Dealing with non-linearity in shape modelling of articulated objects

Gregorg Rogez, Jesus Martinez-del-Rincon, Carlos Orrite

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We address the problem of non-linearity in 2D Shape modelling of a particular articulated object: the human body. This issue is partially resolved by applying a different Point Distribution Model (PDM) depending on the viewpoint. The remaining non-linearity is solved by using Gaussian Mixture Models (GMM). A dynamic-based clustering is proposed and carried out in the Pose Eigenspace. A fundamental question when clustering is to determine the optimal number of clusters. From our point of view, the main aspect to be evaluated is the mean gaussianity. This partitioning is then used to fit a GMM to each one of the view-based PDM, derived from a database of Silhouettes and Skeletons. Dynamic correspondences are then obtained between gaussian models of the 4 mixtures. Finally, we compare this approach with other two methods we previously developed to cope with non-linearity: Nearest Neighbor (NN) Classifier and Independent Component Analysis (ICA).

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis, Pt 1, Proceedings
EditorsJ Marti, JM Benedi, AM Mendonca, J Serrat
Place of PublicationBERLIN
PublisherSpringer
Pages63-71
Number of pages9
Volume4477 LNCS
EditionPART 1
ISBN (Print)978-3-540-72846-7
Publication statusPublished - 2007
Event3rd Iberian Conference on Pattern Recognition and Image Analysis - Girona, Spain
Duration: 06 Jun 200708 Jun 2007

Conference

Conference3rd Iberian Conference on Pattern Recognition and Image Analysis
CountrySpain
CityGirona
Period06/06/200708/06/2007

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Rogez, G., Martinez-del-Rincon, J., & Orrite, C. (2007). Dealing with non-linearity in shape modelling of articulated objects. In J. Marti, JM. Benedi, AM. Mendonca, & J. Serrat (Eds.), Pattern Recognition and Image Analysis, Pt 1, Proceedings (PART 1 ed., Vol. 4477 LNCS, pp. 63-71). Springer.