纺织学报 ›› 2016, Vol. 37 ›› Issue (06): 136-141.

• 管理与信息化 • 上一篇    下一篇

应用Gaussian回代交替方向图像分解算法的色织物疵点检测

  

  • 收稿日期:2015-06-25 修回日期:2016-03-12 出版日期:2016-06-15 发布日期:2016-06-17

Yarn-dyed fabric defect detection based on Gaussian back substitution image decomposition

  • Received:2015-06-25 Revised:2016-03-12 Online:2016-06-15 Published:2016-06-17

摘要:

针对传统的人工织物检测方法效率低,稳定性差,处理速度慢的问题。提出了基于Gaussian回代交替方向(ADMG) 的图像分解的印花织物疵点检测算法。首先对疵点织物进行直方图均衡化的预处理操作,以减少织物背景纹理信息对织物疵点检测产生的影响。然后采用总方差范数与Sobolev空间中的半范数相结合的Gaussian回代交替方向的图像分解算法,将印花织物图像分解为疵点部分u和纹理部分v。最后,应用二维Otsu阈值方法将图像的疵点部分u分割,识别织物图像上的疵点。实验结果表明:通过基于ADMG图像分解算法对包括星型,方格型和圆点型在内的印花织物图像疵点检测是可行、有效的,可以得到满意的识别结果。

关键词: 图像分解, 织物疵点检测, 总方差范数, Gaussian回代交替方向法

Abstract:

Focusing on the problems of low detection efficiency, poor stability and slow processing speed of traditional artificial fabric detection, a patterned fabric defect detection method based on alternating direction method with Gaussian back substitution (ADMG) image decomposition was presented. Firstly, histogram equalization as preprocessing was first conducted for the sampled images to eliminate the influence of background texture of fabric defects. Secondly, ADMG image decomposition method based on combination of the total variation norm and semi-norm in negative Sobolev space was employed, the patterned fabric images could be decomposed into defect structure u and texture structure v. Finally the defect structure u was segmented by using a two-dimensional Otsu thresholding, the fabric defects could be identified. The experimental results demonstrate that method based on ADMG image decomposition is feasible and effective in patterned fabric defect detection contained star-, box- and dot- patterned fabric images and satisfactory identification results could be achieved.

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