Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

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@Article{s16122124,
  author =       "Lorenzo Corgnati and Simone Marini and Luca Mazzei and 
                 Ennio Ottaviani and Stefano Aliani and 
                 Alessandra Conversi and Annalisa Griffa",
  title =        "Looking inside the Ocean: Toward an Autonomous Imaging
                 System for Monitoring Gelatinous Zooplankton",
  journal =      "Sensors",
  year =         "2016",
  volume =       "16",
  number =       "12",
  month =        "14 " # dec,
  note =         "Special Issue Sensing Technologies for Autonomy and
                 Cooperation in Underwater Networked Robot Systems",
  keywords =     "genetic algorithms, genetic programming, content-based
                 image recognition, feature selection, gelatinous
                 zooplankton, autonomous underwater imaging, GUARD1",
  article_number = "2124",
  ISSN =         "1424-8220",
  URL =          "http://www.mdpi.com/1424-8220/16/12/2124",
  URL =          "http://www.mdpi.com/1424-8220/16/12/2124/pdf",
  DOI =          "doi:10.3390/s16122124",
  size =         "28 pages",
  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.",
  notes =        "open access",
}

Genetic Programming entries for Lorenzo Paolo Corgnati Simone Marini Luca Mazzei Ennio Ottaviani Stefano Aliani Alessandra Conversi Annalisa Griffa

Citations