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Summary: Boston University Computer Science Tech. Report No. 2000-22,Dec. 2000 (revised Apr. 2001).
To Appear in Proc. IEEE International Conf. on Computer Vision (ICCV). Canada. Jul. 2001.
3D Hand Pose Reconstruction Using Specialized Mappings
R“omer Rosales, Vassilis Athitsos, Leonid Sigal, and Stan Sclaroff£
Boston University, Computer Science Department
111 Cummington St., Boston, MA 02215
email: rrosales,athitsos,lsigal,sclaroff @bu.edu
Abstract
A system for recovering 3D hand pose from monocu-
lar color sequences is proposed. The system employs a
non-linear supervised learning framework, the specialized
mappings architecture (SMA), to map image features to
likely 3D hand poses. The SMA's fundamental components
are a set of specialized forward mapping functions, and a
single feedback matching function. The forward functions
are estimated directly from training data, which in our case
are examples of hand joint configurations and their corre-
sponding visual features. The joint angle data in the train-
ing set is obtained via a CyberGlove, a glove with 22 sen-
sors that monitor the angular motions of the palm and fin-
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