CAJ | 학술논문

针对常见织物疵点具有方向性,利用传统空间域特征识别方法不能有效定位局部疵点区域且当样本较少时分类率低的问题,为有效定位疵点且提高分类率,提出了水平和垂直方向上小波域特征,利用能有效解决小样本分类问题的支持向量机进行分类识别;并对利用图像灰度共生矩阵特征及小波域特征的分类结果进行了比较。仿真实验结果表明,所选特征不仅能对织物疵点区域进行水平和垂直方向上的定位,而且得到了较高的正确分类率。
침대상견직물자점구유방향성,이용전통공간역특정식별방법불능유효정위국부자점구역차당양본교소시분류솔저적문제,위유효정위자점차제고분류솔,제출료수평화수직방향상소파역특정,이용능유효해결소양본분류문제적지지향량궤진행분류식별;병대이용도상회도공생구진특정급소파역특정적분류결과진행료비교。방진실험결과표명,소선특정불부능대직물자점구역진행수평화수직방향상적정위,이차득도료교고적정학분류솔。
In view of that common fabric defects have directivity,it used the problems that traditional spatial characteristics can not effectively locate defect region and the accuracyis always unsatisfactory when samples are less,and in order to effectively locate faults and improve the classification rate,it proposed a method of wavelet domain features on the horizontal and vertical direction,used support vector machine to effectively solve limited sample classification problem,compared classification results between the traditional features of GLCM（gray level co-occurrence matrix）and wavelet domain features.Experiment results showed that using wavelet features can locate the areas of fabric defects on the horizontal and vertical position and also can receive a higher classification accurate.