Automated Gelatinous Zooplankton Acquisition and Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Lorenzo Corgnati and Luca Mazzei and Simone Marini and 
                 Stefano Aliani and Alessandra Conversi and 
                 Annalisa Griffa and Bruno Isoppo and Ennio Ottaviani",
  booktitle =    "ICPR Workshop on Computer Vision for Analysis of
                 Underwater Imagery (CVAUI 2014)",
  title =        "Automated Gelatinous Zooplankton Acquisition and
  year =         "2014",
  month =        aug,
  address =      "Stockholm",
  abstract =     "Much is still unknown about marine plankton abundance
                 and dynamics in the open and interior ocean. Especially
                 challenging is the knowledge of gelatinous zooplankton
                 distribution, since it has a very fragile structure and
                 cannot be directly sampled using traditional net based
                 techniques. In the last decades there has been an
                 increasing interest in the oceanographic community
                 toward imaging systems. In this paper the performance
                 of three different methodologies, Tikhonov
                 regularisation, Support Vector Machines, and Genetic
                 Programming, are analysed for the recognition of
                 gelatinous zooplankton. The three methods have been
                 tested on images acquired in the Ligurian Sea by a low
                 cost under-water standalone system (GUARD1). The
                 results indicate that the three methods provide
                 gelatinous zooplankton identification with high
                 accuracy showing a good capability in robustly
                 selecting relevant features, thus avoiding
                 computational-consuming preprocessing stages. These
                 aspects fit the requirements for running on an
                 autonomous imaging system designed for long lasting
  keywords =     "genetic algorithms, genetic programming, SVM",
  DOI =          "doi:10.1109/CVAUI.2014.12",
  notes =        "Also known as \cite{6961262}",

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