Automated Approaches to Bowhead Whale Identification

This project aims to automate the identification of bowhead whales using convolutional neural networks. The initial neural network identifies key points to outline each whale and uses these points to divide each whale into three sub-sections: the fluke, the back, and the head. Upon segmenting the wh...

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Bibliographic Details
Main Authors: La, Kacey, Gregory, Sam
Format: Text
Language:unknown
Published: Digital Commons @ Ursinus College 2022
Subjects:
Bowhead whales
active contour
neural network
classification
image processing
convolution
canny edge detector
Computer Sciences
Marine Biology
Physical Sciences and Mathematics
bowhead whale
Online Access:https://digitalcommons.ursinus.edu/comp_sum/5
https://ursinusbowheadwhales.github.io/bowhead_web/
https://digitalcommons.ursinus.edu/context/comp_sum/article/1007/filename/0/type/additional/viewcontent/Gregory_La_2022_Summer_Fellows_Poster.pdf
Description
Summary:This project aims to automate the identification of bowhead whales using convolutional neural networks. The initial neural network identifies key points to outline each whale and uses these points to divide each whale into three sub-sections: the fluke, the back, and the head. Upon segmenting the whale, each sub-section was used to identify individual bowhead whales through the white patterns and scarring on their backs. The results from each segment were then combined into a final classifier to identify bowhead whales.

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