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Journal Article

Automated, high-throughput image calibration for parallel-laser photogrammetry


Robbins,  Martha M.       
Gorillas, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Richardson, J. L., Levy, E. J., Ranjithkumar, R., Yang, H., Monson, E., Cronin, A., et al. (2022). Automated, high-throughput image calibration for parallel-laser photogrammetry. Mammalian Biology, 102, 615-627. doi:10.1007/s42991-021-00174-7.

Cite as: https://hdl.handle.net/21.11116/0000-000A-2ABA-6
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 meas-
uring 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.