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Sketch recognition in interspersed drawings using time-based graphical models

机译:使用基于时间的图形模型在散布工程图中进行草图识别

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Sketching is a natural mode of interaction used in a variety of settings. With the increasing availability of pen-based computers, sketch recognition has gained attention as an enabling technology for natural pen-based interfaces. Previous work in sketch recognition has shown that in certain domains the stroke orderings used when drawing objects contain temporal patterns that can aid recognition. So far, systems that use temporal information for recognition have assumed that objects are drawn one at a time. This paper shows how this assumption can be relaxed to permit temporal interspersing of strokes from different objects. We describe a statistical framework based on dynamic Bayesian networks that explicitly models the fact that objects can be drawn interspersed. We present recognition results for hand-drawn electronic circuit diagrams, showing that handling interspersed drawing provides a significant increase in accuracy.
机译:草图绘制是一种在多种设置中使用的自然交互方式。随着基于笔的计算机的可用性不断提高,草图识别作为一种自然的基于笔的界面的使能技术受到了关注。草图识别的先前工作表明,在某些领域中,绘制对象时使用的笔画顺序包含有助于识别的时间模式。到目前为止,使用时间信息进行识别的系统已假定一次绘制一个对象。本文显示了如何放宽此假设以允许来自不同对象的笔画在时间上散布。我们描述了基于动态贝叶斯网络的统计框架,该框架显式地建模了可以散布对象的事实。我们提供了手绘电子电路图的识别结果,表明处理散布图可显着提高准确性。

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