基于小波域差值系数的织物疵点分割与识别  

Fabric Defects Classification and Identification Based on the Subtracted Images of Wavelet Coefficients

作　　者：赵静[1] 于凤芹[1] 

机构地区：[1]江南大学物联网工程学院,无锡214122

出　　处：《计算机系统应用》2011年第10期109-112,128,共5页Computer Systems & Applications

摘　　要：小波分解系数的织物疵点特征曲线容易受各层周期性噪声的影响,不能有效提取特征和定位疵点区域。提出了小波域差值系数的织物疵点分割与识别方法。首先将小波分解后的水平和垂直高频系数与平滑系数相减,除去周期性噪声,然后,分别提取水平和垂直差值系数熵、能量、方差曲线的最大值、均值及方差特征参数,最后利用支持向量机进行分类识别。仿真实验表明,该方法不仅能对织物疵点区域进行有效定位和分割,且识别率较直接提取小波系数特征的方法提高了4.17%。Fabric defect characteristic curves of the wavelet coefficients are vulnerable to periodic noise in all layers. It cannot effectively extract features and locate defects area. This paper proposed a method of feature extraction and defect segmentation based on the parameters of entropy, energy, variance curve of difference coefficient after wavelet transform. Firstly, it subtracts the horizontal and vertical high-frequency decomposition coefficients with the smoothing coefficients after wavelet transform, removes the periodic noise, extracts maximum, mean and deviation parameters from the curve difference horizontal and vertical coefficient. Then, it uses support vector machine to classify the extracted features. Simulation results show that the method can effectively locate and segement fabric defect region, and the recognition rate increased by 4.17% compared with the features extracted by wavelet coefficients.

关 键 词：小波变换 差值系数 特征提取 疵点检测 分类 

分 类 号：TS101.923[轻工技术与工程—纺织工程] TP391.41[轻工技术与工程—纺织科学与工程]