• Offered by RS Electrical, Energy and Materials Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Classification Advanced
  • Course subject Engineering
  • Areas of interest Engineering
  • Academic career PGRD
  • Course convener
    • AsPr Hongdong Li
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in 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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Understand the foundations of modern computer vision theory, problem and state of the art solutions. 
  2. Implement and test some fundamental computer vision algorithms e.g. image filtering, restoration, image segmentation, camera calibration.
  3. Analyse and evaluate critically the building and integration of computer vision algorithms and systems.
  4. Design and demonstrate a working computer vision system through  team research project, and project report, presentation. 
  5. Continue to critically review and assess scientific literature and apply the knowledge and skills gained from the course in developing innovative applications.

Professional Skills Mapping
Mapping of Learning Outcomes to Assessment and Professional Competencies

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Requisite and Incompatibility

To enrol in this course you must be studying Master of Engineering. Incompatible with ENGN4528.

Specialisations

Fees

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:
2
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.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2019 $4320
International fee paying students
Year Fee
2019 $5700
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

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.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
2829 25 Feb 2019 04 Mar 2019 31 Mar 2019 31 May 2019 In Person N/A

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