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In this paper, we propose a novel model-free approach for tracking multiple objects from RGB-D point set data. This study aims to achieve the robust tracking of arbitrary objects against dynamic interaction cases in real-time. In order to represent an object without prior knowledge, the probability density of each object is represented by Gaussian mixture models (GMM) with a tempo-spatial topological...
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...
Optical motion capturing systems are widely used to acquire human beings' motion patterns in humanoid imitation learning research. However, optical motion capturing systems have a restricted movable area. This paper proposes the HMM based mimesis scheme using a monocular camera mounted on a humanoid. This scheme releases the restriction of movable area and enables imitation in daily life environments...
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