TY  - JOUR
AU  - Laura Igual
AU  - Xavier Perez Sala
AU  - Sergio Escalera
AU  - Cecilio Angulo
AU  - Fernando De la Torre
PY  - 2014//
TI  - Continuous Generalized Procrustes Analysis
T2  - PR
JO  - Pattern Recognition
SP  - 659–671
VL  - 47
IS  - 2
KW  - Procrustes analysis
KW  - 2D shape model
KW  - Continuous approach
N2  - PR4883, PII: S0031-3203(13)00327-0Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, thestandard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA.
UR  - http://dx.doi.org/10.1016/j.patcog.2013.08.006
L1  - http://refbase.cvc.uab.es/files/IPE2013.pdf
N1  - OR; HuPBA; 605.203; 600.046;MILAB
ID  - Laura Igual2014
ER  -