This subject introduces students to understanding of the fundamental problems in computer vision, and their state-of-the-art solutions. Topics covered in detail include: camera geometry, image formation, image filtering, thresholding and image segmentation, edge, point and line detection, geometric frameworks for vision, single view and two views geometry; 3D modelling and reconstruction, camera calibration; stereo vision, motion and optical flow; object recognition, appearance based scene recognition; pose estimation in perspective images, etc. The course is featured by an extensive practical component including computer labs and term projects that provides the students with a tool box of skills in image processing and computer vision.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
1. Understand the foundations of modern computer vision theory, problem and state of the art solutions.2. Implement and test some fundamental computer vision algorithms e.g. image filtering, restoration, image segmentation, camera calibration.
3. Analyse and evaluate critically the building and integration of computer vision algorithms and systems.
4. Design and demonstrate a working computer vision system through team research project, and project report, presentation.
5. Continue to critically review and assess scientific literature and apply the knowledge and skills gained from the course in developing innovative applications.
Professional Skills Mapping:
Mapping of Learning Outcomes to Assessment and Professional Competencies
Indicative Assessment
- Laboratories (30%)
- Term Project (30%)
- Examinations (40%)
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.
Requisite and Incompatibility
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 Engineering, Computer Science,
and Mathematics backgrounds.
Majors
Minors
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and 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). You can find your student contribution amount for each course 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 |
|---|---|
| 2017 | $3852 |
- International fee paying students
| Year | Fee |
|---|---|
| 2017 | $5130 |
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 |
|---|---|---|---|---|---|---|
| 3034 | 20 Feb 2017 | 27 Feb 2017 | 31 Mar 2017 | 26 May 2017 | In Person | N/A |
