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Australian State/Territory : ACT
Research Topic : Computer Graphics
Field of Research : Signal Processing
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  • Funded Activity

    Discovery Projects - Grant ID: DP150104645

    Funder
    Australian Research Council
    Funding Amount
    $370,100.00
    Summary
    Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vecto .... Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vector conversion methods. It is expected to develop a framework where semantic labels and hyperlinks can be embedded in visual data automatically. It hopes to pioneer the creation of a web of images where the links are on image/video regions. New image simplification, stylisation, and non-photorealistic rendering methods are expected to be provided.
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    Funded Activity

    Discovery Projects - Grant ID: DP0773924

    Funder
    Australian Research Council
    Funding Amount
    $415,000.00
    Summary
    Nonlinear Signal Processing: Optimisation and Tracking on Manifolds. Most hi-tech electronic devices must process signals. A mobile phone, for example, must encode, transmit, decode and receive voice signals. This project will use specialised mathematical theories applied in novel ways to advance the theoretical foundations of signal processing and develop better signal processing algorithms for practical applications. Companies with access to better signal processing algorithms have an edge ov .... Nonlinear Signal Processing: Optimisation and Tracking on Manifolds. Most hi-tech electronic devices must process signals. A mobile phone, for example, must encode, transmit, decode and receive voice signals. This project will use specialised mathematical theories applied in novel ways to advance the theoretical foundations of signal processing and develop better signal processing algorithms for practical applications. Companies with access to better signal processing algorithms have an edge over their competitors, and consumers benefit too from better and more advanced products.
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    Funded Activity

    Research Networks - Grant ID: RN0459979

    Funder
    Australian Research Council
    Funding Amount
    $2,250,000.00
    Summary
    ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te .... ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
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    Funded Activity

    Linkage Projects - Grant ID: LP160100379

    Funder
    Australian Research Council
    Funding Amount
    $318,000.00
    Summary
    Spatial sound control for testing multi-channel audio devices. Spatial sound control for testing multi-channel audio devices. This project aims to test Alternative Listening Devices/Personal Sound Amplification Devices (PSAPs), multi-input audio devices fast replacing hearing aids due to their affordability and easy accessibility. With more consumers choosing PSAPs, proper testing is needed to assess the devices’ safety and benefit in real-life acoustic situations. This project will test PSAPs i .... Spatial sound control for testing multi-channel audio devices. Spatial sound control for testing multi-channel audio devices. This project aims to test Alternative Listening Devices/Personal Sound Amplification Devices (PSAPs), multi-input audio devices fast replacing hearing aids due to their affordability and easy accessibility. With more consumers choosing PSAPs, proper testing is needed to assess the devices’ safety and benefit in real-life acoustic situations. This project will test PSAPs in laboratory setups that use spatial audio processing techniques to mimic realistic acoustic environments, and develop theoretical frameworks to overcome existing limitations to accurate spatial sound reproduction. This research is expected to provide innovative solutions to safeguard Australia's future hearing health.
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    Funded Activity

    Discovery Projects - Grant ID: DP140100793

    Funder
    Australian Research Council
    Funding Amount
    $270,000.00
    Summary
    Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. 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 ai .... Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. 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.
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