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)


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
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


Conference3rd Iberian Conference on Pattern Recognition and Image Analysis

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Computer Science
  • Theoretical Computer Science


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