Identifying humpback whale flukes by sequence matching of trailing edge curvature
Authors
Jablons, Zachary
ORCID
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Other Contributors
Stewart, Charles V.
Cutler, Barbara M.
Yener, Bülent, 1959-
Cutler, Barbara M.
Yener, Bülent, 1959-
Issue Date
2016-05
Keywords
Computer science
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
To our knowledge, this is the first method that can achieve this level of accuracy on humpback fluke identification without extensive manual effort at inference time.
Photographic identification of humpback whale (Megaptera novaeangliae) flukes (i.e. their tail) is an important task in marine ecology, and is used in tracking migration patterns and estimating populations. In this thesis, we lay out a method that automates the photo-identification of humpback flukes using the "trailing edge" of the fluke. The method uses convolutional networks to identify keypoints on the fluke and possible trailing edge locations. It then uses this information to extract a detailed trailing edge and its curvature, which is then matched to other trailing edges in the database via dynamic time warping. Using this method, we achieve nearly 80% top-1 ranking accuracy on a large subset of the SPLASH dataset consisting of about 400 identified individuals. We also show that in combination with HotSpotter, a general pattern based matching algorithm, we can achieve 93% accuracy.
Photographic identification of humpback whale (Megaptera novaeangliae) flukes (i.e. their tail) is an important task in marine ecology, and is used in tracking migration patterns and estimating populations. In this thesis, we lay out a method that automates the photo-identification of humpback flukes using the "trailing edge" of the fluke. The method uses convolutional networks to identify keypoints on the fluke and possible trailing edge locations. It then uses this information to extract a detailed trailing edge and its curvature, which is then matched to other trailing edges in the database via dynamic time warping. Using this method, we achieve nearly 80% top-1 ranking accuracy on a large subset of the SPLASH dataset consisting of about 400 identified individuals. We also show that in combination with HotSpotter, a general pattern based matching algorithm, we can achieve 93% accuracy.
Description
May 2016
School of Science
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.