Título:
|
Subspace procrustes analysis
|
Autor/a:
|
Perez Sala, Xavier; De La Torre, Fernando; Igual, Laura; Escalera, Sergio; Angulo Bahón, Cecilio
|
Otros autores:
|
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement |
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 different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more effcient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
Materia(s):
|
-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Computer vision -Pattern recognition systems -Reconeixement de formes (Informàtica) -Visió per ordinador |
Derechos:
|
|
Tipo de documento:
|
Artículo - Borrador Objeto de conferencia |
Compartir:
|
|