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Marine object detection using background modelling and blob analysis
conference contribution
posted on 2015-01-01, 00:00 authored by Hailing Zhou, L Llewellyn, Lei WeiLei Wei, Douglas CreightonDouglas Creighton, Saeid NahavandiMonitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive way via setting up a camera and seeking objects in images is more promising. In this paper, a novel technique of object detection in images is presented, which is applicable to generic objects. A robust background modelling algorithm is proposed to extract foregrounds and then blob features are introduced to classify foregrounds. Particular marine objects, box jellyfish and sea snake, are successfully detected in our work. Experiments conducted on image datasets collected by the Australian Institute of Marine Science (AIMS) demonstrate the effectiveness of the proposed technique.
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Event
IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)Pagination
430 - 435Publisher
IEEELocation
Hong Kong, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-10-09End date
2015-10-12ISSN
1062-922XISBN-13
9781479986965Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and CyberneticsUsage metrics
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