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Abstract:
A nonlinear supervised learning model, the Specialized
Mappings Architecture (SMA), is described and applied to the
estimation of human body pose from monocular images. The SMA
consists of several specialized forward mapping functions and an
inverse mapping function. Each specialized function maps certain
domains of the input space (image features) onto the output space
(body pose parameters). The key algorithmic problems faced are
those of learning the specialized domains and mapping functions
in an optimal way, as well as performing inference given inputs
and knowledge of the inverse function. Solutions to these
problems employ the EM algorithm and alternating choices of
conditional independence assumptions. Performance of the approach
is evaluated with synthetic and real video sequences of human
motion.
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