采用图像处理的织物缝纫平整度自动评估

为解决织物缝纫平整度客观自动评估时分类正确率低的问题，提出了一种基于灰度共生矩阵、小波分析和反向传播（BP）神经网络相结合的织物缝纫平整度的自动评估方法。首先采集标准缝纫图像，将图像的灰度级降至16 级，计算图像在0°和90°方向上的灰度共生矩阵并将其归一化，提取灰度共生矩阵的能量、熵、对比度和相关性4 个特征参数，并分别对特征参数在0°和90°方向上取均值；同时，运用Haar 小波在第6个分析尺度上提取并计算图像的水平细节系数的标准差。然后将提取的这5 个特征参数输入到BP 神经网络中训练和识别，并对标准缝纫图像进行了评估。评估结果显示：提出的算法与单独采用灰度共生矩阵特征、小波特征相比，具有较高的分类正确率，分类效果稳定。

Automatic seam-puckering evaluation using image processing

In order to solve the problem on low-accuracy classification in objective automatic evaluation of seam-puckering,a novel method based on gray level co-occurrence matrix,wavelet analysis and back propagation(BP) network was proposed for automatic seam-puckering evaluation.Firstly,a standard seam image was captured and the gray level of the seam image was reduced to 16 level,the gray level co-occurrence matrix of the seam image in the direction of 0° and 90° were calculated and normalized,then four characteristic parameters including energy,entropy,contrast and correlation were extracted from the gray level co-occurrence matrix,and the mean values of the characteristic parameters were obtained in the direction of 0° and 90°,respectively.Meanwhile,the standard deviation of the horizontal detail coefficients of the seam image was extracted and calculated by using Haar wavelet on the sixth analysis scales.After that,five extracted characteristic parameters were input to the BP neural network for training and recognizing,and the standard seam image was evaluated.The evaluation results show that the proposed algorithm,compared with one adopting gray level co-occurrence matrix characteristic or wavelet characteristic alone,has higher correct classification rate and stable classification effect.