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, deep learning and neural networks for computer vision etc. The course features extensive practical components including computer labs and Term Research projects 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:
- Understand and master basic knowledge, theories and methods 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 systems.
- Communicate effectively and work in teams to develop a working computer vision system.
- Critically review and assess scientific literature in the field and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
- Design and develop practical and innovative image processing and computer vision applications or systems.
Other Information
Professional Skills Mapping:
Mapping of Learning Outcomes to Assessment and Professional Competencies
Indicative Assessment
- Lab Assignment 1 (15) [LO 1,2,6]
- Lab Assignment 2 (15) [LO 3,4,5,6]
- Lab Assignment 3 (15) [LO 1,2,5,6]
- Exam (55) [LO 1,2,3,5]
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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 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, Science and Mathematics backgrounds.
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 |
|---|---|
| 2024 | $4980 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
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 |
|---|---|---|---|---|---|---|
| 4250 | 19 Feb 2024 | 26 Feb 2024 | 05 Apr 2024 | 24 May 2024 | In Person | View |
