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Field of Research : Computer Vision
Research Topic : Cloud computing
Australian State/Territory : VIC
Australian State/Territory : ACT
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  • Funded Activity

    Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156

    Funder
    Australian Research Council
    Funding Amount
    $210,000.00
    Summary
    Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.
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    Funded Activity

    Linkage Projects - Grant ID: LP120100595

    Funder
    Australian Research Council
    Funding Amount
    $145,000.00
    Summary
    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.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP210200213

    Funder
    Australian Research Council
    Funding Amount
    $540,000.00
    Summary
    Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honey .... Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honeybees to provide essential ecosystem services is informed by transferable, standardised data acquisition and management techniques that maintain bee health and maximise pollination. The anticipated outcomes are higher fruit yields and quality, and a beneficial step-change in industry productivity and profitability.
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