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Segmenting Hand-Drawn Strokes

机译:分割手绘笔画

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摘要

Pen-based interfaces utilize sketch recognition so users can create and interact with complex, graphical systems via drawn input. In order for people to freely drawwithin these systems, users' drawing styles should not be constrained. The low-level techniques involved with sketch recognition must then be perfected, because poorlow-level accuracy can impair a user's interaction experience.Corner finding, also known as stroke segmentation, is one of the first steps to free-form sketch recognition. Corner finding breaks a drawn stroke into a set of primitive symbols such as lines, arcs, and circles, so that the original stoke data can be transformed into a more machine-friendly format. By working with sketched primitives, drawn objects can then be described in a visual language, noting whatprimitive shapes have been drawn and the shapes? geometric relationships to eachother.We present three new corner finding techniques that improve segmentation accuracy. Our first technique, MergeCF, is a multi-primitive segmenter that splits drawn strokes into primitive lines and arcs. MergeCF eliminates extraneous primitives by merging them with their neighboring segments. Our second technique, ShortStraw,works with polyline-only data. Polyline segments are important since many domains use simple polyline symbols formed with squares, triangles, and arrows. Our ShortStrawalgorithm is simple to implement, yet more powerful than previous polyline work in the corner finding literature. Lastly, we demonstrate how a combination technique can be used to pull the best corner finding results from multiple segmentation algorithms. This combination segmenter utilizes the best corners found from other segmentation techniques, eliminating many false negatives (missed primitive segmentations) from the final, low-level results.We will present the implementation and results from our new segmentation techniques, showing how they perform better than related work in the corner finding field. We will also discuss limitations of each technique, how we have sought to overcome those limitations, and where we believe the sketch recognition subfield of corner finding is headed.
机译:基于笔的界面利用草图识别,因此用户可以通过绘制的输入创建复杂的图形系统并与之交互。为了使人们能够在这些系统中自由绘制,不应限制用户的绘制样式。然后必须完善与草图识别有关的低级技术,因为低级的准确度会削弱用户的交互体验。拐角查找(也称为笔划分割)是自由形式草图识别的第一步。角点查找将绘制的笔划分解为一组原始符号,例如直线,圆弧和圆形,以便可以将原始的笔划数据转换为更机器友好的格式。通过使用草绘的基元,可以用视觉语言描述绘制的对象,注意已绘制了哪些基元形状以及这些形状?彼此的几何关系。我们提出了三种新的拐角发现技术,可提高分割精度。我们的第一个技术MergeCF是一个多图元分割器,可将绘制的笔划分为原始线和圆弧。 MergeCF通过将它们与相邻段合并来消除无关的基元。我们的第二种技术ShortStraw可用于仅折线数据。折线段很重要,因为许多域使用由正方形,三角形和箭头形成的简单折线符号。我们的ShortStrawalgorithm方法易于实现,但比以前的转角查找文献中的折线工作更强大。最后,我们演示了如何使用组合技术从多个分割算法中获得最佳的拐角发现结果。这种组合分割器利用了其他分割技术中发现的最佳角点,从最终的低级结果中消除了许多假阴性(缺少原始分割)。我们将介绍新的细分技术的实现和结果,展示它们在角落搜索领域中比相关工作表现更好。我们还将讨论每种技术的局限性,我们如何设法克服这些局限性以及我们认为拐角发现的草图识别子领域将走向何方。

著录项

  • 作者

    Wolin Aaron David;

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  • 年度 2010
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  • 原文格式 PDF
  • 正文语种 en_US
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