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dc.contributor.authorMahmood, A.
dc.contributor.authorBennamoun, M.
dc.contributor.authorAn, Senjian
dc.contributor.authorSohel, F.
dc.contributor.authorBoussaid, F.
dc.contributor.authorHovey, R.
dc.contributor.authorKendrick, G.
dc.contributor.authorFisher, R.
dc.identifier.citationMahmood, 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.

© 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.

dc.publisherAcademic Press
dc.titleDeep Learning for Coral Classification
dc.typeBook Chapter
dcterms.source.titleHandbook of Neural Computation
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available

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