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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

We are currently working to resolve an issue where some unit outline links are unavailable. If the link to your unit outline does not appear below, please use the link in your Canvas site. If no link is available on your Canvas site, please contact your unit coordinator.

Unit of study_

AMME4710: Computer Vision and Image Processing

This unit of study introduces students to vision sensors, computer vision analysis and digital image processing. This course will cover the following areas: fundamental principles of vision sensors such as physics laws, radiometry, CMOS/CDD imager architectures, colour reconstruction; the design of physics-based models for vision such as reflectance models, photometric invariants, radiometric calibration. This course will also present algorithms for video/image analysis, transmission and scene interpretation. Topics such as image enhancement, restoration, stereo correspondence, pattern recognition, object segmentation and motion analysis will be covered.

Code AMME4710
Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
MTRX3700 OR MECH4720 OR MECH5720
Assumed knowledge:
The unit assumes that students have strong skills in MATLAB.

At the completion of this unit, you should be able to:

  • LO1. demonstrate skills in presenting a final design solution to a computer vision/image processing problem
  • LO2. demonstrate skills in working on a design project within a team including communicating with team members, planning, and managing tasks
  • LO3. design an engineering solution to a given image processing task by selecting, developing and evaluating appropriate algorithms and techniques.
  • LO4. understand the fundamental principles of how images are formed including the basics of image sensors, radiometry, colour, and projective geometry
  • LO5. apply basic techniques in image processing including the use of image filtering, features, edge detection, colour spaces/transforms, and matching
  • LO6. apply advanced techniques in computer vision including stereo vision, 3D mapping, object detection, image classification, and use of machine learning algorithms in vision
  • LO7. apply a wide range of image processing techniques to real world applications
  • LO8. understand the type of algorithm required for a particular image processing task.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.

There are no unit outlines available online for previous years.