In order to establish an objective, stable and efficient wrinkle evaluation system for fabric wrinkle evaluation, a method based on 2-D Gabor transform was proposed. Among this system, the directions of Gabor filter were determined according to the range of amplitude response. Then a set of Gabor filters were obtained by selecting and optimizing the central frequency, the half peak bandwidth and the shape factor of the Gaussian surface. After Gabor transform by such filter bank, the amplitudes of different response spectrums were extracted, constructing a multi-dimensional feature vector. Finally, the feature vectors of the fabric image samples, whose wrinkle degrees were evaluated manually in advance, were extracted and used to train a support vector machine (SVM), which achieved 81.82 % evaluation accuracy on the 345 samples. The trained SVM was applied to evaluate the wrinkle degree of the fabric samples acquired in different illumination directions, and verified the stability of the proposed method to illumination environment. Compared with the existing method, the proposed method has higher classification accuracy. The comparison results indicate the Gabor amplitude feature proposed by this research has a high correlation with the fabric wrinkle grades.

为了建立客观，稳定，有效的织物皱纹评价系统，提出了一种基于二维Gabor变换的方法。在该系统中，根据振幅响应的范围来确定Gabor滤波器的方向。然后，通过选择和优化中心频率，半峰带宽和高斯表面的形状因子，获得了一组Gabor滤波器。经过这种滤波器组的Gabor变换后，提取出不同响应谱的幅度，构建了多维特征向量。最终，提取出织物图像样本的特征向量，该样本的褶皱度是预先手动评估的，并用于训练支持向量机（SVM），对345个样本的评估精度达到81.82％。训练后的支持向量机用于评估在不同照明方向上采集的织物样品的皱纹度，并验证了该方法对照明环境的稳定性。与现有方法相比，该方法具有较高的分类精度。比较结果表明，该研究提出的Gabor振幅特征与织物的皱纹等级具有高度相关性。