Overview
Offerings
Requisites
Contacts
Chief Examiner(s)
Unit Coordinator(s)
Learning outcomes
Interpret and apply mathematical optimisation, linear algebra, and supervised and unsupervised learning to computer vision problems.
Simulate cameras using projective and multi-view geometry to design model-based vision systems and algorithms that extract 3D and rotational information from images, alongside methods for image registration and stitching.
Differentiate between elements of the human visual system and computer vision pipelines, and reflect on the consequences for the design of algorithms for scene understanding.
Generate and document implementations of low, mid and high-level vision processes such as filtering and structure from motion, image segmentation and clustering, and model fitting and tracking.
Design ethical machine learning solutions to problems in computer vision, such as image classification, 3D reconstruction and pose estimation, object detection and semantic segmentation, by critically appraising information and publications.
Demonstrate the development, training and deployment of computer vision algorithms using a high-level programming language.
Teaching approach
Assessment summary
Continuous assessment: 40%
Final assessment: 60%
This unit contains threshold hurdle requirement that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment
Scheduled and non-scheduled teaching activities
Workload requirements
Learning resources
Availability in areas of study
Minor: Artificial intelligence in engineering
S2010 Bachelor of Applied Data Science - Applied studies: Computer systems engineering
S3003 Bachelor of Applied Data Science Advanced (Honours) - Applied studies: Computer systems engineering