Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics
Abstract
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.
Cite
@article{arxiv.1807.11887,
title = {Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics},
author = {Tingran Gao and Shahar Z. Kovalsky and Doug M. Boyer and Ingrid Daubechies},
journal= {arXiv preprint arXiv:1807.11887},
year = {2019}
}
Comments
41 pages, 17 figures, 3 tables. Some portions of this work appeared earlier as arXiv:1802.03479, which was split into 2 parts during the refereeing process. This version combines the main text with the supplemental materials. Figure sizes have been reduced to meet arxiv size limit