Computer Vision is an important field of Artificial Intelligence concerned with automatically extracting useful information from images or videos. The explosive growth in digital imaging technologies, vision datasets and deep learning makes the problems of automated image interpretation even more exciting and more relevant than ever. This course introduces students to fundamental problems in computer vision and foundational techniques for solving them. 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, object recognition, deep learning and neural networks. The course features extensive practical components including computer labs that provide students with the opportunity to practice and refine their skills in computer vision.
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Requisite and Incompatibility
To enrol in this course you must be studying Master of Engineering. Incompatible with COMP6528 and COMP4528 and ENGN4528.
Referenced entries (3)
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- COMP6528 · 2027
- COMP4528 · 2027 · saved source not linked
- ENGN4528 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Understand and proficiently apply specialised knowledge, methods and skills in image processing and computer vision applications, research and development.
Identify, formulate and innnovatively solve problems in image processing and computer vision.
Critically analyse, evaluate and examine existing practical computer vision systems.
Design and develop practical and innovative image processing and computer vision applications or systems using advanced knowledge and research techniques.
Communicate effectively to both specialist and non-specialist audiences, explaining and synthesising complex concepts related to visual information processing systems.
Conduct themselves professionally and responsibly in the areas of computer vision, image processing and deep learning.
Critically read, review and assess scientific literature in the field and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | First Semester | 3772 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 5352 | 21 Feb 2028 | 28 Feb 2028 | 31 Mar 2028 | 26 May 2028 | In Person | N/A |
Complete offering text
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. The list of offerings for future years is indicative only. Class summaries, if available, can be accessed by clicking on the View link for the relevant class number. 2027 2028 First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 3772 22 Feb 2027 01 Mar 2027 31 Mar 2027 28 May 2027 In Person N/A First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 5352 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
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