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    Deep Learning for Coral Classification

    Access Status
    Fulltext not available
    Authors
    Mahmood, A.
    Bennamoun, M.
    An, Senjian
    Sohel, F.
    Boussaid, F.
    Hovey, R.
    Kendrick, G.
    Fisher, R.
    Date
    2017
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Mahmood, A. and Bennamoun, M. and An, S. and Sohel, F. and Boussaid, F. and Hovey, R. and Kendrick, G. et al. 2017. Deep Learning for Coral Classification. In Handbook of Neural Computation, 383-401. USA: Academic Press.
    Source Title
    Handbook of Neural Computation
    DOI
    10.1016/B978-0-12-811318-9.00021-1
    ISBN
    9780128113196
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/69952
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 Elsevier Inc. All rights reserved. This chapter presents a summary of the use of deep learning for underwater image analysis, in particular for coral species classification. Deep learning techniques have achieved the state-of-the-art results in various computer vision tasks such as image classification, object detection, and scene understanding. Marine ecosystems are complex scenes and hence difficult to tackle from a computer vision perspective. Automated technology to monitor the health of our oceans can facilitate in detecting and identifying marine species while freeing up experts from the repetitive task of manual annotation. Classification of coral species is a challenging task in itself and deep learning has a potential of solving this problem efficiently.

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