安阳工学院学报
安暘工學院學報
안양공학원학보
JOURNAL OF ANYANG INSTITUTE OF TECHNOLOGY
2015年
4期
38-40
,共3页
局部相位量化%主成分分析%疵点识别%神经网络
跼部相位量化%主成分分析%疵點識彆%神經網絡
국부상위양화%주성분분석%자점식별%신경망락
the local phase quantization%the principal component analysis%fabric defect recognition%neural net-work
给出了一种基于LPQ特征向量的帘子布疵点识别方法。首先给出了局部相位量化(LPQ)的定义,，然后计算帘子布样本图像的LPQ特征向量，使用PCA降维处理，再利用降维后的LPQ特征向量对预先设置的BP神经网络参数进行最优选择，最后利用最优的BP神经网络作为帘子布疵点分类器。此识别算法能对断经、浆斑、劈缝、稀经和经线粘连等帘子布疵点进行有效识别。
給齣瞭一種基于LPQ特徵嚮量的簾子佈疵點識彆方法。首先給齣瞭跼部相位量化(LPQ)的定義,，然後計算簾子佈樣本圖像的LPQ特徵嚮量，使用PCA降維處理，再利用降維後的LPQ特徵嚮量對預先設置的BP神經網絡參數進行最優選擇，最後利用最優的BP神經網絡作為簾子佈疵點分類器。此識彆算法能對斷經、漿斑、劈縫、稀經和經線粘連等簾子佈疵點進行有效識彆。
급출료일충기우LPQ특정향량적렴자포자점식별방법。수선급출료국부상위양화(LPQ)적정의,，연후계산렴자포양본도상적LPQ특정향량，사용PCA강유처리，재이용강유후적LPQ특정향량대예선설치적BP신경망락삼수진행최우선택，최후이용최우적BP신경망락작위렴자포자점분류기。차식별산법능대단경、장반、벽봉、희경화경선점련등렴자포자점진행유효식별。
In this paper, the method based on the Local Phase Quantization are presented to recognize the de?fects of the cord fabric. First of all, A new kind of the Local Phase Quantization is constructed, and its implemen?tation is given. Then, the Local Phase Quantization of the cord sample images are calculate, the BP neural net?work is trained by these moments. At last, it is used trained BP neural network to implement the cord fabric ’s de?fect identification. Experiment results show that the method accurately identifies defects such as broke end, lump, split slot, broken warp and warp adhesion.
