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Research Topic : Communication
Socio-Economic Objective : Information processing services
Australian State/Territory : ACT
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  • Funded Activity

    Discovery Projects - Grant ID: DP0879736

    Funder
    Australian Research Council
    Funding Amount
    $196,000.00
    Summary
    Location-Based Personalisation in Mobile Commerce (M-Commerce). M-commerce, though playing an important role in future competitiveness of Australia, suffers a low user demand. While location-based services have taken off in Europe, they are still at their infancy in Australia. In terms of IT access, Australia is ranked lower than many Asian countries. Our work gains an understanding of users' concerns and expectations of location-based services, which leads to better application designs and thus .... Location-Based Personalisation in Mobile Commerce (M-Commerce). M-commerce, though playing an important role in future competitiveness of Australia, suffers a low user demand. While location-based services have taken off in Europe, they are still at their infancy in Australia. In terms of IT access, Australia is ranked lower than many Asian countries. Our work gains an understanding of users' concerns and expectations of location-based services, which leads to better application designs and thus a wider adoption. An examination of users' attitude towards personalised content and concerns about data privacy provides insights to Australian legislation in relation to telemarketing and data-driven marketing. National benefits will stem from a balance between telemarketing efficiency and users' benefits.
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    Funded Activity

    Discovery Projects - Grant ID: DP0343610

    Funder
    Australian Research Council
    Funding Amount
    $277,440.00
    Summary
    Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior .... Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior knowledge of physical environments, etc.) and combine the advantages of feature based high-level vision methods with low-level machine learning techniques. This proposal is part of a joint IST project with partners from the European Union.
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    Funded Activity

    Discovery Projects - Grant ID: DP0773761

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    Computer Vision Optimization Problems Using Machine Learning. Computer Vision concerns itself with understanding the world through the analysis of images obtained by a video or still camera. An important application is tracking of people in video and modelling their movements. This has evident applications in security, sport and entertainment. By enabling the computer to capture the motion of a subject in a video, we may detect suspicious activity in security, analyze the motion (golf-swing, .... Computer Vision Optimization Problems Using Machine Learning. Computer Vision concerns itself with understanding the world through the analysis of images obtained by a video or still camera. An important application is tracking of people in video and modelling their movements. This has evident applications in security, sport and entertainment. By enabling the computer to capture the motion of a subject in a video, we may detect suspicious activity in security, analyze the motion (golf-swing, diving style) of a sports-person, or capture the motion of an actor for animation or game applications. Development of a reliable technology requires new optimization techniques, which will place Australia at the forefront of the application of such research, commercially and for the public benefit.
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    Funded Activity

    Linkage Projects - Grant ID: LP0991757

    Funder
    Australian Research Council
    Funding Amount
    $240,000.00
    Summary
    Generic Content-based News Picture Retrieval with Local Invariant Features. Image Retrieval searches for images from large databases whose visual content meets the requirements submitted by users. Besides directly benefiting the Partner Organization, this project will enable more efficient access to large picture repositories in news agencies and publishers, digital libraries and film archives. It will make public use of visual information much more convenient and economical. It will help securi .... Generic Content-based News Picture Retrieval with Local Invariant Features. Image Retrieval searches for images from large databases whose visual content meets the requirements submitted by users. Besides directly benefiting the Partner Organization, this project will enable more efficient access to large picture repositories in news agencies and publishers, digital libraries and film archives. It will make public use of visual information much more convenient and economical. It will help security officers to effortlessly and accurately find particular scenes from the images generated by a large closed-circuit TV networks. Also, the developed technology can be applied to tele-education and e-commerce. New algorithms developed in this project will benefit the Australian and world scientific communities.
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    Funded Activity

    Discovery Projects - Grant ID: DP0880874

    Funder
    Australian Research Council
    Funding Amount
    $315,000.00
    Summary
    Adaptive data stream processing in heterogeneous distributed computing environments using real-time context. This project falls within the ARC research priority goal, Smart Information Use. The innovative contributions of this project through the development of adaptive data stream mining algorithms for heterogeneous devices will have an impact on a range of emerging application areas such as: 1. Meeting time-critical, intelligent information needs of the mobile workforce (e.g. mobile healthca .... Adaptive data stream processing in heterogeneous distributed computing environments using real-time context. This project falls within the ARC research priority goal, Smart Information Use. The innovative contributions of this project through the development of adaptive data stream mining algorithms for heterogeneous devices will have an impact on a range of emerging application areas such as: 1. Meeting time-critical, intelligent information needs of the mobile workforce (e.g. mobile healthcare professionals, stockbrokers). 2. Improving Intelligent Transportation Systems via in-vehicle analysis and crash prevention. 3. Facilitating 'on-board' analysis in sensors that monitor the environment and patients. The project will enhance Australia's leading international role in the area of data stream processing in distributed computing environments.
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