A STATISTICAL PIPELINE FOR IDENTIFYING PHYSICAL FEATURES THAT DIFFERENTIATE CLASSES OF 3D SHAPES

• 成果类型: Article
• 署名作者: Wang, Bruce; Sudijono, Timothy; Kirveslahti, Henry; Gao, Tingran; Boyer, Douglas M.; Mukherjee, Sayan; Crawford, Lorin
• 署名单位: Princeton University; Brown University; Duke University; University of Chicago; Duke University; Duke University; Duke University; Microsoft
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1430
• 发表日期: 2021
• 页码: 638-661
• 关键词: VARIABLE SELECTION; mixed-model; regularization

摘要

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global patterns in morphological variation. Studies, which focus on identifying differences between shapes, have been limited to simple pairwise comparisons and rely on prespecified landmarks (that are often known). We present SINATRA, the first statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two classes of shapes and highlights the physical features that best describe the variation between them. We use a rigorous simulation framework to assess our approach. Lastly, as a case study we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.