@Article{XavierPerezSala2017,
author="Xavier Perez Sala
and Fernando De la Torre
and Laura Igual
and Sergio Escalera
and Cecilio Angulo",
title="Subspace Procrustes Analysis",
journal="International Journal of Computer Vision",
year="2017",
volume="121",
number="3",
pages="327--343",
abstract="Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given severalinstances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach.",
optnote="MILAB; HuPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2841), last updated on Wed, 23 May 2018 08:58:35 +0200",
opturl="https://link.springer.com/article/10.1007/s11263-016-0938-x"
}