Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-touch gestural interaction in X3D using hidden Markov models
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
Towards workflow acquisition of assembly skills using hidden Markov models
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Towards a unified gesture description language
Proceedings of the 13th International Conference on Humans and Computers
Enhancing realism of mixed reality applications through real-time depth-imaging devices in X3D
Proceedings of the 16th International Conference on 3D Web Technology
Skeletal input for user interaction in X3D
Proceedings of the 18th International Conference on 3D Web Technology
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With the appearance of natural interaction devices such as the Microsoft Kinect or Asus Xtion PRO cameras, a whole new range of interaction modes have been opened up to developers. Tracking frameworks can make use of the additional depth image or skeleton tracking capabilities to recognize gestures. A popular example of one such implementation is the NITE framework from PrimeSense, which enables fine grained gesture recognition. However, recognized gestures come with additional information such as velocity, angle or accuracy, which are not encapsulated in a standardized format and therefore cannot be integrated into X3D in a meaningful way. In this paper, we propose a flexible way to inject gesture based meta data into X3D applications to enable fine grained interaction. We also discuss how to recognize these gestures if the underlying framework provides no mechanism to do so.