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Subspace procrustes analysis
dc.contributor.author | Perez Sala, Xavier |
dc.contributor.author | De La Torre, Fernando |
dc.contributor.author | Igual, Laura |
dc.contributor.author | Escalera, Sergio |
dc.contributor.author | Angulo Bahón, Cecilio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2014-11-11T11:05:55Z |
dc.date.available | 2014-11-11T11:05:55Z |
dc.date.created | 2014 |
dc.date.issued | 2014 |
dc.identifier.citation | Perez, X. [et al.]. Subspace procrustes analysis. A: European Conference on Computer Vision. "ECCV Workshop on ChaLearn Looking at People". Zurich: 2014. |
dc.identifier.uri | http://hdl.handle.net/2117/24672 |
dc.description.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. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Computer vision |
dc.subject.lcsh | Pattern recognition systems |
dc.title | Subspace procrustes analysis |
dc.type | Conference report |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.subject.lemac | Visió per ordinador |
dc.contributor.group | Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement |
dc.relation.publisherversion | http://www.ca.cs.cmu.edu/papers/subspace_pa.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 15263239 |
dc.description.version | Preprint |
local.citation.author | Perez, X.; De La Torre, F.; Igual, L.; Escalera, S.; Angulo, C. |
local.citation.contributor | European Conference on Computer Vision |
local.citation.pubplace | Zurich |
local.citation.publicationName | ECCV Workshop on ChaLearn Looking at People |