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    Kernel-based high-dimensional histogram estimation for visual tracking

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    208_2008_ICIP.pdf (426.5Kb)
    Date
    2008-10
    Author
    Karasev, Peter
    Malcolm, James G.
    Tannenbaum, Allen R.
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    Abstract
    We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches.
    URI
    http://hdl.handle.net/1853/27945
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    • Biomedical Imaging Lab (Minerva Research Group) [210]

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