| 陈 珺,王亦红.基于机器视觉的低对比度纸病识别算法研究[J].中国造纸学报,2013,28(2):29-33 |
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| 基于机器视觉的低对比度纸病识别算法研究 |
| Identification Algorithm of Low Contrast Paper Defects Based on Machine Vision |
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| DOI：10.11981/j.issn.1000-6842.2013.02.29 |
| 中文关键词: LOG算子 数学形态学 小波变换 图像融合 纸病识别 |
| Key Words:LOG operator mathematical morphology wavelet transformation image fusion paper defect identification |
| 基金项目: |
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| 摘要点击次数: 3969 |
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| 中文摘要: |
| 利用机器视觉识别纸病时，若背景与目标纸病的对比度低，且采用单一的边缘检测算法，将会出现对目标纸病边缘定位不准确、抗噪性能不好等问题。对此，提出了一种解决方法，即首先分别用LOG算子和基于数学形态学的边缘检测方法对低对比度纸病图像进行边缘检测，然后对这两种边缘检测算法得到的图像进行小波融合，融合得到的图像中纸病边缘定位准确且具有一定的抗噪性，最后，对此进行了实验验证。研究结果表明，文中提出的解决方法可行，即可将该方法用于基于机器视觉的低对比度纸病识别。 |
| Abstract: |
| When machine vision is used to identify low contrast paper defects, if single edge detection algorithm is adopted, paper defects edge location become inaccurate, anti-noise performance is not good because of low contrast between the backgrounded and paper defects. This paper proposes a solution for this problem. Firstly, the paper containing low contrast paper defects should be edge detected by LOG operator and mathematical morphology operator separately. Then, the two images which are obtained by two types of edge detection algorithms should be merged by wavelet. In the fusion image, the paper defect’s edge location is accurate and that algorithm has the ability of anti-noise. Finally, the validity and dependability of the method and arithmetic have been validated. |
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