Abstract
We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, variational methods), we solve the inverse problem and locally segment the foreground from the background, estimate the nonuniform motion of each, and fill in the disocclusions. To illustrate the usefulness of both the representation and the estimation algorithm, we show results on stabilization and frame interpolation that are obtained by generating from the trained models.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kannan, A., Frey, B., Jojic, N. (2006). A Generative Model of Dense Optical Flow in Layers. In: MacLean, W.J. (eds) Spatial Coherence for Visual Motion Analysis. SCVMA 2004. Lecture Notes in Computer Science, vol 3667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676959_9
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DOI: https://doi.org/10.1007/11676959_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32533-8
Online ISBN: 978-3-540-32534-5
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