Directions and projective shapes

The Annals of Statistics · 2005 · Volume 33 · Issue 4

Abstract

This paper deals with projective shape analysis, which is a study of finite configurations of points modulo projective transformations. The topic has various applications in machine vision. We introduce a convenient projective shape space, as well as an appropriate coordinate system for this shape space. For generic configurations of k points in m dimensions, the resulting projective shape space is identified as a product of k−m−2 copies of axial spaces ℝPm. This identification leads to the need for developing multivariate directional and multivariate axial analysis and we propose parametric models, as well as nonparametric methods, for these areas. In particular, we investigate the Frećhet extrinsic mean for the multivariate axial case. Asymptotic distributions of the appropriate parametric and nonparametric tests are derived. We illustrate our methodology with examples from machine vision.

Keywords

62H10, 62H11, 62H35, Bootstrapping, directional statistics, equivariant embedding, extrinsic means, machine vision, projective frame, projective shape space, Projective transformations, shape analysis, tangent approximation

Enable JavaScript for citation tools and additional paper interactions.