Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Amit Gruber, Yair Weiss


The problem of \Structure From Motion" is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of the points. Un- der simplifled camera models, the problem reduces to factorizing a measurement matrix into the product of two low rank matrices. Each element of the measurement matrix contains the position of a point in a particular image. When all elements are observed, the problem can be solved trivially using SVD, but in any realistic sit- uation many elements of the matrix are missing and the ones that are observed have a difierent directional uncertainty. Under these conditions, most existing factorization algorithms fail while human perception is relatively unchanged. In this paper we use the well known EM algorithm for factor analy- sis to perform factorization. This allows us to easily handle missing data and measurement uncertainty and more importantly allows us to place a prior on the temporal trajectory of the latent variables (the camera position). We show that incorporating this prior gives a signiflcant improvement in performance in challenging image se- quences.