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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Online Multibody Factorization Based on Bayesian Principal Component Analysis of Gaussian Mixture Models
verfasst von : Kentarou Hitomi, Takashi Bando, Naoki Fukaya, Kazushi Ikeda, Tomohiro Shibata
Erschienen in: Advances in Neuro-Information Processing
Verlag: Springer Berlin Heidelberg
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An online multibody factorization method for recovering the shape of each object from a sequence of monocular images is proposed. We formulate multibody factorization problem of data matrix of feature positions as the parameter estimation of the mixtures of probabilistic principal component analysis (MPPCA) and use the variational inference method as an estimation algorithm that concurrently performs classification of each feature points and the three-dimensional structures of each object. We also apply the online variational inference method make the algorithm suitable for real-time applications.