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A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna

The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identificat...

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Detalles Bibliográficos
Autores principales: Aguzzi, Jacopo, Costa, Corrado, Fujiwara, Yoshihiro, Iwase, Ryoichi, Ramirez-Llorda, Eva, Menesatti, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260594/
https://www.ncbi.nlm.nih.gov/pubmed/22291517
http://dx.doi.org/10.3390/s91108438
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author Aguzzi, Jacopo
Costa, Corrado
Fujiwara, Yoshihiro
Iwase, Ryoichi
Ramirez-Llorda, Eva
Menesatti, Paolo
author_facet Aguzzi, Jacopo
Costa, Corrado
Fujiwara, Yoshihiro
Iwase, Ryoichi
Ramirez-Llorda, Eva
Menesatti, Paolo
author_sort Aguzzi, Jacopo
collection PubMed
description The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identification from animals' outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan) was analysed. Out of 150,000 frames (1 per 4 s), a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts), red crabs (Paralomis multispina), and snails (Buccinum soyomaruae). Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea.
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spelling pubmed-32605942012-01-30 A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna Aguzzi, Jacopo Costa, Corrado Fujiwara, Yoshihiro Iwase, Ryoichi Ramirez-Llorda, Eva Menesatti, Paolo Sensors (Basel) Article The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identification from animals' outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan) was analysed. Out of 150,000 frames (1 per 4 s), a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts), red crabs (Paralomis multispina), and snails (Buccinum soyomaruae). Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea. Molecular Diversity Preservation International (MDPI) 2009-10-26 /pmc/articles/PMC3260594/ /pubmed/22291517 http://dx.doi.org/10.3390/s91108438 Text en © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Aguzzi, Jacopo
Costa, Corrado
Fujiwara, Yoshihiro
Iwase, Ryoichi
Ramirez-Llorda, Eva
Menesatti, Paolo
A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title_full A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title_fullStr A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title_full_unstemmed A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title_short A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna
title_sort novel morphometry-based protocol of automated video-image analysis for species recognition and activity rhythms monitoring in deep-sea fauna
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260594/
https://www.ncbi.nlm.nih.gov/pubmed/22291517
http://dx.doi.org/10.3390/s91108438
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