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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Automated, high-throughput image calibration for parallel-laser photogrammetry
Mammalian Biology, Volume 102, No. 3, Year 2022
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Description
Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject’s body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is also the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. © 2022, The Author(s) under exclusive licence to Deutsche Gesellschaft für Säugetierkunde.
Authors & Co-Authors
Richardson, Jack L.
United States, Washington, D.c.
The George Washington University
Levy, Emily J.
United States, Durham
Duke University
Ranjithkumar, Riddhi
United States, Durham
Duke University
Galbany, Jordi
United States, Washington, D.c.
The George Washington University
Spain, Barcelona
Universitat de Barcelona
Robbins, Martha M.
Germany, Leipzig
Max-planck-institut Für Evolutionäre Anthropologie
Alberts, Susan C.
United States, Durham
Duke University
McFarlin, Shannon C.
United States, Washington, D.c.
The George Washington University
Statistics
Citations: 5
Authors: 7
Affiliations: 6
Identifiers
Doi:
10.1007/s42991-021-00174-7
ISSN:
16165047