应用单演小波分析的织物疵点检测

为了解决现有织物疵点检测算法对种类繁多的疵点形式尤其是对微弱纹理变化疵点的适应性较弱问题，提出以单演小波分析工具为基础的织物疵点检测算法。通过拉普拉斯分数阶算子与多重调和样条构建各向同性拉普拉斯小波后，对其进行Riesz变换构建Riesz–拉普拉斯小波，实现了织物图像的单演小波分析。对单演小波分析结果中的多分辨率方向与振幅子带，分别设计了最优响应子带判断标准以及最优响应子带分割方法。实验结果表明，本文提出的检测算法可有效分割不同织物纹理中的多种类疵点，分割结果可反映疵点位置与轮廓，对342幅实验样本图像实现了97.37%的检出率，具有较好的自适应性与鲁棒性。

Fabric defect detection using monogenic wavelet analysis

In order to overcome the poor adaptability ofexisting fabric defect detection algorithms on numerous kinds of defects, especially the ones appearing as minor texture changes, a fabric defect detection algorithm based on monogenic wavelet analysis was proposed. The monogenic wavelet analysis on fabric images works with the Riesz?Laplace wavelet, which is generated by performing Riesz transform to an isotropic Laplace wavelet constructed by combining a fractional Laplacian and a polyharmonic spline. For the multiresolusional orientation and amplitude subbands outputted by monogenic wavelet analysis, respective criteria for the best responses and segmentation method on the best response subbands were designed. Experiment results showed that the proposed detection algorithm could effectively segment various kinds of defects in different fabric textures, consequently demonstrating the position and shape of defects, and achieved a detection rate of 97.37% on 342 experimental sample images, bearing a sound self-adaptability and robustness.