Online multibody factorization based on Bayesian principal component analysis of Gaussian mixture models

Authors:
Kentarou Hitomi;Takashi Bando;Naoki Fukaya;Kazushi Ikeda;Tomohiro Shibata
Affiliations:
Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan and DENSO Corporation, Kariya, Aichi, Japan;DENSO Corporation, Kariya, Aichi, Japan;DENSO Corporation, Kariya, Aichi, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

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.