Gaussian Process Landmarking For Three-Dimensional Geometric Morphometrics
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE(2019)
摘要
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.
更多查看译文
关键词
Gaussian process, experimental design, active learning, manifold learning, geometric morphometrics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络