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李雪琴, 蒋红海, 刘培勇, 殷国富. 非下采样Contourlet域自适应阈值面的磁瓦表面缺陷检测[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 553-558.
引用本文: 李雪琴, 蒋红海, 刘培勇, 殷国富. 非下采样Contourlet域自适应阈值面的磁瓦表面缺陷检测[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 553-558.
Li Xueqin, Jiang Honghai, Liu Peiyong, Yin Guofu. Defect Detection on Magnetic Tile Surface Based on Adaptive Threshold Surfaces in NSCT Domain[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(4): 553-558.
Citation: Li Xueqin, Jiang Honghai, Liu Peiyong, Yin Guofu. Defect Detection on Magnetic Tile Surface Based on Adaptive Threshold Surfaces in NSCT Domain[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(4): 553-558.

非下采样Contourlet域自适应阈值面的磁瓦表面缺陷检测

Defect Detection on Magnetic Tile Surface Based on Adaptive Threshold Surfaces in NSCT Domain

  • 摘要: 为提高磁瓦表面缺陷人工检测效率、防止缺陷漏检,针对经典缺陷检测算法不能很好地提取颜色暗、对比度低的磁瓦图像缺陷问题,提出一种非下采样Contourlet域自适应阈值面的磁瓦缺陷自动检测方法.该方法根据非下采样Contourlet变换(NSCT)子带系数所在不同区域的特性,采用线扫描的方式对NSCT系数进行阈值处理,给出不同尺度、不同方向的归一化自适应阈值面;并与原始NSCT各子带归一化系数对比分割,以实现对磁瓦图像NSCT系数逐列自适应修正;最后重构NSCT系数提取出磁瓦缺陷.实验结果表明,文中方法能够有效地去除磁瓦表面纹理,提取出磁瓦表面缺陷的准确率可达95%.

     

    Abstract: In order to improve the defect detection efficiency on magnetic tile surface, prevent defect undetected, an automatic detection method is proposed for the problem that defects with dark color and low contrast on magnetic tile surface can not be extracted via the classical defect detection algorithm. This method is based on adaptive threshold surface in nonsubsampled Contourlet domain.According to the characteristics of NSCT sub-band coefficients in different regions, the threshold of the NSCT coefficients is changed by using line-scan model, and then the normalized adaptive threshold surfaces are formed for different scales and different directions.Compared with the original NSCT sub-band coefficients, a series of new NSCT sub-band coefficients are formed.Finally the new NSCT coefficients are reconstructed and the defects on magnetic tile surface are extracted.The experimental results indicate that the proposed method can remove texture on magnetic tile surface effectively and the accuracy rate of extraction defect achieves 95%.

     

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