@InProceedings{XavierPerezSala2014,
author="Xavier Perez Sala
and Fernando De la Torre
and Laura Igual
and Sergio Escalera
and Cecilio Angulo",
title="Subspace Procrustes Analysis",
booktitle="ECCV Workshop on ChaLearn Looking at People",
year="2014",
volume="8925",
pages="654--668",
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 several instances 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 dierent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benets of our approach.",
optnote="OR; HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2539), last updated on Tue, 18 Oct 2016 11:50:31 +0200",
doi="10.1007/978-3-319-16178-5_46",
file=":http://refbase.cvc.uab.es/files/PTI2014.pdf:PDF"
}