Title: Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

Authors: Romer Rosales and Stan Sclaroff

Date: June 10, 2000

Abstract:

A non-linear supervised learning architecture, the Specialized Mapping
Architecture (SMA) and its application to articulated body pose
reconstruction from single monocular images is described. The
architecture is formed by a number of specialized mapping functions,
each of them with the purpose of mapping certain portions (connected
or not) of the input space, and a feedback matching process.  A
probabilistic model for the architecture is described along with a
mechanism for learning its parameters. The learning problem is
approached using a maximum likelihood estimation framework; we present
Expectation Maximization (EM) algorithms for two different instances
of the likelihood probability. Performance is characterized by
estimating human body postures from low level visual features, showing
promising results