[MARMAM] New Publication: Rise of the Machines: Best Practices and Experimental Evaluation of Computer-Assisted Dorsal Fin Image Matching Systems for Bottlenose Dolphins

Reny Tyson Moore rtysonmoore at mote.org
Mon Apr 18 05:18:33 PDT 2022


Dear Colleagues,

On behalf of myself and my co-authors, we would like to alert you to our
new publication examining computer-assisted dorsal fin image matching
systems that has recently been published in Frontiers in Marine Science as
part of The Dolphins of Sarasota Bay: Lessons from 50 years of Research and
Conservation Research Topic.

*Citation:*
Tyson Moore, R.B., Urian, K., Allen, J.B., Cush, C., Parham, J., Blount,
D., Holmberg, J., Thompson, J. and Wells, R. 2022. Rise of the Machines:
Best Practices and Experimental Evaluation of Computer-Assisted Dorsal Fin
Image Matching Systems for Bottlenose Dolphins. *Frontiers in Marine
Science*, p.445.

*Abstract:*
Photographic-identification (photo-ID) of bottlenose dolphins using
individually distinctive features on the dorsal fin is a well-established
and useful tool for tracking individuals; however, this method can be
labor-intensive, especially when dealing with large catalogs and/or
infrequently surveyed populations. Computer vision algorithms have been
developed that can find a fin in an image, characterize the features of the
fin, and compare the fin to a catalog of known individuals to generate a
ranking of potential matches based on dorsal fin similarity. We examined if
and how researchers use computer vision systems in their photo-ID process
and developed an experiment to evaluate the performance of the most
commonly used, recently developed, systems to date using a long-term
photo-ID database of known individuals curated by the Chicago Zoological
Society’s Sarasota Dolphin Research Program. Survey results obtained for
the “Rise of the machines – Application of automated systems for matching
dolphin dorsal fins: current status and future directions” workshop held at
the 2019 World Marine Mammal Conference indicated that most researchers
still rely on manual methods for comparing unknown dorsal fin images to
reference catalogs of known individuals. Experimental evaluation of the
finFindR R application, as well as the CurvRank, CurvRank v2, and finFindR
implementations in Flukebook suggest that high match rates can be achieved
with these systems, with the highest match rates found when only good to
excellent quality images of fins with average to high distinctiveness are
included in the matching process: for the finFindR R application and the
CurvRank and CurvRank v2 algorithms within Flukebook more than 98.92% of
correct matches were in the top 50-ranked positions, and more than 91.94%
of correct matches were returned in the first ranked position. Our results
offer the first comprehensive examination into the performance and accuracy
of computer vision algorithms designed to assist with the photo-ID process
of bottlenose dolphins and can be used to build trust by researchers
hesitant to use these systems. Based on our findings and discussions from
the “Rise of the Machines” workshop we provide recommendations for best
practices for using computer vision systems for dorsal fin photo-ID.

The article and supplementary materials can be accessed via
https://doi.org/10.3389/fmars.2022.849813, or you can email me at
renytyson at gmail.com <renytyson at gmai.com> for copies.

Best,
Reny Tyson Moore

-- 
Reny Tyson Moore, PhD
Staff Scientist
Chicago Zoological Society's Sarasota Dolphin Research Program
(352) 408-6018 cell
rtysonmoore at mote.org <rtyson at mote.org>
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