UWA Handbook 2027

Unit Overview

Description

Computer Vision is the science of enabling computers to automatically interpret, understand, and make decisions from visual data, including images and videos. It integrates concepts from image processing, machine learning, pattern recognition, geometry, and artificial intelligence to extract meaningful information from visual observations and support intelligent decision-making. It has numerous applications, including autonomous vehicles, robotics, medical image analysis, industrial inspection, precision agriculture, surveillance and security, augmented and virtual reality, sports analytics, and human-computer interaction.

This unit introduces the fundamental principles of computer vision as well as contemporary machine learning based computer vision techniques. Topics include image representation and processing, image filtering and enhancement, feature detection and description, image segmentation, object detection and recognition, motion and tracking, camera calibration, projective geometry, and stereo-based 3D reconstruction.

Students will gain both theoretical understanding and practical experience in designing, implementing, and evaluating computer vision algorithms. Through hands-on laboratory sessions and project, students will learn how to apply computer vision to solve real-world problems. As visual sensing becomes increasingly pervasive in industry and society, demand for professionals with expertise in computer vision and AI continues to grow. This unit equips students with the foundational knowledge and practical skills required for such careers.

Credit

6 points

Offering
(see Timetable)

Availability	Location	Mode
Semester 1	UWA (Perth)	On-campus

The timetable for this teaching period is not currently available. Please see the Important Dates page for the timetable release date and other key date information.

Outcomes

Students are able to (1) explain the fundamental principles of image formation, image analysis, camera calibration and projective geometry; ; (2) analyse and clearly communicate computer vision problems, methodologies, and results; (3) design, implement, and evaluate computer vision algorithms and software solutions; (4) apply computer vision techniques to practical applications and assess their performance and limitations; (5) apply critical thinking and problem-solving skills to develop solutions for real-world computer vision problems; and (6) critically evaluate and compare classical and modern machine learning-based computer vision solutions..

Assessment

Indicative assessments in this unit are as follows: (1) practical projects; (2) mid-semester test; and (3) final exam. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)

Professor Ajmal Mian

Unit rules

Prerequisites

Enrolment in

HON-CMSSE Computer Science and Software Engineering
or 62530 Master of Data Science

or

62550 Master of Professional Engineering and SP-EBIOM Biomedical Engineering specialisation
or SP-EELEC Electrical and Electronic Engineering specialisation
or SP-ESOFT Software Engineering specialisation

or

53560 Master of Physics and SP-MEDPH Medical Physics
or 73660 Master of Medical Physics

or

BH008 Bachelor of Advanced Computer Science [Honours] and MJD-ARIDM Artificial Intelligence

or

Bachelor of Engineering (Honours) or an associated Combined Degree
and Successful completion of

96 points

and CITS2401 Computer Analysis and Visualisation
or CITS1401 Computational Thinking with Python
or CITX1401 Computational Thinking with Python

Incompatibility

CITS4240 Computer Vision

Advisable prior study

CITS2401 Computer Analysis and Visualisation
And MATH1012 Mathematical Theory and Methods.
Students must have the ability to program in a high-level programming language and the ability to reason in linear algebra and calculus.