Unsupervised probabilistic segmentation of motion data for mimesis modeling
Unsupervised probabilistic segmentation of motion data for mimesis modeling
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DOI：
10.1109/icar.2005.1507443
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发表时间：
2005-07
期刊：
影响因子：
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通讯作者：
Bastien Janus;Yoshihiko Nakamura
中科院分区：
文献类型：
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作者：
Bastien Janus;Yoshihiko Nakamura
类人的发展表达了对能够自动实现行为获取和符号涌现的智能学习系统的需求。在模仿模型的框架下，我们提出了一种无监督的动态HMM算法，以分析矢量运动数据。通过对连续的真实的运动序列的分割，验证了该算法的有效性。我们还建议使用它作为第一级的信息处理系统，将其与识别过程相关联。与其他现有的分割识别系统不同，我们的分割过程不需要任何参数的学习，这增加了整个分割识别系统的灵活性及其可能的应用范围
Humanoid developments express the need for intelligent learning systems that can automatically realize behavior acquisition and symbol emergence. In the framework of mimesis model, we present an unsupervised dynamic HMM-based algorithm in order to analyze vectorial motion data. The efficiency of this algorithm is demonstrated by segmenting continuous sequence of real movements. We also propose to use it as the first level of an information treatment system by associating it with a recognition process. Unlike other existing segmentation-recognition system, our segmentation process does not need any learning of the parameters that increases the flexibility of the whole segmentation-recognition system and the range of its possible applications