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Computer Vision and Image Analysis PG (8890.3)

Level: Postgraduate Level
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


Possible changes to your unit's learning activities and assessment items

For the remainder of 2020, resulting from Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, any exams that are required for assessment in a unit will be online exams. Online exams may also use online proctoring to help assure the academic integrity of those exams. Please contact your unit convener with any questions.

While the University has made efforts to ensure that Unit Outlines reflect a unit’s learning activities and assessment items, any changes to Australian Government directives because of the COVID-19 pandemic may require changes to these during the semester to ensure the safety and well being of students and staff. These changes will not be updated in the published unit outline, but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. Any changes made will continue to meet the unit’s learning outcomes, as described in the Unit Outline.

Unit Outlines

To view your Unit Outline, click View to log in to MyUC and access this information, or visit your unit's online teaching site.

  • Semester 1, 2020, ON-CAMPUS, BRUCE (193403) - View
  • Semester 1, 2019, ON-CAMPUS, BRUCE (185102) - View
  • Semester 1, 2018, ON-CAMPUS, BRUCE (182286) - View
  • Semester 1, 2017, ON-CAMPUS, BRUCE (165683) - View
  • Semester 1, 2016, ON-CAMPUS, BRUCE (153804) - View
  • Semester 1, 2015, ON-CAMPUS, BRUCE (145926) - View

If a link to your Unit Outline is not displayed, please check back later. Unit Outlines are generally published by Week One of the relevant teaching period.


The aim of this research-led education unit is to provide students with an overview of fundamental areas of computer vision and image analysis as well as in-depth knowledge of selected research topics, which will be explored in theory and practice.

Through specific, applied examples, students will explore this highly topical field in the context of a widespread use of digital camera and image technology. These include examples from application areas such as digital photography, digital image enhancement, computer graphics, object recognition, object tracking, image segmentation, visual motion estimation, and multi-view camera systems.

As computer vision and image analysis draw from other related fields, such as perceptual psychology, digital signal processing, human-computer interaction, artificial intelligence and pattern recognition, students will be able to explore relevant theories and algorithms in these areas.

Learning Outcomes

On successfully completing the unit, students will have a sound understanding of and will have gained hands-on experience in:

1. What computer vision and image analysis entails;

2. How images are formed and represented;

3. Understanding the basics of image processing and analysis techniques;

4. Understanding the concepts of fundamental theories in computer vision;

5. Writing Matlab programs for performing computer vision and image analysis tasks;

6. Being able to choose appropriate computer vision and image analysis techniques to solve real-world problems; and

7. Understanding the relationships between computer vision and image analysis on the one hand and fields such as perceptual psychology, digital signal processing, artificial intelligence and pattern recognition on the other hand.

Assessment Items

Contact Hours

5 hours per week in Winter Term 3.5 hours per week in normal semester mode.





Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

Equivalent Units


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