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Missing motion data recovery using factorial hidden Markov models
2008
2008 IEEE International Conference on Robotics and Automation
This paper proposes a method to recover missing data during observation by factorial hidden Markov models (FHMMs). The fundamental idea of the proposed method originates from the mimesis model, inspired by the mirror neuron system. By combining the motion recognition from partial observation algorithm and the proto-symbol based duplication of observed motion algorithm, whole body motion imitation from partial observation can be achieved. The algorithm for missing data recovery uses the same
doi:10.1109/robot.2008.4543449
dblp:conf/icra/LeeKN08
fatcat:n6jkvjlie5h5tdpandc2by3kja