Automated gelatinous zooplankton acquisition and recognition (Contributo in atti di convegno)

Type
Label
  • Automated gelatinous zooplankton acquisition and recognition (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/CVAUI.2014.12 (literal)
Alternative label
  • Corgnati L.; Mazzei L.; Marini S.; Aliani S.; Conversi A.; Griffa A.; Isoppo B.; Ottaviani E. (2014)
    Automated gelatinous zooplankton acquisition and recognition
    in Computer Vision for Analysis of Underwater Imagery (CVAUI), 2014 ICPR Workshop on
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Corgnati L.; Mazzei L.; Marini S.; Aliani S.; Conversi A.; Griffa A.; Isoppo B.; Ottaviani E. (literal)
Pagina inizio
  • 1 (literal)
Pagina fine
  • 8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84916637220&partnerID=q2rCbXpz (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISMAR - Marine Sciences Institute in la Spezia, CNR - National Research Council of Italy, Forte Santa Teresa, Loc. Pozzuolo, Lerici (SP), 19032, Italy; Marine Institute, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, United Kingdom; SVM Srl, Via Turini 27, Lerici (SP), 19030, Italy; On AIR Srl, Via Carlo Barabino 26/4B, Genova, 16129, Italy (literal)
Titolo
  • Automated gelatinous zooplankton acquisition and recognition (literal)
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 diffierent methodologies, Tikhonov regulariza- tion, Support Vector Machines, and Genetic Programming, are analyzed 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 identication with high accu- racy 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 deployments. (literal)
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