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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

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
30cp of any 3000 or higher level Engineering units of study AND (ENGG1801 OR ENGG1810 OR INFO1110 OR INFO1910)
Assumed knowledge:
The unit assumes that students have strong skills in either MATLAB or Python.

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 the relevant session.