Course Title: Image Systems Engineering

Part A: Course Overview

Course Title: Image Systems Engineering

Credit Points: 12.00

Course Code




Learning Mode

Teaching Period(s)


City Campus


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


City Campus


172H School of Engineering


Sem 1 2017

Course Coordinator: Professor Hong Ren Wu

Course Coordinator Phone: +61 3 9925 5376

Course Coordinator Email:

Course Coordinator Location: B10.10.06, City Campus

Pre-requisite Courses and Assumed Knowledge and Capabilities

You will need basic proficiency in Matlab and basic knowledge of digital signal processing.

Course Description

Image Systems Engineering is an area of information science and engineering of growing importance with applications in video conferencing and mobile video phones, TV broadcasting and video streaming, radar and infrared imaging, satellite imaging, digital photography, industrial imaging systems, video surveillance and security systems, multimedia computing and retrieval, and medical imaging including CR (Computed Radiography), CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography) scan, mammography and ultrasound imaging.

This postgraduate course covers digital image fundamentals, image analysis, image processing and restoration, image compression, image segmentation and recognition techniques, and data hiding and watermarking concepts and techniques.

Objectives/Learning Outcomes/Capability Development

The objective of the course is to equip you with fundamental knowledge and basic technical competence in the field of digital image processing and image processing systems engineering.

The course will contribute to the development of your ability to work with multidisciplinary teams in diverse areas of digital imaging and image processing applications such as satellite imaging and remote sensing, industrial imaging and automated inspection systems, automated paper currency or license plate recognition and reading, biomedical imaging including X-ray, MRI, and ultrasound.

On successful completion of the course, you should

  1. Be equipped with theoretical knowledge, principles and techniques, methods and approach to image processing problems, implementation of image processing tasks via software and hardware systems. 
  2. Be able to identify which areas of knowledge are required to tackle a given task, and benchmark alternative techniques for a given problem by simulation using, e.g., Matlab. 
  3. Acquire a good understanding of image processing systems in various applications. 
  4. Acquire a good appreciation of roles of image processing and systems in a variety of applications as outlined in the course description. 
  5. Acquire good communication skills in comprehension, oral and written presentations on technical topics of image processing and systems engineering. 
  6. Have a good understanding of the history and the current state-of-the-art image processing systems and applications which constantly raise challenges to other fields of studies such as mathematics, physics, and computer systems engineering. 
  7. Possess the basic awareness of enormous potential applications of image processing to advancement of our knowledge in sciences and engineering, its benefits in policing, public safety and security, and social issues such as privacy.

Overview of Learning Activities

Individual Learning: includes attending lectures, contribution to class discussions, completing written assignments, attending and completing laboratory assignments on image processing tasks using Matlab numerical computing environment and programming language, completing lab assignment reports, and by individual study.

Assignments and Reports: require written reports in a format and style suitable for scientific and technical writing.

Overview of Learning Resources

Matlab numerical computing environment and programming language is provided for you to use during lab sessions and outside lab hours (when lab is not required for other classes).

Lecture notes and Lab instructions notes will be provided on-line as well as information on textbook and references for further reading.

You may find it convenient to acquire and install student edition of Matlab on your home computer or laptop.

Textbook: R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd Ed., Prentice-Hall, 2008.

Overview of Assessment

The assessments include laboratory work, assignments on images analysis and processing, and a final exam.