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基于计算机视觉的片烟形状测量方法

A Computer Vision-based Method for Measuring Shape of Tobacco Strips

  • 摘要: 为准确描述片烟形状,基于计算机视觉提出了一种片烟形状测量方法。采用基于Mean-shift均值漂移算法的图像分割方法实现图像中片烟区域的初步提取,利用形态学梯度算法精确提取片烟轮廓。分别使用分形维数和傅里叶描述子两种方法对片烟形状进行定量描述,并以一组形状各异的片烟为测试对象,对分形维数和傅里叶描述子形状描述能力进行了分析实验。结果表明:对于不同形状的片烟,分形维数与傅里叶描述子的取值差异明显,且两种方法的取值随着片烟形状差异的扩大而增大,表现出单调性,验证了两种方法对片烟形状均具有可靠的描述能力。将两种形状定量方法同时运用于片烟形状测量系统,能够有效评价片烟形状。

     

    Abstract: In order to describe the shape of tobacco strips accurately, a measuring method based on computer vision was proposed. Image segmentation method based on Mean-shift algorithm was used to extract the area, where tobacco strip was present from an image, then the profile of tobacco strip was precisely extracted by a morphological gradient algorithm. The shape of tobacco strip was quantitatively described by Fractal dimension and Fourier descriptors separately. The ability of shape description of Fractal dimension and Fourier descriptors was tested with tobacco strips of different shapes. The results showed that for tobacco strips of different shapes, Fractal dimension and Fourier descriptors took different values and the difference between the two increased with the widening of the discrepancy between shapes in a monotonic way. It was verified that both methods were reliable. By adopting the two methods together, the shape of tobacco strip can be more effectively evaluated.

     

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