NDLI: Shark detection using optical image data from a mobile aerial platform

Content Provider	IEEE Xplore Digital Library
Author	Gururatsakul, S. Gibbins, D. Kearney, D. Lee, I.
Copyright Year	2010
Description	Author affiliation: Dept of Electrical and Electronic Engineering, The University of Adelaide, Australia (Gibbins, D.) \|\| School of Computer and Information Science, University of South Australia, Australia (Gururatsakul, S.; Kearney, D.; Lee, I.)
Abstract	Sharks are one of the major predators in the ocean. In particular, the great white shark is a primary threat to swimmers. This work proposes an automatic method for the recognition of deformable submerged objects (i.e. sharks) from aerial images of the coast line in an uncontrolled environment. It focuses on great white shark recognition in the surf zone of coastal areas. As the images were taken in an uncontrolled environment and the object shapes of interest are deformable, it is not easy to distinguish sharks from shark-like objects such as dolphins. In this paper, we propose two feature extraction methods that are based on the object's shape: the fish shape feature and shape profile methods. All feature extraction methods are applied to a new image database that contains aerial views of sharks and shark-like objects. The classifiers that are used in our proposed methods are the Support Vector Machine (SVM) and the feed-forward backpropagation neural network.
Starting Page	1
Ending Page	8
File Size	281606
Page Count	8
File Format	PDF
ISBN	9781424496297
ISSN	21512205
e-ISBN	9781424496310
DOI	10.1109/IVCNZ.2010.6148828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-08
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Shape Shape measurement feature extraction deformable object recognition Feature extraction Clutter image analysis Dolphins
Content Type	Text
Resource Type	Article

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