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So far, discovery mechanisms based on the UDDI standard rely on many centralized and area-specific directories, which poses information stress problems such as performance bottlenecks and fault tolerance.
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Second, we rely on pose information during training.
However, finer action recognition requires to segment the human body and extract the pose information.
In this paper, we extend the spherical harmonics representation to encode pose information.
The authors of this paper extend the spherical harmonics representation to encode pose information.
We train an interaction model from only a few videos with pose information.
A set of inertial sensors is employed for collecting pose information including yaw angle.
Ning et al. [1] propose a model by adding one hidden layer to conditional random fields (CRF) containing pose information.
Especially when relying only on pose information alone in the HOG model, the confusion between interactions increases.
This can be attributed to the inaccurate pose information in the training data as a result of frequent occlusions.
Without such pose information, discriminative patterns of image movements are modeled implicitly, for instance using co-occurring spatiotemporal codewords [15].
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