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Unsupervised Learning of Multiple Aspects of Moving Objects from Video

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3746)

Abstract

A popular framework for the interpretation of image sequences is based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2]. Jojic and Frey [3] provide a generative probabilistic model framework for this task. However, this layered models do not explicitly account for variation due to changes in the pose and self occlusion. In this paper we show that if the motion of the object is large so that different aspects (or views) of the object are visible at different times in the sequence, we can learn appearance models of the different aspects using a mixture modelling approach.

Keywords

  • Tracking Algorithm
  • Unsupervised Learn
  • Current Frame
  • Appearance Model
  • Foreground Object

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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© 2005 Springer-Verlag Berlin Heidelberg

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Titsias, M.K., Williams, C.K.I. (2005). Unsupervised Learning of Multiple Aspects of Moving Objects from Video. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_71

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  • DOI: https://doi.org/10.1007/11573036_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29673-7

  • Online ISBN: 978-3-540-32091-3

  • eBook Packages: Computer ScienceComputer Science (R0)