- Code ENGN4528
- 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 Information Technology, Engineering
- Academic career UGRD
- AsPr Hongdong Li
- Mode of delivery In Person
- Co-taught Course
First Semester 2019
See Future Offerings
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:On successful completion of this course, students should have the skills and knowledge to:
- Understand and master basic knowledge, theories and methods in image procesing and computer vision.
- Identify, formulate and solve problems in image procecssing 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.
- Conduct themselves professionally and responsibly in the areas of computer vision image processing and deep learning.
Professional Skills Mapping:
Mapping of Learning Outcomes to Assessment and Professional Competencies
- Seminar presentations (10%);
- Labs (30%);
- Project (30%);
- Exams (30%)
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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, and Mathematics backgrounds.
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.
- Domestic fee paying students
- International fee paying students
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.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2827||25 Feb 2019||04 Mar 2019||31 Mar 2019||31 May 2019||In Person||N/A|