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 J. Math. Data Sci., 1 (2019), pp. 208--236] to geometric morphometrics, a branch of evolutionary biology centered at the analysis and comparisons of anatomical shapes, and compare 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.
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Supplementary Material
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Index of Supplementary Materials
Title of paper: Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics
Authors: Tingran Gao, Shahar Kovalsky, Doug Boyer, and Ingrid Daubechies
File: supplement.pdf
Type: PDF
Contents: Additional figures, proof of a theorem in the main text, and comparative biological discussions.
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Information & Authors
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Published In
SIAM Journal on Mathematics of Data Science
Pages: 237 - 267
DOI: 10.1137/18M1203481
ISSN (online): 2577-0187
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© 2019, Society for Industrial and Applied Mathematics.
History
Submitted: 27 July 2018
Accepted: 27 December 2018
Published online: 12 February 2019
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Funding Information
National Science Foundation https://doi.org/10.13039/100000001 : BCS-1552848
Funding Information
Simons Foundation https://doi.org/10.13039/100000893 : 400837
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- Gaussian Process Landmarking on ManifoldsSIAM Journal on Mathematics of Data Science, Vol. 1, No. 1 | 12 February 2019