In this paper we present an automatic vectorization algorithm based on image marking.
本文我们提出了一种基于标记的自动矢量化方法。
SIMD optimization is very similar to traditional automatic vectorization for vector processors.
这种优化和传统的针对向量处理器的自动向量化非常类似。
For automatic vectorization or scalarization, the compiler USES versions of the MASS functions contained in the compiler library libxlopt.a.
对于自动化的标量或者向量，汇编器会使用汇编器库libxlopt . a中包含的mass函数的版本。
According to the applications in real systems, this method is both efficient and available in automatic vectorization and recognition of engineering drawings.
实际系统应用表明，该方法在图纸自动矢量化和识别中非常有效。
Vectorization is a crucial step in the automatic punching.
矢量化是图像自动编针中很重要的步骤。
The automatic recognition of electro-symbol is the base of engineering drawing vectorization.
工程图纸中电气符号的自动识别是实现电气工程图纸矢量化的基础。
This paper not only improved this algorithm but also implemented semi-automatic tracking grid-vectorization and central line pick-up using this algorithm.
本文对该算法进行了优化处理，实现了栅格矢量化半自动跟踪和面状地物中心线提取。
This paper not only improved this algorithm but also implemented semi-automatic tracking grid-vectorization and central line pick-up using this algorithm.
本文对该算法进行了优化处理，实现了栅格矢量化半自动跟踪和面状地物中心线提取。
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