TCB-spline-based Image Vectorization
TCB-spline-based Image Vectorization
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DOI：
10.1145/3513132
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Haikuan Zhu;Juan Cao;Yanyang Xiao;Zhonggui Chen;Z. Zhong;Y. Zhang
中科院分区：
文献类型：
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作者：
Haikuan Zhu;Juan Cao;Yanyang Xiao;Zhonggui Chen;Z. Zhong;Y. Zhang
在许多实际应用中，需要能够忠实地重建光栅图像中的对象和颜色变化的矢量图像表示方法。本文提出了基于三角形配置B样条（简称TCB样条）的矢量图形，用于光栅图像矢量化。基于这种新的表示，提出了一种自动光栅图像矢量化范例。所提出的框架首先检测图像中的尖锐曲线特征，并根据检测到的特征线构建结网格。它迭代地优化控制点的颜色和位置并更新结网格。通过在特征线上使用共线结，可以通过同一组二次 TCB 样条有效地对平滑和不连续的颜色变化进行建模。设计了一种变分结网格生成方法，自适应地引入结并更新其连接性以满足局部重建质量。实验和比较表明，我们的框架在提供更忠实的重建结果方面优于现有的最先进的方法。特别是，我们的方法能够对未检测到的特征以及特征之间微妙或复杂的颜色变化进行建模，这是以前的方法无法有效处理的。我们的矢量化表示还有助于直接在矢量图像上执行各种编辑操作。
Vector image representation methods that can faithfully reconstruct objects and color variations in a raster image are desired in many practical applications. This article presents triangular configuration B-spline (referred to as TCB-spline)-based vector graphics for raster image vectorization. Based on this new representation, an automatic raster image vectorization paradigm is proposed. The proposed framework first detects sharp curvilinear features in the image and constructs knot meshes based on the detected feature lines. It iteratively optimizes color and position of control points and updates the knot meshes. By using collinear knots at feature lines, both smooth and discontinuous color variations can be efficiently modeled by the same set of quadratic TCB-splines. A variational knot mesh generation method is designed to adaptively introduce knots and update their connectivity to satisfy the local reconstruction quality. Experiments and comparisons show that our framework outperforms the existing state-of-the-art methods in providing more faithful reconstruction results. In particular, our method is able to model undetected features and subtle or complicated color variations in-between features, which the previous methods cannot handle efficiently. Our vectorization representation also facilitates a variety of editing operations performed directly over vector images.