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Australian State/Territory : QLD
Field of Research : Pattern Recognition
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  • Funded Activity

    Discovery Projects - Grant ID: DP0879814

    Funder
    Australian Research Council
    Funding Amount
    $220,000.00
    Summary
    Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries .... Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries in Australia. More specifically, it is to be applied here to classify brain tumours and detect network intruders. This cross-disciplinary project will contribute to Australia's economic of public health, protect Australia from crime, and strength Australian researchers' capacity and capability of participating in this emerging science.
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    Funded Activity

    Discovery Projects - Grant ID: DP0557387

    Funder
    Australian Research Council
    Funding Amount
    $162,000.00
    Summary
    Enhanced Multilingual Speaker Recognition through the Incorporation of High-Level Features, Late Fusion and Discriminative Classification Methods. The development of robust multilingual speaker recognition systems will benefit the community through the elimination of fraud incurred by financial institutions and customers by enabling several person authentication applications such as: voice based signatures and document issuance; credit card verification by voice and secure over-the-phone financi .... Enhanced Multilingual Speaker Recognition through the Incorporation of High-Level Features, Late Fusion and Discriminative Classification Methods. The development of robust multilingual speaker recognition systems will benefit the community through the elimination of fraud incurred by financial institutions and customers by enabling several person authentication applications such as: voice based signatures and document issuance; credit card verification by voice and secure over-the-phone financial transactions. The technology will also assist in the protection of the community and safeguard Australia by enabling the implementation of the following: suspect identification using voice print; national security measures for combating terrorism by using voice to locate and track terrorists; preemptive criminal activity counter-measures; surveillance and secure building access by voice.
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    Funded Activity

    Special Research Initiatives - Grant ID: SR0354604

    Funder
    Australian Research Council
    Funding Amount
    $10,000.00
    Summary
    ARC Network in Imaging Science and Technology. The ARC Network in Imaging Science and Technology is a field of research network covering the fundamental science and technological development of applied imaging systems. The network will encompass all aspects of the imaging sciences from image formation, through image processing and analysis, and on to image visualisation. In particular, the network will focus on a number of application areas that utilise these core technologies: medical imaging; .... ARC Network in Imaging Science and Technology. The ARC Network in Imaging Science and Technology is a field of research network covering the fundamental science and technological development of applied imaging systems. The network will encompass all aspects of the imaging sciences from image formation, through image processing and analysis, and on to image visualisation. In particular, the network will focus on a number of application areas that utilise these core technologies: medical imaging; surveillance and security; materials science and metallurgy; environmental monitoring; and consumer imaging. In this way, the network will provide an environment for creative inter-disciplinary research to the socio-economic benefit of Australia.
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    Funded Activity

    Discovery Projects - Grant ID: DP0879250

    Funder
    Australian Research Council
    Funding Amount
    $275,000.00
    Summary
    Improved detection and characterisation of breast cancer using magnetic resonance imaging, and novel image analysis and pattern recognition techniques. Breast cancer is a leading cause of death in Australian women. With no clear cause, one mainstay of management has been early detection. Newer medical imaging technologies such as magnetic resonance imaging require complex analysis to achieve their full benefit. Should the computationally demanding analyses of these images provide more sensitive .... Improved detection and characterisation of breast cancer using magnetic resonance imaging, and novel image analysis and pattern recognition techniques. Breast cancer is a leading cause of death in Australian women. With no clear cause, one mainstay of management has been early detection. Newer medical imaging technologies such as magnetic resonance imaging require complex analysis to achieve their full benefit. Should the computationally demanding analyses of these images provide more sensitive and specific detection of early cancers, the potential reductions in morbidity and mortality from breast cancer will be of immense value. Successful implementation of the proposed project will further enhance Australia's position as a world leader in biomedical research and application of computational technologies to health problems.
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    Funded Activity

    Special Research Initiatives - Grant ID: SR0567196

    Funder
    Australian Research Council
    Funding Amount
    $55,000.00
    Summary
    Improved early detection of breast cancer enabled by grid-computing and advanced modelling and visualisation of MR images. This project will investigate the utility of grid computing in the detection of breast cancer from magnetic resonance (MR) images. The large quantity of data acquired using MR imaging is difficult for clinicians to review and the cost of missed or incorrect detection is high. To provide rapid visualisation and assessment of the acquired data, grid computing will be used in c .... Improved early detection of breast cancer enabled by grid-computing and advanced modelling and visualisation of MR images. This project will investigate the utility of grid computing in the detection of breast cancer from magnetic resonance (MR) images. The large quantity of data acquired using MR imaging is difficult for clinicians to review and the cost of missed or incorrect detection is high. To provide rapid visualisation and assessment of the acquired data, grid computing will be used in conjunction with interactive visualisation with haptic feedback. Grid computing experience and haptic device expertise will be achieved via Swedish collaborators. The successful outcome of this project will be software for the production of 3D colour-coded breast images in which suspicious regions are highlighted and can be physically interrogated using the haptic device.
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    Funded Activity

    Linkage Projects - Grant ID: LP0774994

    Funder
    Australian Research Council
    Funding Amount
    $501,000.00
    Summary
    Multi-modal, Multi-dimensional Virtual Microscopy for Diagnostic Quantitative Pathology. This project will contribute to the development of a new generation of virtual microscopy (VM) systems that provide new and innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of pathology. These systems have the potential to significantly enhance the efficiency and efficacy of not only primary diagnostic workflows, but also aspects of profi .... Multi-modal, Multi-dimensional Virtual Microscopy for Diagnostic Quantitative Pathology. This project will contribute to the development of a new generation of virtual microscopy (VM) systems that provide new and innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of pathology. These systems have the potential to significantly enhance the efficiency and efficacy of not only primary diagnostic workflows, but also aspects of proficiency testing and continuing education vital for a vibrant, well regulated discipline. In addition, the project will contribute to our knowledge of the pathology assessed in the screening and diagnosis of cancers such as cervical, lung and bladder cancers.
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    Funded Activity

    Discovery Projects - Grant ID: DP0451129

    Funder
    Australian Research Council
    Funding Amount
    $195,000.00
    Summary
    Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer me .... Privacy Preserving Data Mining as Autonomous Data Analysis Expands into Safeguarding from Threats like Crime and Terrorism. In a world characterized by digitally coded data, Data Mining allows automatic exploration of huge numbers of personal records for target marketing, as well as demographic, medical and criminal research. Investigations into terrorist attacks use this technology, but a balance with privacy protection is necessary. Even if names and unique identifiers are removed, computer methods can be used to infer confidential information about individuals. This project develops new techniques to ensure privacy and alleviate public concerns such as secondary use of personal data. We shall develop new methods for replacing original data with data that exhibits approximately the same patterns, but conceals sensitive data.
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    Funded Activity

    Discovery Projects - Grant ID: DP0877929

    Funder
    Australian Research Council
    Funding Amount
    $196,000.00
    Summary
    Feature-Level Fusion with Incomplete Data for Automatic Person Identification. This research addresses the current key problems in automated person recognition with incomplete data using multiple traits. The outcomes of this research will not only make a significant contribution to fundamental theory but also result in a wide range of crime and terrorism preventing applications including police database searching, access control, security monitoring and surveillance. They can be used either by p .... Feature-Level Fusion with Incomplete Data for Automatic Person Identification. This research addresses the current key problems in automated person recognition with incomplete data using multiple traits. The outcomes of this research will not only make a significant contribution to fundamental theory but also result in a wide range of crime and terrorism preventing applications including police database searching, access control, security monitoring and surveillance. They can be used either by police and law enforcement agencies, or at places of airport, government buildings, military facilities and even sensitive areas in offices and factories. It will help reduce crime, enhance the security of the nation to a world-advanced level, and generate new industry and export opportunities for Australian security industry.
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    Funded Activity

    Discovery Projects - Grant ID: DP0774601

    Funder
    Australian Research Council
    Funding Amount
    $170,000.00
    Summary
    RadioGenes2: Modelling complex biomolecular interactions in radiated tumours: Towards understanding the genesis of therapeutic radioresistance. About 45% of bladder cancer patients require radiotherapy or surgery. Radiotherapy has a failure rate of ~50%. Surgery (bladder removal) diminishes quality of life considerably. Modelling complex gene interactions in radiated cancer cells will provide crucial knowledge on the molecular genesis of radiotherapy-resistance of tumours. Our findings will prov .... RadioGenes2: Modelling complex biomolecular interactions in radiated tumours: Towards understanding the genesis of therapeutic radioresistance. About 45% of bladder cancer patients require radiotherapy or surgery. Radiotherapy has a failure rate of ~50%. Surgery (bladder removal) diminishes quality of life considerably. Modelling complex gene interactions in radiated cancer cells will provide crucial knowledge on the molecular genesis of radiotherapy-resistance of tumours. Our findings will provide: (i) an accurate mathematical/computational model for diagnosing radiosensitivity; (ii) further insights to be applied in the pharmaceutical sector such as the discovery of novel molecular targets that have the potential to increase radiotherapy success ratios; (iii) a holistic modelling technique applicable to a larger diversity of tumours.
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    Funded Activity

    Discovery Projects - Grant ID: DP0451091

    Funder
    Australian Research Council
    Funding Amount
    $165,000.00
    Summary
    Face recognition under varying pose and lighting--towards automatic personal identification for surveillance systems. One of the key remaining problems in computerized human face recognition is the need to handle the variability in appearance due to changes in pose. This proposed research targets at identifying a person with a face image in a pose different from the example view by using a novel texture analysis and synthesis technique. This technique makes use of facial textures at different vi .... Face recognition under varying pose and lighting--towards automatic personal identification for surveillance systems. One of the key remaining problems in computerized human face recognition is the need to handle the variability in appearance due to changes in pose. This proposed research targets at identifying a person with a face image in a pose different from the example view by using a novel texture analysis and synthesis technique. This technique makes use of facial textures at different viewing directions and can recover appropriate textures for virtual views in arbitrary poses. The successfulness of the proposed research would make a technical breakthrough towards solving the major remaining problem in face recognition.
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