- Code ENGN6528
- Unit Value 6 units
- Offered by School of Engineering
- ANU College ANU College of Engineering Computing & Cybernetics
- Course subject Engineering
- Areas of interest Computer Science, Engineering, Mechatronics, Advanced Computing
- Academic career PGRD
- Dr Miaomiao Liu
- Mode of delivery In Person
First Semester 2023
See Future Offerings
In 2023, this course is on campus with remote adjustments only for participants with unavoidable travel restrictions/visa delays.
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.
Upon successful completion, students will have the knowledge and skills to:
- Proficiently apply specialised knowledge, methods and skills in image processing and computer vision applications, research and development.
- Identify, formulate and innnovatively solve problems in image processing and computer vision.
- Critically analyse, evaluate and examine existing practical computer vision systems.
- Communicate effectively to both specialist and non-specialist audiences to integrate and synthesize complex visual information processing systems.
- Critically review and assess scientific literature in the field and and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
- Apply research methods and advanced knowledge to design and develop practical and innovative image processing and computer vision applications or systems.
- Conduct themselves professionally and responsibly in the areas of computer vision, image processing and deep learning.
Professional Skills Mapping:
- Labs (30) [LO 1,2,3,4,5,6,7]
- Project (40) [LO 1,2,3,4,5,6,7]
- Quiz (30) [LO 1,2,3,4,5,6,7]
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10 hours per week; which consists of 3 hours lecture/tute time, 2 hours lab time, and the rest are for project and self-study.
Information on inherent requirements for this course are currently not available.
Requisite and Incompatibility
- 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.
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:
- 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.
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