  We demonstrate applications of the Gaussian process-based landmarking
algorithm proposed in [T. Gao, S.Z. Kovalsky, and I. Daubechies, SIAM Journal
on Mathematics of Data Science (2019)] to geometric morphometrics, a branch of
evolutionary biology centered at the analysis and comparisons of anatomical
shapes, and compares the automatically sampled landmarks with the "ground
truth" landmarks manually placed by evolutionary anthropologists; the results
suggest that Gaussian process landmarks perform equally well or better, in
terms of both spatial coverage and downstream statistical analysis. We provide
a detailed exposition of numerical procedures and feature filtering algorithms
for computing high-quality and semantically meaningful diffeomorphisms between
disk-type anatomical surfaces.
