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Sparse pixel vectorization: an algorithm and its performance evaluation

机译:稀疏像素向量化:一种算法及其性能评估

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摘要

Accurate and efficient vectorization of line drawings is essential for their higher level processing. We present a thinningless sparse pixel vectorization (SPV) algorithm. Rather than visiting all the points along the wire's black area, SPV sparsely visits selected medial axis points. The result is a crude polyline, which is refined through polygonal approximation by removing redundant points. Due to the sparseness of pixel examination and the use of a specialized data structure, SPV is both time efficient and accurate, as evaluated by our proposed performance evaluation criteria.
机译:线图的准确和有效矢量化对于其更高级别的处理至关重要。我们提出了一种稀疏稀疏像素矢量化(SPV)算法。 SPV不会访问导线的黑色区域上的所有点,而是会稀疏地访问选定的中间轴点。结果是粗折线,可通过去除多余点,通过多边形逼近来对其进行细化。由于像素检查的稀疏性以及使用专用数据结构的原因,SPV既省时又准确,这是根据我们提出的性能评估标准进行评估的。

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