A touch interface for soft data modeling in Bayesian estimation
A touch interface for soft data modeling in Bayesian estimation
复制标题
贝叶斯估计中软数据建模的触摸界面
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
J. Curtis
中科院分区：
文献类型：
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作者：
S. Mehta;M. McCourt;E. Doucette;J. Curtis
提出了一种基于触摸界面设备的“软信息”建模与贝叶斯融合的新方法。人类生成的软信息可以使用单个、多个和重叠笔画的组合来编码，这些笔画表示可以使用非参数密度估计器来近似的任意测量似然函数。所提出的接口提供了一个灵活和自然的媒介来编码一个大类的定性不同类型的信息的积极和消极的意见。触摸界面自然地提供了关于心理生理和环境参数方面的人类可变性的鲁棒性，而不需要离线训练。一个城市目标跟踪的例子来说明融合的软信息（使用建议的软传感器模型产生）与传统的自动传感器的测量。
A novel approach for human-generated “soft information” modeling and Bayesian fusion using touch interface devices is presented. The human-generated soft information can be encoded using a combination of single, multiple, and overlapping strokes that represent arbitrary measurement likelihood functions which can be approximated using non-parametric density estimators. The proposed interface offers a flexible and natural medium to encode a large class of qualitatively distinct types of information for both positive and negative observations. The touch interface naturally provides robustness with respect to human variability in terms of psycho-physiological and environmental parameters without the need for offline training. An urban-target tracking example is provided to illustrate fusion of soft information (generated using the proposed soft sensor model) with measurements from traditional automated sensors.