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dc.contributor.authorCastro Cabanillas, Andrea
dc.contributor.authorAyma Quirita, Víctor Hugo
dc.contributor.otherCastro Cabanillas, Andrea
dc.contributor.otherAyma Quirita, Víctor Hugo
dc.date.accessioned2021-08-24T19:20:22Z
dc.date.available2021-08-24T19:20:22Z
dc.date.issued2021
dc.identifier.citationCastro Cabanillas, A. & Ayma, V. H. (2021). Humpback Whale’s Flukes Segmentation Algorithms. En: Lossio-Ventura J.A., Valverde-Rebaza J.C., Díaz E., Alatrista-Salas H. (eds.) Information Management and Big Data: Seventh Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings, Communications in Computer and Information Science (vol. 1410, pp. 291-303). Springer. https://doi.org/10.1007/978-3-030-76228-5_21es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12724/13950
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractPhoto-identification consists of the analysis of photographs to identify cetacean individuals based on unique characteristics that each specimen of the same species exhibits. The use of this tool allows us to carry out studies about the size of its population and migratory routes by comparing catalogues. However, the number of images that make up these catalogues is large, so the manual execution of photo-identification takes considerable time. On the other hand, many of the methods proposed for the automation of this task coincide in proposing a segmentation phase to ensure that the identification algorithm takes into account only the characteristics of the cetacean and not the background. Thus, in this work, we compared four segmentation techniques from the image processing and computer vision fields to isolate whales’ flukes. We evaluated the Otsu (OTSU), Chan Vese (CV), Fully Convolutional Networks (FCN), and Pyramid Scene Parsing Network (PSP) algorithms in a subset of images from the Humpback Whale Identification Challenge dataset. The experimental results show that the FCN and PSP algorithms performed similarly and were superior to the OTSU and CV segmentation techniques.en_EN
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherSpringeres_PE
dc.relation.ispartofurn:issn:1865-0929
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectPendientees_PE
dc.subject.classificationPendiente / Pendientees_PE
dc.titleHumpback Whale’s Flukes Segmentation Algorithmsen_EN
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.type.otherArtículo de conferencia en Scopuses_PE
dc.identifier.journalCommunications in Computer and Information Science
dc.publisher.countryDEes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.identifier.doihttps://doi.org/10.1007/978-3-030-76228-5_21


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