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    Mosaics For Nephrops Detection in Underwater Survey Videos

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    Author
    Sooknanan, K
    Doyle, J
    Lordan, C
    Wilson, J
    Kokaram, A
    Corrigan, D
    Keyword
    UWTV
    Nephrops norvegicus
    Stock assessment
    Mosaics
    Burrow detection
    Date
    2014
    
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    Abstract
    Harvesting the commercially significant lobster, Nephrops norvegicus, is a multimillion dollar industry in Europe. Stock assessment is essential for maintaining this activity but it is conducted by manually inspecting hours of underwater surveillance videos. To improve this tedious process, we propose an automated procedure. This procedure uses mosaics for detecting the Nephrops, which improves visibility and reduces the tedious video inspection process to the browsing of a single image. In addition to this novel application approach, key contributions are made for handling the difficult lighting conditions in these kinds of videos. Mosaics are build using 1-10 minutes of footage and candidate Nephrops regions are selected using image segmentation based on local image contrast and colour features. A K-Nearest Neighbour classifier is then used to select the respective Nephrops from these candidate regions. Our final decision accuracy at 87.5% recall and precision shows a corresponding 31.5% and 79.4% improvement compared with previous work.
    URI
    http://hdl.handle.net/10793/1048
    Citation
    K. Sooknanan, A. Kokaram, J. Doyle, C. Lordan, J. Wilson and D. Corrigan. Mosaics For Nephrops Detection in Underwater Survey Video. In IEEE International Conference on Oceans (OCEANS'14), St. John’s, NL, Canada, September 2014, pages 10 - 16.
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