Patch-based image vectorization with automatic curvilinear feature alignment
Patch-based image vectorization with automatic curvilinear feature alignment
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DOI：
10.1145/1661412.1618461
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Tian Xia;Binbin Liao;Yizhou Yu
中科院分区：
文献类型：
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作者：
Tian Xia;Binbin Liao;Yizhou Yu
光栅图像矢量化是越来越重要的，因为基于矢量的图形内容已被采用在个人计算机和互联网上。在本文中，我们介绍了一种有效的基于矢量的表示和相关的矢量化算法的全色光栅图像。我们的代表有两个重要的特点。首先，图像平面被分解成不重叠的参数三角形曲面片。这样的简单布局支持灵活的拓扑结构，并有助于自适应补丁分布。其次，一个子集的曲面片边界是专门忠实地表示曲线特征。它们将自动与要素对齐。正因为如此，补丁预计有适度的内部变化，可以很好地近似使用平滑函数。我们已经开发出有效的技术补丁边界优化和补丁颜色拟合，以准确和compoundly近似光栅图像的平滑变化和曲线特征。一个实时的GPU加速并行算法的基础上递归补丁细分光栅化的矢量化图像也已开发。实验和比较表明，我们的图像矢量化算法实现了一个更准确和紧凑的矢量表示比现有的。
Raster image vectorization is increasingly important since vector-based graphical contents have been adopted in personal computers and on the Internet. In this paper, we introduce an effective vector-based representation and its associated vectorization algorithm for full-color raster images. There are two important characteristics of our representation. First, the image plane is decomposed into nonoverlapping parametric triangular patches with curved boundaries. Such a simplicial layout supports a flexible topology and facilitates adaptive patch distribution. Second, a subset of the curved patch boundaries are dedicated to faithfully representing curvilinear features. They are automatically aligned with the features. Because of this, patches are expected to have moderate internal variations that can be well approximated using smooth functions. We have developed effective techniques for patch boundary optimization and patch color fitting to accurately and compactly approximate raster images with both smooth variations and curvilinear features. A real-time GPU-accelerated parallel algorithm based on recursive patch subdivision has also been developed for rasterizing a vectorized image. Experiments and comparisons indicate our image vectorization algorithm achieves a more accurate and compact vector-based representation than existing ones do.