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Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model

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Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model

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dc.contributor.author Atienza-Vanacloig, Vicente es_ES
dc.contributor.author Andreu García, Gabriela es_ES
dc.contributor.author López García, Fernando es_ES
dc.contributor.author Valiente González, José Miguel es_ES
dc.contributor.author Puig Pons, Vicente es_ES
dc.date.accessioned 2017-03-01T12:27:08Z
dc.date.available 2017-03-01T12:27:08Z
dc.date.issued 2016-11-15
dc.identifier.issn 0168-1699
dc.identifier.uri http://hdl.handle.net/10251/78418
dc.description.abstract This paper proposes a robust deformable adaptive 2D model, based on computer vision methods, that automatically fits the body (ventral silhouette) of Bluefin tuna while swimming. Our model (without human intervention) adjusts to fish shape and size, obtaining fish orientation, bending to fit their flexion motion and has proved robust enough to overcome possible segmentation inaccuracies. Once the model has been successfully fitted to the fish it can ensure that the detected object is a tuna and not parts of fish or other objects. Automatic requirements of the fishing industry like biometric measurement, specimen counting or catch biomass estimation could then be addressed using a stereoscopic system and meaningful information extracted from our model. We also introduce a fitting procedure based on a fitting parameter - Fitting Error Index (FEI) - which permits us to know the quality of the results. In the experiments our model has achieved very high success rates (up to 90%) discriminating individuals in highly complex images acquired for us in real conditions in the Mediterranean Sea. Conclusions and future improvements to the proposed model are also discussed. es_ES
dc.description.sponsorship This work was partially supported by the EU Commission [2013/410/EU] (BIACOP project). We acknowledge funding of ACUSTUNA project ref. CTM2015-70446-R (MINECO/FEDER, UE). en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computers and Electronics in Agriculture es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Shape modelling es_ES
dc.subject Fish detection es_ES
dc.subject Underwater video processing es_ES
dc.subject Computer vision es_ES
dc.subject Image segmentation es_ES
dc.subject Automatic biomass estimation es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compag.2016.10.009
dc.relation.projectID info:eu-repo/grantAgreement/EC//2013%2F410%2FEU/EU/Sistema de medida de biomasa en transferencias atún rojo por técnicas acústicas/BIACOP/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//CTM2015-70446-R/ES/ACUSTICA Y BIOMETRIA DEL ATUN ROJO (THUNNUS THYNNUS)  / es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial es_ES
dc.description.bibliographicCitation Atienza-Vanacloig, V.; Andreu García, G.; López García, F.; Valiente González, JM.; Puig Pons, V. (2016). Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model. Computers and Electronics in Agriculture. 130:142-150. https://doi.org/10.1016/j.compag.2016.10.009 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.compag.2016.10.009 es_ES
dc.description.upvformatpinicio 142 es_ES
dc.description.upvformatpfin 150 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 130 es_ES
dc.relation.senia 327916 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder European Commission es_ES


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