The goal of Computer Vision is to enable the computer or AI agent to ‘see’ and ‘understand’ the world like if not better than human beings. To that end, the computer needs to use sensors such as RGB or depth sensors to interact with the world. It mainly includes the understanding of the environment (scene understanding), humans, and further interaction with humans. Mapping to specific computer vision problems, this course will cover advanced topics in computer vision, such as 1) Scene Understanding, 2)Graphical Models, 3)3D visual perception , 4) Human Analysis and modeling. This course will review current research literature in the above fields and update students with state-of-the-art techniques. Students will work on group projects related to concrete research problems and present their research results in the form of seminars.
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Requisite and Incompatibility
To enrol in this course you must have completed: COMP6528 or COMP4528 or ENGN4528. You are not able to enrol in this course if you have previously completed ENGN8501.
Referenced entries (4)
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- COMP6528 · 2027
- COMP4528 · 2027 · saved source not linked
- ENGN4528 · 2027 · saved source not linked
- ENGN8501 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe and analyze the main research challenges in the field of computer vision.
Summarize research literature and state-of-the-art techniques for solving the challenging research problems in those areas.
Model and formulate problems, propose effective solutions to the problem and implement algorithms using suitable programming languages.
Design network structure and loss functions in cases where problems need to be solved using deep learning techniques.
Analyze the results and effectively evaluate the results on benchmark datasets.
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 | Second Semester | 10149 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |
| 2028 | Second Semester | 11050 | 24 Jul 2028 | 31 Jul 2028 | 31 Aug 2028 | 27 Oct 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 Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 10149 26 Jul 2027 02 Aug 2027 31 Aug 2027 29 Oct 2027 In Person N/A Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 11050 24 Jul 2028 31 Jul 2028 31 Aug 2028 27 Oct 2028 In Person N/A
Source evidence (2 saved copies)
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assets/Courses/Artificial Intelligence/8000/Advanced Topics in Computer Vision/Advanced Topics in Computer Vision - ANU.html
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