Overview

This unit aims to develop an understanding of methods for extracting useful information (eg 3-D structure; object size, motion, shape, location and identity, etc) from images. It will allow you to understand how to construct computer vision systems for robotics, surveillance, medical imaging, and related application areas.

Offerings

S1-01-CLAYTON-FLEXIBLE
S1-01-MALAYSIA-ON-CAMPUS

Contacts

Chief Examiner(s)

Dr Michael Burke

Unit Coordinator(s)

Dr Michael Burke
Dr Maxine Tan

Learning outcomes

On successful completion of this unit, you should be able to:
1.

Interpret and apply mathematical optimisation, linear algebra, and supervised and unsupervised learning to computer vision problems.

2.

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.

3.

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.

4.

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.

5.

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.

6.

Demonstrate the development, training and deployment of computer vision algorithms using a high-level programming language.

Teaching approach

Peer assisted learning
Problem-based learning
Enquiry-based learning
Active learning

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

1 - Lab assessments
2 - Quizzes
3 - Final assessment

Scheduled and non-scheduled teaching activities

Laboratories
Workshops

Workload requirements

Workload

Learning resources

Recommended resources

Availability in areas of study

E3001 Bachelor of Engineering (Honours) - Specialisation: Robotics and mechatronics engineering (Artificial intelligence stream)
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