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CVPR
2008
IEEE

Learning stick-figure models using nonparametric Bayesian priors over trees

14 years 5 months ago
Learning stick-figure models using nonparametric Bayesian priors over trees
We present a fully probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree in such a way that their parameter distributions are probabilistically centered around their parent-node. This prior enables the inference procedures to learn multiple explanations for motion-capture data, each of which could be trees of different depth and path lengths. Thus, the algorithm can automatically determine a reasonable distribution over the number of sticks in a given dataset and their hierarchical relationships. We provide experimental results on several motion-capture datasets, demonstrating the model's ability to recover plausible stick-figure structure, and also the model's robust behavior when faced with occlusion.
Edward Meeds, David A. Ross, Richard S. Zemel, Sam
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis
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