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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >Effective Clipart Image Vectorization through Direct Optimization of Bezigons
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Effective Clipart Image Vectorization through Direct Optimization of Bezigons

机译：通过直接优化bezigons有效的剪贴画图像矢量化

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摘要

Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.
机译：Bezigons，即由Bézier曲线组成的闭合路径，已被广泛用于描述图像矢量化结果中的形状。但是，大多数现有的矢量化技术都通过简单地近似中间矢量表示形式（例如多边形）来推断bezigon。因此，特别是对于低分辨率图像，由于累积的误差，拟合歧义和缺少曲线先验而有时会导致所生成的bezigon不完美。在本文中，我们描述了一种矢量化剪贴画图像的新颖方法。与以前的方法相比，我们直接优化了bezigon，而不是使用其他中间表示。因此，与原始栅格图像相比，所得的bezigon不仅保真度更高，而且由于熟练的专家对其进行了追踪，因此更加合理。为了实现这种优化，我们克服了一些挑战，并设计了可区分的数据能量以及一些基于曲线的先验项。为了提高优化效率，我们还利用了bezigons的局部控制特性，并采用了重叠的分段优化策略。实验结果表明，就苯并gon质量而言，我们的方法优于当前的最新方法和常用的商业软件。

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