Humpback whale’s flukes segmentation algorithms

Andrea Castro Cabanillas, Victor H. Ayma

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Photo-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.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas13
ISBN (versión impresa)978-3-030-76227-8
EstadoPublicada - 2021
Publicado de forma externa
Evento7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duración: 1 oct. 20203 oct. 2020

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1410 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CiudadVirtual, Online

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Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


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