Modelling and Removal of Noise and Artefacts in Surveillance and Security Video for Forensic Image Analysis and Enhancement. This project spearheads research in advanced digital image and video processing technology, placing Australia at the forefront of both theoretical and applied research to safeguard Australia. It tackles fundamental issues identified in our earlier research in this area and consulting work for Victoria and NSW Police Departments in forensic investigations since 2000. Althou ....Modelling and Removal of Noise and Artefacts in Surveillance and Security Video for Forensic Image Analysis and Enhancement. This project spearheads research in advanced digital image and video processing technology, placing Australia at the forefront of both theoretical and applied research to safeguard Australia. It tackles fundamental issues identified in our earlier research in this area and consulting work for Victoria and NSW Police Departments in forensic investigations since 2000. Although the main investigation focuses on video surveillance and security systems for public safety, policing, crime prevention and border control, the outcomes of the investigation will have other applications, including digital photography for fine-art, medical imaging, picture archiving and communication systems for telemedicine and rural healthcare systems.Read moreRead less
Vision Model Based Perceptual Digital Video Coding. Digital video coding and compression is an enabling technology and has diversified applications in audiovisual communications, multimedia computing, digital television broadcast and electronic entertainment industries. The project aims at spearheading research in theory, techniques and implementation of perceptual video coding in order to achieve constant and guaranteed quality in visual communications and services. It will explore a new appr ....Vision Model Based Perceptual Digital Video Coding. Digital video coding and compression is an enabling technology and has diversified applications in audiovisual communications, multimedia computing, digital television broadcast and electronic entertainment industries. The project aims at spearheading research in theory, techniques and implementation of perceptual video coding in order to achieve constant and guaranteed quality in visual communications and services. It will explore a new approach to digital video coding other than the constant bit rate coding techniques which have dominated digital video research for the past four decades. It will form a part of the theoretical foundation and principles for the next generation video coding and compression techniques, and may lead to new standards and practice.Read moreRead less
Extending fuzzy logic. Fuzzy logic is good for dealing with uncertain data somewhat like people do, and this technique has been used in train braking systems, computer animation etc, but can be slow for problems with large or complex data especially if the data are changing with time. The project will design efficient fuzzy logic algorithms capable of dealing with complex real world problems.
Intelligent techniques to exploit the dynamic temporal structure in detection of attacks in credit application fraud. Obtaining credit using fraudulent information costs financial institutions billions of dollars. This project develops fraud detection methods in credit applications, working with credit bureau data. Existing fraud detection models are mostly applicable to transaction fraud, rather than application fraud, and are static. Fraudsters however constantly change their method of attack. ....Intelligent techniques to exploit the dynamic temporal structure in detection of attacks in credit application fraud. Obtaining credit using fraudulent information costs financial institutions billions of dollars. This project develops fraud detection methods in credit applications, working with credit bureau data. Existing fraud detection models are mostly applicable to transaction fraud, rather than application fraud, and are static. Fraudsters however constantly change their method of attack. The temporal characteristics of fraud attacks provide an additional source of information that can be exploited to gain increased predictive power. We propose a hybrid intelligent approach to construct models that are sensitive to the temporal dynamics of fraud attacks, and evolve to acknowledge the changing behaviour of fraudsters.Read moreRead less
On Line Real Time Inspection of Vehicle Structures. The aim of this project is to develop an automated, on-line, real-time, inspection system that can detect incorrect placement or absence of specific components on the underside of a vehicle structure. The inspection system is to be integrated with a factory wide quality control and information gathering system. Development of an automated inspection system will enable the reliable identification of defects and tracking of quality levels in the ....On Line Real Time Inspection of Vehicle Structures. The aim of this project is to develop an automated, on-line, real-time, inspection system that can detect incorrect placement or absence of specific components on the underside of a vehicle structure. The inspection system is to be integrated with a factory wide quality control and information gathering system. Development of an automated inspection system will enable the reliable identification of defects and tracking of quality levels in the final assembly station. The expected outcome is the design and implementation in prototype form, of an intelligent, automated inspection system that can accommodate a wide range of product variants.Read moreRead less
Ensembles of Collaborative Neural Networks. Artificial neural networks have been used successfully for data mining and control. A neural network ensemble(NNE) is a collection of networks that exhibits properties of self-organization, plasticity, and adaptive behaviour. The aim of this research is to develop an efficient and theoretically sound algorithm for NNE learning. The outcomes of the project will include insights into self-organization of complex NNE and automatic problem decomposition an ....Ensembles of Collaborative Neural Networks. Artificial neural networks have been used successfully for data mining and control. A neural network ensemble(NNE) is a collection of networks that exhibits properties of self-organization, plasticity, and adaptive behaviour. The aim of this research is to develop an efficient and theoretically sound algorithm for NNE learning. The outcomes of the project will include insights into self-organization of complex NNE and automatic problem decomposition and an efficient algorithm for constructing and training NNE. Practical outcomes will include research training for early career researchers and new modelling tools for data mining, robotics and multi-agent systems. The project contributes to the national priority area of smart information use.Read moreRead less
Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quan ....Automatic detection of the circle of Willis in neuro-images using multi-scale gradient calculation and knowledge-based genetic algorithms. Stroke is the third most common cause of death and a major contributor to long term disability in Australia. The most efficient way of preventing stroke from happening is to detect related symptoms early. The group of cerebral blood vessels that closely related to strokes is the circle of Willis (CoW). We build a system that can automatically detect and quantify CoW in neuroimages, providing ways of preventing strokes from happening. The project will enhance Australia¡¯s leading position in promoting and maintaining good health, especially in preventive healthcare.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algor ....A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.Read moreRead less
An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed all ....An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed allowing the estimation of the proximity of objects for road safety and rating. The expected outcome will be new identification techniques and software which can be incorporated with road data collection systems.Read moreRead less