用于三维几何形态测量的高斯过程标界
应用统计
2019-01-10 v3
摘要
我们展示了[T. Gao, S.Z. Kovalsky, and I. Daubechies, SIAM Journal on Mathematics of Data Science (2019)]中提出的基于高斯过程的标界算法在几何形态测量学（进化生物学的一个分支，以解剖形状的分析与比较为中心）中的应用，并将自动采样的界标与进化人类学家手动放置的“地面真值”界标进行比较；结果表明，高斯过程界标在空间覆盖和下游统计分析两方面表现同等或更好。我们详细阐述了用于计算盘型解剖表面之间高质量且语义上有意义的微分同胚的数值程序和特征滤波算法。
引用
@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}
}
备注
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