Semantic Authentication of Visual Data. Data authentication systems can detect the smallest modification to a message. Authentication systems for media objects such as images, and audio and video clips have a different requirement they must ensure authenticity of the content without needing all the changes to be detectable. The aims of this project are to develop a framework for design and analysis of image and video authentication systems, and construct secure and flexible systems that can be ....Semantic Authentication of Visual Data. Data authentication systems can detect the smallest modification to a message. Authentication systems for media objects such as images, and audio and video clips have a different requirement they must ensure authenticity of the content without needing all the changes to be detectable. The aims of this project are to develop a framework for design and analysis of image and video authentication systems, and construct secure and flexible systems that can be used in practice. This research addresses the urgent need of providing security for multimedia objects in electronic commerce and is of high importance to the acceptance of advanced communication and information services.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Robust and scalable change detection in geo-spatial data. A flood of data in the form of text, images and video emanate from a proliferation of sensors. These data are collected but rarely analysed, rendering it meaningless. This project aims to develop new software and techniques to detect changes over time in large scale geographically referenced data (for example photomaps) for use across numerous domains.
Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer ....Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.Read moreRead less
A data science framework for modelling disease patterns from medical images. A data science framework for modelling disease patterns from medical images. This project aims to extract models of disease patterns from medical imaging data, using deep learning, smart image processing, machine learning, and statistical modelling to quantify and model patterns conventional methods cannot detect. These disease models are expected to improve understanding of particular diseases and enable precision medi ....A data science framework for modelling disease patterns from medical images. A data science framework for modelling disease patterns from medical images. This project aims to extract models of disease patterns from medical imaging data, using deep learning, smart image processing, machine learning, and statistical modelling to quantify and model patterns conventional methods cannot detect. These disease models are expected to improve understanding of particular diseases and enable precision medicine, which recognises that there are important differences between individuals with a particular disease, and that when patients are separated into sub-populations with similar disease patterns, treatment can be tailored to these sub-populations.Read moreRead less
Omni-modality medical image analysis and visualisation. The term ‘Omni’-modality imaging (OMI) has been coined to describe the integration of multiple, complementary medical imaging modalities. However, there is currently a lack of an appropriate means to assimilate and derive maximum benefit from these integrated data. This project aims to provide a new approach to OMI data analysis and visualisation, by deriving a novel ‘level of relevance’ from the overlapping anatomical and pathological stru ....Omni-modality medical image analysis and visualisation. The term ‘Omni’-modality imaging (OMI) has been coined to describe the integration of multiple, complementary medical imaging modalities. However, there is currently a lack of an appropriate means to assimilate and derive maximum benefit from these integrated data. This project aims to provide a new approach to OMI data analysis and visualisation, by deriving a novel ‘level of relevance’ from the overlapping anatomical and pathological structures in the data which will be used to suppress superfluous data and highlight the most relevant data to maximise the information gained from the OMI data. Further, OMI visualisation is proposed to efficiently navigate through the overlapping data.Read moreRead less
Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the need ....Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the needs of automatic strawberry harvesting. This project aims to investigate high-level syntactic recognition approaches that embed high-order texture patterns of ripe fruit and hyperspectral analysis techniques to achieve partially occluded fruit recognition and grading of fruit at the level required by commercial production.Read moreRead less
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.
High quality benthic and demersal surveys from small form factor underwater robots. This project will develop improved surveying systems for environmental consultancies. By enhancing the imaging and mapping capabilities of small underwater robots and extending automated interpretation tools to work with their data, this project will reduce operating costs, and increase the quality and quantity of scientifically useful data that they generate.
Automation of species recognition and size measurement of fish from underwater stereo-video imagery. The project aims to develop algorithms to automate the processing of stereo-video images recorded to count and measure the size of fish. This will improve husbandry and monitoring for finfish aquaculture at reduced costs, create technology export for industry partners, and develop cost effective, non-destructive finfish sampling tools for marine agencies.