Course Title: Image Processing
Part A: Course Overview
Course Title: Image Processing
Credit Points: 12.00
Please note that this course may have compulsory in-person attendance requirements for some teaching activities.
To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.
Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209.
Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus
Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.
125H Electrical & Computer Engineering
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016
172H School of Engineering
Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022
Course Coordinator: Dr Shaun Cloherty
Course Coordinator Phone: +61 3 9925 0424
Course Coordinator Email: firstname.lastname@example.org
Course Coordinator Location: 012.08.017
Course Coordinator Availability: by appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
You will need basic knowledge of digital signal processing and proficiency in MATLAB.
Knowledge of engineering mathematics including transform theory and linear algebra is an advantage.
Image Processing is an area of information science and engineering of growing importance with a wide range of applications, including: digital photography, video conferencing and streaming, industrial imaging and computer vision systems, autonomous vehicles, remote sensing and satellite imaging, video surveillance and security systems, and medical imaging such as CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), x-ray and ultrasound imaging.
This course will cover topics on digital image processing fundamentals, image analysis, image processing and restoration, image compression, morphological image processing, image segmentation and description as well as contemporary applications of neural networks and machine learning in image analysis and computer vision.
The course will contribute to consolidation of students’ engineering mathematical knowledge and engineering programming skills, extension of theoretical knowledge and practical skills to solve multidimensional signal processing problems, and the development of your ability to work on multidisciplinary issues in diverse areas of digital imaging, image processing, computer vision, and machine learning applications.
The course will reinforce and enhance students’ skills in technical communication through preparation of technical laboratory reports and presentations.
Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following program learning outcomes for students who commenced their program prior to 2023:
- 1.1. Comprehensive, theory-based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline
- 1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline
- 1.4. Discernment of knowledge development and research directions within the engineering discipline
- 2.1. Application of established engineering methods to complex engineering problem solving
- 2.2. Fluent application of engineering techniques, tools and resources
- 2.3. Application of systematic engineering synthesis and design processes
- 3.2. Effective oral and written communication in professional and lay domains
- 3.5. Orderly management of self, and professional conduct.
This course contributes to the following program learning outcomes for students who commenced their program in 2023:
- PLO1: Demonstrate an in-depth understanding and knowledge of fundamental engineering and scientific theories, principles and concepts and apply advanced technical knowledge in specialist domain of engineering.
- PLO2: Utilise mathematics and engineering fundamentals, software, tools and techniques to design engineering systems for complex engineering challenges.
- PLO3: Apply engineering research principles, methods and contemporary technologies and practices to plan and execute projects taking into account ethical, environmental and global impacts.
- PLO4: Apply systematic problem solving, design methods and information and project management to propose and implement creative and sustainable solutions with intellectual independence and cultural sensitivity.
- PLO5: Communicate respectfully and effectively with diverse audiences, employing a range of communication methods, practising professional and ethical conduct.
- PLO6: Develop and demonstrate the capacity for autonomy, agility and reflection of own learning, career and professional development and conduct.
On completion of this course, you should be able to (CLOs):
- Describe the basic issues and principal applications of image processing, and the roles of image processing and systems in a variety of applications.
- Demonstrate a good understanding of current state-of-the-art image processing systems and applications which constantly push the boundaries and raise challenges in other fields of studies such as mathematics, physics, and computer systems engineering.
- Identify areas of knowledge which are required, select an appropriate approach to a given image processing task, and critically evaluate alternative solutions.
- Implement image processing tasks with a high level of proficiency.
- Identify applications of image processing to advancement of knowledge in sciences and engineering with benefits in, e.g., policing, public safety and security, and social issues such as privacy.
- Demonstrate a high level of self-directed learning ability and good oral and written communication skills on technical topics of image processing and systems engineering.
Overview of Learning Activities
Student Learning occurs through the following experiences and evaluation processes:
- Recorded lectures where syllabus material will be presented and explained, and the subject will be illustrated with demonstrations and examples.
- Completion of tutorial questions and laboratory projects which provide an introduction to software tools for design, simulation and evaluation of image processing systems, and are designed to give further practice in practical application of the course material and provide feedback on student progress and understanding.
- Self-directed private study and problem-based learning, working through the course material as presented in class and learning materials, and gaining practice at solving conceptual and numerical problems.
Feedback will be provided throughout the semester in class and/or online discussions, through individual and group feedback on practical exercises and by individual consultation.
Overview of Learning Resources
You will be expected to use library and electronic resources (as well as any other appropriate resources) to engage in professional reading and private study of relevant material on image processing and machine learning.
The learning resources for this course include:
- Lecture material prepared by the teaching staff.
- Recommended textbook and references as listed in the Course Guide Part B and the RMIT online teaching platform.
- You will be expected to have access to suitable computing equipment for design and evaluation of image processing systems. Required software (MATLAB) is freely available to RMIT students.
Overview of Assessment
This course has no hurdle requirements.
Assessment for this course consists of the following components:
- Laboratory Tasks.
- Laboratory Project.
- Final Assessment (submitted online).
You will be required to submit formal individual reports for each laboratory task. Feedback will be provided in the submitted report, via Canvas, or via individual consultation. Furthermore, during the laboratory sessions the tutor will provide further insight into your work and how it could potentially be improved or expanded
Assessment Task 1: Laboratory Tasks
Assessment Task 2: Laboratory Project
Assessment Task 3: Final Assessment