• Offered by School of Computing
  • ANU College ANU College of Engineering Computing & Cybernetics
  • Classification Advanced
  • Course subject Computer Science
  • Areas of interest Computer Science, Engineering, Mechatronics, Advanced Computing
  • Academic career PGRD
  • Mode of delivery In Person
  • Co-taught Course
  • STEM Course

Computer Vision is an important field of Artificial Intelligence concerned with questions such as "how to extract information from image or video, and how to build a machine to see". Recent explosive growth of digital imaging technology, advanced computing, and deep learning makes the problems of automated image interpretation even more exciting and much more relevant than ever. This course introduces students to fundamental problems in image processing and computer vision, as well as their state-of-the-art solutions.

Topics covered in detail include: image formation, image filtering, camera geometry, thresholding and image segmentation, edge, point and feature detection, geometric frameworks for vision, single view and two views geometry; 3D visual reconstruction, camera calibration; stereo vision, image classification and object recognition, brief introduction to deep learning and neural networks for computer vision etc. The course features extensive practical components including computer labs that provide students with the opportunity to practice and refine their skills in image processing and computer vision.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Proficiently apply specialised knowledge, methods and skills in image processing and computer vision applications, research and development.
  2. Identify, formulate and innnovatively solve problems in image processing and computer vision.
  3. Critically analyse, evaluate and examine existing practical computer vision systems.
  4. Communicate effectively to both specialist and non-specialist audiences to integrate and synthesize complex visual information processing systems.
  5. Critically review and assess scientific literature in the field and and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
  6. Design and develop practical and innovative image processing and computer vision applications or systems using advanced knowledge and embedding research methods.
  7. Conduct themselves professionally and responsibly in the areas of computer vision, image processing and deep learning.

Indicative Assessment

  1. Labs (30) [LO 1,2,5,7]
  2. Assignment(s) (15) [LO 3,4,6,7]
  3. Exam (55) [LO 1,2,3,4,5,6]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.


130 hours including lectures, tutorials, laboratories and self-study.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must be studying one of: Master of Machine Learning and Computer Vision Master of Computing Master of Computing (Advanced). Incompatible with ENGN6528 and COMP4528 and ENGN4528.

Prescribed Texts


Preliminary Reading

Computer Vision: Algorithms and Applications - Szeliski.org


Assumed Knowledge

Basic calculus, linear algebra and basic probability theory.

Entry-level computer programming experience in either Matlab, Python, or C/C++.

Previous knowledge of digital signal processing or image and graphics processing will be helpful, but is not essential. 

This course is open to and welcomes students from Computer Science, Engineering, Science and Mathematics backgrounds.


Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

There are no current offerings for this course.

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