A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species

A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species

Abstract Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this...

Full description

Bibliographic Details
Main Authors: Philip T. Patton, Ted Cheeseman, Kenshin Abe, Taiki Yamaguchi, Walter Reade, Ken Southerland, Addison Howard, Erin M. Oleson, Jason B. Allen, Erin Ashe, Aline Athayde, Robin W. Baird, Charla Basran, Elsa Cabrera, John Calambokidis, Júlio Cardoso, Emma L. Carroll, Amina Cesario, Barbara J. Cheney, Enrico Corsi, Jens Currie, John W. Durban, Erin A. Falcone, Holly Fearnbach, Kiirsten Flynn, Trish Franklin, Wally Franklin, Bárbara Galletti Vernazzani, Tilen Genov, Marie Hill, David R. Johnston, Erin L. Keene, Sabre D. Mahaffy, Tamara L. McGuire, Liah McPherson, Catherine Meyer, Robert Michaud, Anastasia Miliou, Dara N. Orbach, Heidi C. Pearson, Marianne H. Rasmussen, William J. Rayment, Caroline Rinaldi, Renato Rinaldi, Salvatore Siciliano, Stephanie Stack, Beatriz Tintore, Leigh G. Torres, Jared R. Towers, Cameron Trotter, Reny Tyson Moore, Caroline R. Weir, Rebecca Wellard, Randall Wells, Kymberly M. Yano, Jochen R. Zaeschmar, Lars Bejder
Format: Article
Language:English
Published: Wiley 2023-10-01
Series:Methods in Ecology and Evolution
Subjects:
artificial intelligence
cetacean
computer vision
convolutional neural network
deep learning
dolphin
Online Access:https://doi.org/10.1111/2041-210X.14167

Internet

https://doi.org/10.1111/2041-210X.14167

Similar Items