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Science and Engineering a university for the real world

Overview

Our discipline makes sense of the world’s data streaming in from cameras, devices, and sensors.

Our expertise in machine learning, computer vision and signal processing allows us to develop systems to automatically interpret the world, its people and their activities from visual and audio sources.

We improve our ability to interact and sense our environment through advanced wireless communications and electromagnetics.

We aim to improve human health through AI, medical imaging and signal processing.

Our experts

Our discipline brings together a diverse team of experts who deliver world-class education and achieve breakthroughs in research.

Explore our staff profiles to discover the amazing work our researchers are contributing to.

Professor Clinton Fookes
Discipline Leader, Vision and Signal Processing

Meet our experts

Professor Wageeh Boles
Position
Professor
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research field
Artificial Intelligence and Image Processing
Email
Professor Clinton Fookes
Position
Professor
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research field
Artificial Intelligence and Image Processing
Email
Professor Bouchra Senadji
Position
Professor
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Electrical and Electronic Engineering
Mechanical Engineering
Email
Dr Jasmine Banks
Position
Senior Lecturer
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Artificial Intelligence and Image Processing
Computer Hardware
Electrical and Electronic Engineering
Email
Dr Jacob Coetzee
Position
Snr Lecturer Electrical Engineering
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research field
Electrical and Electronic Engineering
Email
Dr Simon Denman
Position
Senior Lecturer
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Artificial Intelligence and Image Processing
Electrical and Electronic Engineering
Email
Dr Dhammika Jayalath
Position
Senior Lecturer in Electrical Engineering
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Electrical and Electronic Engineering
Communications Technologies
Curriculum and Pedagogy
Email
Dr Sabesan Sivapalan
Position
Associate Lecturer
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Email
Dr Kien Nguyen Thanh
Position
Research Fellow
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Computation Theory and Mathematics
Electrical and Electronic Engineering
Email
Adjunct Professor Peyman Moghadam
Position
Adjunct Professor
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research fields
Artificial Intelligence and Image Processing
Other Engineering
Other Information and Computing Sciences
Email
Emeritus Professor Sridha Sridharan
Position
Adjunct Professor
Division / Faculty
Vision and Signal Processing,
School of Electrical Engineering, Computer Science
Research field
Artificial Intelligence and Image Processing
Email

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Speech, Audio, Image, Video and Technology

Our researchers conduct postgraduate training, industrial consultancy and product development in the areas of speech, audio, image and video technologies.

A major focus of our research is in applying machine learning and pattern recognition techniques to solve real world problems in computer vision and speech and language processing.

Visit the research page

Radio Frequency Laboratory

Our radio frequency laboratory hosts an echo-free chamber to help staff and students in the discipline carry out far-field antenna measurements.

This anechoic chamber supports research into the design of antenna arrays, smart antennas and the design of wireless communication systems.

The chamber is insulated from all external noise and is designed to absorb all reflections of electromagnetic waves.

Projects

Improving productivity and efficiency of Australian airports – a real time analytics and statistical approach

Project leader

Professor Clinton Fookes

Dates

2015-2019

Project summary

Aviation is a major economic driver both within Australia and overseas, but the aviation industry faces growing challenges from the increase in passengers and changing regulations. To meet these challenges, airports, airlines, government agencies and others need to maximise their efficiency and productivity. However, complex dependencies and differing operational objectives complicate this task.

This project aims to develop a real-time, whole-of-system operational performance framework that can help operators in finding and evaluating solutions to maximise throughput, reduce wait times and mitigate flow-on effects. Innovative new video analytic and Bayesian Network based tools are integrated to address the challenges of adaptability and uncertainty.

Monitoring intuitive expertise in the context of airport security screening

Project leader

Professor Clinton Fookes

Dates

2015-2019

Project summary

During airport security screening and processing, confusion and error are greatest when systems or contexts are unfamiliar. Poorly designed systems compromise the interactions of airport security personnel and decrease their ability to promptly and accurately respond to situations.

This project aims to deliver a suite of automated methods to monitor security operator knowledge and engagement, to assess the real-time security screening context, and to detect unusual passenger behavior at the screening check-point. This monitoring aims to provide new knowledge and techniques to enhance security operator performance, refine the screening process, improve passenger experience and, most critically, ensure safety at Australian airports.

One shot three-dimensional reconstruction of human anatomy and motion

Project leader

Professor Clinton Fookes

Dates

2017-2021

Project summary

The last three decades have seen the field of computer vision make tremendous advancements in the area of structure from motion.

Despite this progress, the reality is that current approaches are unable to accurately and densely reconstruct the most important object we care about: people.

This project will create dense 3D reconstruction techniques which can manage non-rigid human anatomy using only 2D images from video sequences and medical imaging sources, such as X-rays.

We will also create these models in one shot from a single image.

Solve it or ignore it? The challenge of alignment distortion and creating next generation automatic facial expression detection

Project leader

Emeritus Professor Sridha Sridharan

Dates

2014-2019

Project summary

The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive.

Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world's largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.

Sweet Pepper (Capsicum) Data Analytics

Project leader

Professor Clinton Fookes

Dates

2016 - Ongoing

Project summary

In vegetable and fruit production, the cost of manual labor related to harvesting can be significant to farm operations.

This project can potentially enable growers managed more efficiently their workforce and the harvesting with improved situational awareness of quantity, quality and spacial variability of the crops.

This project seeks to develop data analytics to provide capsicum growers with situational awareness of crops based on multi-spectral robotic-visual data.

This is a research component conducted by QUT as part of a project led by the Queensland Government in collaboration with QUT and CSIRO and funded by Horticulture Innovation Australia.

View our student topics

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