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An efficient and general-purpose technique for grouping hand-drawn pen strokes into objects.

机译:一种有效的通用技术,用于将手绘的笔触分组为对象。

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摘要

Engineers use sketches in the early phases of design because their expressiveness and ease of creation facilitate creativity and efficient communication. Our goal is to build software that leverages these strengths and enables natural sketch-based human-computer interaction. Specifically, our work is focused on creating algorithms that group hand-drawn strokes into individual objects so that they can be recognized. Grouping strokes is a difficult problem. Many previous approaches have required the user to manually group the strokes. Others have used a search process, resulting in a computational cost that rises exponentially with the number of strokes in the sketch. In this dissertation we present a novel method for grouping strokes into objects based on a two-step algorithm that has a polynomial computational cost. In the first step, strokes are classified according to the type of object to which they belong, thus helping to create artificial separation between objects. In the second step, a pairwise classifier groups strokes of the same class into individual objects. Both steps rely on general machine learning techniques which seamlessly integrate spatial and temporal information, and which can be extended to new domains with no hand-coding. Our single-stroke classifier is the first in literature to perform multi-way classification to facilitate efficient grouping, and it performs as well as or better than previous classifiers on text vs. non-text classification. Our grouping algorithm correctly groups between 84% and 91% of the ink in diagrams from four different domains, with between 61% and 82% of objects being perfectly clustered. Our method runs in O( n2) time, where n is the number of points in the sketch. Real-world performance is improved with a conservative filter to eliminate consideration of distant strokes, and computation occurs incrementally as the sketch is constructed. Even without the filter, the computation for a large sketch containing over 700 strokes took less than 12% of the time required to draw the sketch. Experimental evaluation of our technique has shown it to be accurate and effective in four domains.
机译:工程师在设计的早期阶段就使用草图,因为它们的表现力和易于创建有助于创造力和有效的沟通。我们的目标是构建能够利用这些优势并实现基于草图的自然人机交互的软件。具体来说,我们的工作重点是创建将手绘笔划分组为单个对象的算法,以便可以识别它们。将笔划分组是一个难题。许多先前的方法要求用户手动将笔划分组。其他人则使用搜索过程,导致计算成本随草图中笔划的数量呈指数增长。在本文中,我们提出了一种新的方法,将笔划分组为对象,该算法基于具有多项式计算成本的两步算法。第一步,根据笔划所属的对象类型对笔划进行分类,从而有助于在对象之间建立人为的分隔。在第二步中,成对分类器将相同类别的笔划分组为单个对象。这两个步骤都依赖于通用的机器学习技术,该技术无缝集成了空间和时间信息,并且无需手动编码即可扩展到新领域。我们的单笔划分类器是文献中第一个执行多方分类以促进有效分组的文献,它在文本分类和非文本分类上的表现均优于或优于以前的分类器。我们的分组算法在来自四个不同域的图表中正确地将墨水的84%到91%进行了分组,其中61%到82%的对象完美地聚集在一起。我们的方法在O(n2)时间中运行,其中n是草图中的点数。使用保守的滤波器可以消除对远距离笔划的影响,从而提高了实际性能,并且在构建草图时以递增方式进行计算。即使没有过滤器,对于包含700多个笔画的大型草图,计算所用的时间也不到绘制草图所需时间的12%。对我们技术的实验评估表明,它在四个领域都是准确有效的。

著录项

  • 作者

    Peterson, Eric Jeffrey.;

  • 作者单位

    University of California, Riverside.;

  • 授予单位 University of California, Riverside.;
  • 学科 Engineering Mechanical.;Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 203 p.
  • 总页数 203
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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