A Test to Determine the Multivariate Normality of a Data Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Novel Approach to Generate Multiple Shape Models for Tracking Applications
AMDO '02 Proceedings of the Second International Workshop on Articulated Motion and Deformable Objects
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In this paper we explain a new practical methodology to fully parameterise Gaussian Mixture Models (GMM) to describe data set distributions. Our approach analyses hierarchically a data set distribution to be modeled, determining unsupervisedly an appropriate number of components of the GMM, and their corresponding parameterisation. Results are provided that show the improvement of our method with regard to an implementation of the traditional approach usually applied to solve this problem. The method is also tested in the unsupervised generation of shape models for visual tracking applications.