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Scheme : Linkage Projects
Field of Research : Image Processing
Research Topic : Machine tools
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  • Researchers (32)
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  • Funded Activity

    Linkage Projects - Grant ID: LP100200430

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    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.
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    Funded Activity

    Linkage Projects - Grant ID: LP0667490

    Funder
    Australian Research Council
    Funding Amount
    $650,000.00
    Summary
    Data Management Technologies for the Magnetic Resonance Imaging e-Research Grid. Howard Florey Institute researchers will collaborate with SGI's file-systems engineering team. Substantial benefits are expected from the development of techniques to support centralized and distributed processing medical image datasets. Issues requiring research include file space allocation algorithms and caching strategies. The proposed rapid database access technologies aim at solving these problems in the medic .... Data Management Technologies for the Magnetic Resonance Imaging e-Research Grid. Howard Florey Institute researchers will collaborate with SGI's file-systems engineering team. Substantial benefits are expected from the development of techniques to support centralized and distributed processing medical image datasets. Issues requiring research include file space allocation algorithms and caching strategies. The proposed rapid database access technologies aim at solving these problems in the medical imaging research context. The project attempts to 'improve data management for existing and new business applications'. This enhanced sharing of information will improve critical mass therefore fostering national and international collaboration.
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    Funded Activity

    Linkage Projects - Grant ID: LP0347960

    Funder
    Australian Research Council
    Funding Amount
    $276,000.00
    Summary
    Predictive Assessment Tool for Beach Conditions Using Video Imaging and Neural Network Analysis. Beaches are vital part of Australian lifestyle and economic development. Monitoring of variable beach conditions is vital for beach safety and for management of storm erosion. The aim of this project is to develop an assessment tool which will permit a rating system of beach conditions both from an environmental as well as a leisure aspect. This tool will be developed into a software analysis system .... Predictive Assessment Tool for Beach Conditions Using Video Imaging and Neural Network Analysis. Beaches are vital part of Australian lifestyle and economic development. Monitoring of variable beach conditions is vital for beach safety and for management of storm erosion. The aim of this project is to develop an assessment tool which will permit a rating system of beach conditions both from an environmental as well as a leisure aspect. This tool will be developed into a software analysis system which will include components of artificial intelligence to enable the system to use feedback loops to create a better prediction of the beach morphology relative to the breaking wave characteristics.
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    Funded Activity

    Linkage Projects - Grant ID: LP120200089

    Funder
    Australian Research Council
    Funding Amount
    $495,000.00
    Summary
    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.
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    Funded Activity

    Linkage Projects - Grant ID: LP100100588

    Funder
    Australian Research Council
    Funding Amount
    $805,000.00
    Summary
    Advancing Medical Image Analysis through High Performance Heterogeneous Computing, Numerical Simulation, and Novel Human Computer Interfaces. This project will link Australian researchers with a major multi-national IT company. The engagement of world-class personnel from Microsoft will provide unprecedented opportunities for graduate students to experience research in both an academic and an industrial setting. The participation of Microsoft product division offers the potential to transform th .... Advancing Medical Image Analysis through High Performance Heterogeneous Computing, Numerical Simulation, and Novel Human Computer Interfaces. This project will link Australian researchers with a major multi-national IT company. The engagement of world-class personnel from Microsoft will provide unprecedented opportunities for graduate students to experience research in both an academic and an industrial setting. The participation of Microsoft product division offers the potential to transform the outcomes of this project into widely-used software solutions. The project will pave the way for more widespread and reliable evidenced-based computer-aided diagnosis and image-guided treatment. It will produce well-trained and sought-after graduates and research associates with extensive inter-disciplinary knowledge of medical image analysis and high-performance computing.
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