TY  - JOUR
AU  - Xavier Perez Sala
AU  - Fernando De la Torre
AU  - Laura Igual
AU  - Sergio Escalera
AU  - Cecilio Angulo
PY  - 2017//
TI  - Subspace Procrustes Analysis
T2  - IJCV
JO  - International Journal of Computer Vision
SP  - 327–343
VL  - 121
IS  - 3
N2  - 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.
UR  - https://link.springer.com/article/10.1007/s11263-016-0938-x
N1  - MILAB; HuPBA; no proj
ID  - Xavier Perez Sala2017
ER  -