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.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Understand and apply basic knowledge, methods and skills in image processing and computer vision.
- Identify, formulate and solve problems in image processing and computer vision.
- Analyse, evaluate and examine existing practical computer vision algorithms.
- Design and develop practical and innovative image processing and computer vision applications or systems.
- Communicate effectively to both specialist and non-specialist audiences, explaining concepts related to visual information processing systems.
- Conduct themselves professionally and responsibly in the areas of computer vision, image processing and deep learning.
Other Information
Professional Skills Mapping:
Mapping of Learning Outcomes to Assessment and Professional Competencies
Indicative Assessment
- Assignment 1 (15) [LO 1,2,5,6]
- Assignment 2 (15) [LO 1,2,3,5]
- Assignment 3 (15) [LO 1,2,5,6]
- Final Exam (55) [LO 1,2,3,4,5]
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.
Workload
130 hours consisting of lecture/tutorials, labs, and self-study.
Inherent Requirements
NA
Requisite and Incompatibility
Prescribed Texts
None
Preliminary Reading
Computer Vision: Algorithms and Applications - Szeliski.org
Assumed Knowledge
- Basic calculus, linear algebra and probability theory.
- Moderate programming experience in Python.
- COMP3670 (Introduction to Machine Learning) is highly recommended.
Minors
Fees
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:
- 2
- 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.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2025 | $5280 |
- International fee paying students
| Year | Fee |
|---|---|
| 2025 | $6720 |
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 3893 | 17 Feb 2025 | 24 Feb 2025 | 31 Mar 2025 | 23 May 2025 | In Person | View |