TY - JOUR
T1 - Humpback Whale’s Flukes Segmentation Algorithms
AU - Castro Cabanillas, Andrea
AU - Ayma Quirita, Victor Hugo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cetology
KW - Computer vision
KW - Image segmentation
KW - Photo-identification
UR - https://hdl.handle.net/20.500.12724/13950
UR - http://www.scopus.com/inward/record.url?scp=85111170665&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/0002ca13-6bc9-3f7e-96ad-901a0617b2c1/
U2 - https://doi.org/10.1007/978-3-030-76228-5_21
DO - https://doi.org/10.1007/978-3-030-76228-5_21
M3 - Article (Contribution to Journal)
SN - 1865-0937
SP - 291
EP - 303
JO - Communications in Computer and Information Science
JF - Communications in Computer and Information Science
ER -