Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton
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- @Article{s16122124,
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author = "Lorenzo Corgnati and Simone Marini and Luca Mazzei and
Ennio Ottaviani and Stefano Aliani and
Alessandra Conversi and Annalisa Griffa",
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title = "Looking inside the Ocean: Toward an Autonomous Imaging
System for Monitoring Gelatinous Zooplankton",
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journal = "Sensors",
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year = "2016",
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volume = "16",
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number = "12",
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month = "14 " # dec,
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note = "Special Issue Sensing Technologies for Autonomy and
Cooperation in Underwater Networked Robot Systems",
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keywords = "genetic algorithms, genetic programming, content-based
image recognition, feature selection, gelatinous
zooplankton, autonomous underwater imaging, GUARD1",
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article_number = "2124",
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ISSN = "1424-8220",
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URL = "http://www.mdpi.com/1424-8220/16/12/2124",
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URL = "http://www.mdpi.com/1424-8220/16/12/2124/pdf",
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DOI = "doi:10.3390/s16122124",
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size = "28 pages",
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abstract = "Marine plankton abundance and dynamics in the open and
interior ocean is still an unknown field. The knowledge
of gelatinous zooplankton distribution is especially
challenging, because this type of plankton has a very
fragile structure and cannot be directly sampled using
traditional net based techniques. To overcome this
shortcoming, Computer Vision techniques can be
successfully used for the automatic monitoring of this
group.This paper presents the GUARD1 imaging system, a
low-cost stand-alone instrument for underwater image
acquisition and recognition of gelatinous zooplankton,
and discusses the performance of three different
methodologies, Tikhonov Regularization, Support Vector
Machines and Genetic Programming, that have been
compared in order to select the one to be run onboard
the system for the automatic recognition of gelatinous
zooplankton. The performance comparison results
highlight the high accuracy of the three methods in
gelatinous zooplankton identification, showing their
good capability in robustly selecting relevant
features. In particular, Genetic Programming technique
achieves the same performances of the other two methods
by using a smaller set of features, thus being the most
efficient in avoiding computationally consuming
preprocessing stages, that is a crucial requirement for
running on an autonomous imaging system designed for
long lasting deployments, like the GUARD1. The Genetic
Programming algorithm has been installed onboard the
system, that has been operationally tested in a
two-months survey in the Ligurian Sea, providing
satisfactory results in terms of monitoring and
recognition performances.",
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notes = "open access",
- }
Genetic Programming entries for
Lorenzo Paolo Corgnati
Simone Marini
Luca Mazzei
Ennio Ottaviani
Stefano Aliani
Alessandra Conversi
Annalisa Griffa
Citations