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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Hitomi, K., Bando, T., Fukaya, N., Ikeda, K., & Shibata, T. (2009). Online multibody factorization based on bayesian principal component analysis of gaussian mixture models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5506 LNCS, pp. 679–687). https://doi.org/10.1007/978-3-642-02490-0_83
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