- Code ENGN8501
- Unit Value 6 units
- Offered by RS Electrical, Energy and Materials Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Areas of interest Computer Science, Engineering, Electronics, Computer Systems, Computer Engineering
- Academic career PGRD
- Mode of delivery In Person
- Offered in See Future Offerings
This course will be offered annually in Semester 2 from 2020 onwards.
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.
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.
- Literature Reading Reports (50) [LO null]
- Research/Design Project (30) [LO null]
- Seminar Presentation (20) [LO null]
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Requisite and Incompatibility
The reading list includes the most recent following conference proceedings for computer vision and machine learning, which are
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
International Conference on Computer Vision (ICCV)
European Conference on Computer Vision (ECCV)
Advances in Neural Information Processing Systems (NIPS)
International Conference on Learning Representations (ICLR)
Students are expected to have experience in undergraduate-level mathematics and programming skills. Basic knowledge of computer vision and/or machine learning, for example acquired through the courses Computer Vision and/or Statistical Machine Learning is also required.
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:
- 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.
Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class start date
|Last day to enrol
|Class end date
|Mode Of Delivery
|27 Jul 2020
|03 Aug 2020
|31 Aug 2020
|30 Oct 2020