Part A: Course Overview

Course Title: Image Systems Engineering

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET1255

City Campus

Postgraduate

125H Electrical & Computer Engineering

Face-to-Face

 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

EEET1255

City Campus

Postgraduate

172H School of Engineering

Face-to-Face

 Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 2 2023,
Sem 2 2024

Course Coordinator: Margaret Lech

Course Coordinator Phone: +61399251028

Course Coordinator Email: margaret.lech@rmit.edu.au


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.


Course Description

Image Systems Engineering is an area of information science and engineering of growing importance with a wide range of applications, including digital photography, in 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 postgraduate course covers 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.


Objectives/Learning Outcomes/Capability Development

This course contributes to the program learning outcomes for the following program(s):  

PLO 2. Demonstrate an advanced and integrated understanding of specialist bodies of knowledge within the engineering discipline
PLO 3. Demonstrate advanced and integrated knowledge of the scope, principles, norms, accountabilities, and bounds and a critical appreciation of  trends in contemporary practice, sustainability, research and innovation in the engineering discipline.
PLO 5. Utilise advanced mathematics, software, tools and techniques, in the conduct of research into the  design and analysis of complex engineering systems
PLO 8. Communicate engineering designs and solutions respectfully and effectively, employing a range of advanced communication methods in interpreting and transmitting knowledge, in an individual or team environment,  to diverse audiences.​​
PLO 9. Demonstrate the capacity for personal accountability, professional and ethical conduct, intellectual independence, cultural sensitivity, autonomy, adaptability, and critical reflection on own learning and career ​​ when undertaking engineering projects
PLO 10. Critically analyse, evaluate, and transform information, while exercising professional expert judgement in a dynamic environment in the absence of complete data, in an engineering context.

For more information on the program learning outcomes for your program, please see the program guide.  


Upon successful completion of this course, you will be able to:

CLO1 Demonstrate an advanced and integrated understanding of the principles and concepts of imaging systems and image processing.
CLO2 Apply specialised technical knowledge and skills in design and implementation of image processing solutions with a high level of proficiency.
CLO3 Critically analyse image processing problems, synthesise solutions, and critically evaluate their implementation and performance.
CLO4 Apply high level, independent research to synthesise complex information and apply established concepts and theories to problems in image processing and image systems engineering.
CLO5 Apply a high level of technical communication skill through effective documentation and communication of technical concepts and solutions to a specialist audience.
CLO6 Establish personal accountability, professional and ethical conduct through effective collaboration and contribution in a team environment.

 


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

Assessment tasks

Assessment Task 1: Lab Tasks, 40%, CLO2, CLO3, CLO4, and CLO5 
Assessment Task 2: Lab Project, 45%, CLO1, CLO2, CLO3, CLO4, CLO5 and CLO6 
Assessment Task 3: Quiz, 15%, CLO1, CLO2, and CLO5