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Research Topic : BIOMEDICAL APPLICATI
Field of Research : Image Processing
Australian State/Territory : VIC
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  • Funded Activity

    Linkage Projects - Grant ID: LP0348045

    Funder
    Australian Research Council
    Funding Amount
    $69,099.00
    Summary
    Monitoring, Calibration and Control of a Micro Assembly Process with Machine Vision Systems. A machine vision system will be developed and applied to monitor, calibrate, and control a novel surgical product assembly process of microscale. The machine vision system will benchmark the volumetric information of the needles, calibrate the position of the components, and control the assembly procedures. The successful implementation of the system will assist the production of finer surgical product .... Monitoring, Calibration and Control of a Micro Assembly Process with Machine Vision Systems. A machine vision system will be developed and applied to monitor, calibrate, and control a novel surgical product assembly process of microscale. The machine vision system will benchmark the volumetric information of the needles, calibrate the position of the components, and control the assembly procedures. The successful implementation of the system will assist the production of finer surgical products.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT130101394

    Funder
    Australian Research Council
    Funding Amount
    $606,740.00
    Summary
    Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi .... Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.
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