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    Fish identification from videos captured in uncontrolled underwater environments

    Access Status
    Fulltext not available
    Authors
    Shafait, F.
    Mian, A.
    Ghanem, B.
    Culverhouse, P.
    Edgington, D.
    Cline, D.
    Ravenbakhsh, M.
    Seager, J.
    Harvey, Euan
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Shafait, F. and Mian, A. and Ghanem, B. and Culverhouse, P. and Edgington, D. and Cline, D. and Ravenbakhsh, M. et al. 2013. Fish identification from videos captured in uncontrolled underwater environments. ICES Journal of Marine Science. 73 (10): pp. 2737-2746.
    Source Title
    ICES Journal of Marine Science
    DOI
    10.1093/icesjms/fsw106
    ISSN
    1054-3139
    School
    Department of Environment and Agriculture
    URI
    http://hdl.handle.net/20.500.11937/51183
    Collection
    • Curtin Research Publications
    Abstract

    There is an urgent need for the development of sampling techniques which can provide accurate and precise count, size, and biomass data for fish. This information is essential to support the decision-making processes of fisheries and marine conservation managers and scientists. Digital video technology is rapidly improving, and it is now possible to record long periods of high resolution digital imagery cost effectively, making single or stereo-video systems one of the primary sampling tools. However, manual species identification, counting, and measuring of fish in stereo-video images is labour intensive and is the major disincentive against the uptake of this technology. Automating species identification using technologies developed by researchers in computer vision and machine learning would transform marine science. In this article, a new paradigm of image set classification is presented that can be used to achieve improved recognition rates for a number of fish species. State-of-the-art image set construction, modelling, and matching algorithms from computer vision literature are discussed with an analysis of their application for automatic fish species identification. It is demonstrated that these algorithms have the potential of solving the automatic fish species identification problem in underwater videos captured within unconstrained environments.

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