>
New
New
<p>•Introduction to digital imaging and image representation, addition and subtraction of images, spatial and frequency filtering.<br>
•Image analysis algorithm with methods involving feature extraction, image segmentation, edge detection, object counting and measurement and other low-level image analysis techniques.<br>
•Pattern recognition and classification techniques.<br>
•Artificial intelligence, reasoning, search and different machine learning and optimization algorithm / techniques.<br>
•Introduction to artificial neural network, classifier, classification errors, perceptron update rule, perceptron convergence, generalisation, regularisation, regression, boosting, Markov models and hidden Markov models.<br>
•Bayesian networks, radial bias networks, probabilistic neural networks, generalised neural networks, self-organising and learning vector quantisation networks.<br>
•Introduction to fuzzy logic, fuzzy set and fuzzy logic expert systems.<br>
•Use of artificial intelligence techniques in classification and pattern recognition.</p>
New
true
New
<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
New
true
Hidden Page Items
Semester
Semester
0200
Year
Year
2027
Current Year
2026
Breadcrumb Positioning
Handbook
ENGIN3405 - Digital Imaging and Artificial Intelligence
Pseudo Breadcrumb
ENGIN3405 - Digital Imaging and Artificial Intelligence
Breadcrumb Type
Unit
Catalogue Term and Year
Version Text
You are viewing a version for
Semester
Semester 1
Semester 2
Summer Semester
Year
2027
2026
Download PDF
RDS Holder
Overview
Key information
Requisites
Learning outcomes
Assessment
FedTASK
Parent Region
Overview
Unit description
<p>The course introduces students to the advanced level knowledge and understanding of digital imaging and artificial intelligence. The students will learn about the historical development of artificial intelligence and image processing technologies and appreciate their use in current industrial environments. Students will further learn about the diversity of artificial intelligence, image processing and their applications. Through such learning, students will gain practical skills in developing various algorithms and building software based models to be implemented in physical mechatronic systems.</p>
Unit content
<p>•Introduction to digital imaging and image representation, addition and subtraction of images, spatial and frequency filtering.<br> •Image analysis algorithm with methods involving feature extraction, image segmentation, edge detection, object counting and measurement and other low-level image analysis techniques.<br> •Pattern recognition and classification techniques.<br> •Artificial intelligence, reasoning, search and different machine learning and optimization algorithm / techniques.<br> •Introduction to artificial neural network, classifier, classification errors, perceptron update rule, perceptron convergence, generalisation, regularisation, regression, boosting, Markov models and hidden Markov models.<br> •Bayesian networks, radial bias networks, probabilistic neural networks, generalised neural networks, self-organising and learning vector quantisation networks.<br> •Introduction to fuzzy logic, fuzzy set and fuzzy logic expert systems.<br> •Use of artificial intelligence techniques in classification and pattern recognition.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Undergraduate
Credit points:
15
EFTSL:
0.125
Field of education:
030101 - Manufacturing Engineering
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/engin3405">View unit fees</a>
Requisites
Prerequisite
<p>must have completed 1 of "<a href="/ords/r/curriculum/catalogue/course?p6_code=MATHS3001" target="_blank">MATHS3001</a>" / "MATHS3040"</p>
Corequisite
None
Exclusion
<p>must not have completed "ENMTX3030"</p>
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Demonstrate understanding of image processing, image representation, image segmentation, feature extraction and low-level image analysis techniques.</p>
K2
<p>Demonstrate understanding of spatial and frequency filtering.</p>
K3
<p>Interpret and analyse image analysis algorithms in edge and shape detection, colour based segmentation and image thresholding.</p>
K4
<p>Demonstrate understanding of pattern recognition and classification process.</p>
K5
<p>Explain and outline the advanced concepts and historical development of artificial intelligence.</p>
K6
<p>Interpret and discriminate the development of various optimization and machine learning algorithms / techniques.</p>
K7
<p>Demonstrate advanced understanding of expert systems and neural networks.</p>
S1
<p>Test and critically analyse results from the performed image analysis.</p>
S2
<p>Perform spatial and frequency filtering and feature extraction.</p>
S3
<p>Develop and analyse image analysis algorithms.</p>
S4
<p>Perform classification and pattern recognition using artificial intelligence and suitable methodologies.</p>
S5
<p>Evaluate optimization / network learning algorithms.</p>
S6
<p>Formulate and appraise fuzzy rules.</p>
A1
<p>Apply digital imaging and artificial intelligence techniques in areas of robot vision, condition monitoring, quality control, environmental sensing and interaction, object recognition and classification</p>
A2
<p>Design, develop and optimize intelligent models based on artificial intelligence methodologies.</p>
A3
<p>Develop advanced learning algorithms for a neural network model to achieve the required design objectives.</p>
A4
<p>Implement the knowledge and skills gained through this subject in designing and developing intelligent mechatronics product / system.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>Experimental work and / or projects to verify students ability to apply knowledge and skills acquired in the course</p>
<p>Reports, demonstrations</p>
<p>10 - 30%</p>
S1, S2, S3, S4, S5, S6, A1, A2, A3, A4
2
<p>Relevant tasks and problems to enforce understanding of the students and help in gradual development of knowledge and skills throughout the course</p>
<p>Assignments, quizzes</p>
<p>10 - 30%</p>
K1, K2, K3, K4, K5, K6, K7, S1, S2, S3, S4, S5, S6
3
<p>Questions and problems related to the course contents</p>
<p>Exams / Tests</p>
<p>40 - 60%</p>
K1, K2, K3, K4, K5, K6, K7
Supplementary assessment available:
Yes
Supplementary assessment information:
<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
FedTASK
FedTASK
Description of FedTASK outcome
Level
FedTASK 1: Interpersonal
The ability to effectively communicate, interact and work with others both individually and in groups.
FedTASK 2: Leadership
The ability to apply professional skills and behaviours in leading others.
FedTASK 3: Critical Thinking and Creativity
The ability to work in complexity and ambiguity using imagination to create new ideas.
FedTASK 4: Digital Literacy
The ability to work fluently across a range of tools and applications to achieve a range of skills.
FedTASK 5: Sustainable and Ethical Mindset
The ability to consider and assess the consequences and impact of ideas and actions in enacting ethical and sustainable decisions.
FedTASK
<p>Federation University recognises that students require key transferable employability skills to prepare them for their future workplace and society. FedTASKs (<strong>T</strong>ransferable <strong>A</strong>ttributes <strong>S</strong>kills and <strong>K</strong>nowledge) provide a targeted focus on five key, transferable Attributes, Skills, and Knowledge that are embedded within curriculum, developed gradually towards successful measures and interlinked with cross-discipline and co-operative learning opportunities.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Undergraduate
Credit points:
15
EFTSL:
0.125
Field of education:
030101 - Manufacturing Engineering
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/engin3405">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: ENGIN3405 - U1.2