基于稀疏优化的织物疵点检测算法

纺织学报 ›› 2016, Vol. 37 ›› Issue (05): 56-61.

基于稀疏优化的织物疵点检测算法

收稿日期:2015-04-03 修回日期:2016-01-28 出版日期:2016-05-15 发布日期:2016-05-10

Fabric defect detection algorithm research based on sparse optimization

Received:2015-04-03 Revised:2016-01-28 Online:2016-05-15 Published:2016-05-10

摘要/Abstract

摘要：

为提高稀疏表示方法对织物疵点的检测精度，提出了基于稀疏优化的织物疵点检测算法。首先，利用L1范数最小化方法从待检织物图像中学习出自适应字典库，用该库对织物图像稀疏表示，进而计算出稀疏表示系数矩阵；然后，对系数矩阵进行优化处理，采用字典库及优化系数矩阵对织物图像稀疏重构；最后，将重构图像与待检织物图像相减生成残差图像，用最大熵阈值方法对残差图像进行分割，定位出疵点区域。实验结果表明，本文算法所重构图像准确地表示了正常织物纹理，相比已有检测方法具有较高的疵点检测精度。

关键词: L1范数, 稀疏表示, 织物图像, 疵点检测

Abstract:

A novel fabric defect detection algorighm based on sparse optimization is proposed. Firstly, an adaptive dictionary is learned from test fabric image using L1-norm minimization method, the test fabric image is sparsely represented using the learned dictionary, and then the coefficient matrix of sparse representation is xalculated. Secondly, the abnormal coefficients are removed using optimization function, then a new image is reconstructed using the optimized coefficient matrix and the dictionary. Finally, the reconstructed image is subtracted from original test image to acquire a residual image, and then the maximum entropy threshold method is used to segment the defect region. Experimental results demonstrate that the proposed algorithm has higher detection accuracy comparing with the state of the art.

Key words: L1-norm, sparse representation, textile image, defect detection

刘洲峰闫磊李春雷董燕李阳. 基于稀疏优化的织物疵点检测算法[J]. 纺织学报, 2016, 37(05): 56-61.

参考文献

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