Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales
Hendrik J. Weideman, Zachary M. Jablons, Jason Holmberg, Kiirsten, Flynn, John Calambokidis, Reny B. Tyson, Jason B. Allen, Randall S. Wells,, Krista Hupman, Kim Urian, Charles V. Stewart

TL;DR
This paper introduces an integral curvature-based representation for identifying individual dolphins and whales from fin images, demonstrating high accuracy in real-world datasets by matching unique edge patterns despite pose variations.
Contribution
The paper presents a novel integral curvature representation and two matching algorithms for cetacean identification, improving robustness to viewpoint changes and enabling effective ranking.
Findings
Achieved approximately 95% top-1 accuracy for bottlenose dolphins.
Achieved approximately 80% top-1 accuracy for humpback whales.
Demonstrated robustness of the method across real-world datasets.
Abstract
We address the problem of identifying individual cetaceans from images showing the trailing edge of their fins. Given the trailing edge from an unknown individual, we produce a ranking of known individuals from a database. The nicks and notches along the trailing edge define an individual's unique signature. We define a representation based on integral curvature that is robust to changes in viewpoint and pose, and captures the pattern of nicks and notches in a local neighborhood at multiple scales. We explore two ranking methods that use this representation. The first uses a dynamic programming time-warping algorithm to align two representations, and interprets the alignment cost as a measure of similarity. This algorithm also exploits learned spatial weights to downweight matches from regions of unstable curvature. The second interprets the representation as a feature descriptor.…
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