多纹理分级融合的织物缺陷检测算法
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摘要
针对织物缺陷检测过程中纹理分布的复杂多样性引起误检和漏检的问题,结合织物纹理周期性特点,提出一种多纹理分级融合的织物缺陷检测算法。在检测过程中,首先利用织物缺陷图像的Tamura粗糙度图,对缺陷区域进行初步定位和自适应性生长,将初步定位的区域映射到原始织物图像中;其次根据织物图像的周期性分布特征,对初步定位区域进行分块,提取图像块的局部相位量化(LPQ)特征、Tamura特征,并将2种特征融合;然后计算融合特征与正常块特征的相似度,获取相似度图;最后将初步定位区域的经纬向特征图与相似度特征图融合,检测缺陷存在的区域。经TILDA织物纹理库数据的实验测试结果表明,缺陷区域的初步定位和自适应生长,降低了缺陷检测过程的冗余度,提高了检测效率,避免了织物缺陷检测过程中的误检和漏检情况。
Aiming at the false detection and missing detection caused by complexity and diversity of texture distribution in fabric defect detection,considering the periodicity of fabric texture,an algorithm of multi-texture gradation fusion for fabric defect detection was proposed. In the process of testing,firstly,the defect region was subjected to primary positioning and self-adaptive growth by using the Tamura roughness graph of the fabric defect image,then the primary positioned region was mapped to the original fabric image. The primary positioned region was blocked and the local phase quantization( LPQ) texture feature and Tamura texture feature of each image block were extracted,and the two different texture features were fused. The similarity between the fusion feature and the normal block feature was calculated to obtain the similarity image. Finally,the longitude and latitude feature map and the similarity feature map were fused to find the region of the defects in fabric images. The experimental results on TILDA dataset show that the new approach can reduce the redundancy of defect detection and improve the detection efficiency,and can avoid errors and omissions in the process of defect detection.
Aiming at the false detection and missing detection caused by complexity and diversity of texture distribution in fabric defect detection,considering the periodicity of fabric texture,an algorithm of multi-texture gradation fusion for fabric defect detection was proposed. In the process of testing,firstly,the defect region was subjected to primary positioning and self-adaptive growth by using the Tamura roughness graph of the fabric defect image,then the primary positioned region was mapped to the original fabric image. The primary positioned region was blocked and the local phase quantization( LPQ) texture feature and Tamura texture feature of each image block were extracted,and the two different texture features were fused. The similarity between the fusion feature and the normal block feature was calculated to obtain the similarity image. Finally,the longitude and latitude feature map and the similarity feature map were fused to find the region of the defects in fabric images. The experimental results on TILDA dataset show that the new approach can reduce the redundancy of defect detection and improve the detection efficiency,and can avoid errors and omissions in the process of defect detection.
引文
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[16]CHIKKERUR S,GOVINDARAJU V,CARTWRIG AN.Fingerprint image enhancement using STFT analysis[J].Pattern Recognition,2007,40(1):198-211.
[17]周建,王静安,高卫东.应用局部纹理特征的无监督织物瑕疵检测算法[J].纺织学报,2016,37(12):43-48.ZHOU Jian,WANG Jingan,GAO Weidong.An unsupervised fabric defect Detection algorithm based on Local texture feature[J].Journal of Textile Research,2016,37(12):43-48.
[18]王钢,周建,汪军,等.基于奇异值分解的机织物瑕疵检测算法[J].纺织学报,2014,35(7):61-66.WANG Gang,ZHOU Jian,WANG Jun,et al.Flaw detection algorithm for woven fabrics based on singular value decomposition[J].Journal of Textile Research,2014,35(7):61-66.
[2]LIU Bing,ZHANG Hong.Image retrieval algorithm based on convolutional neural network and manifold ranking[J].Journal of Computer Applications,2016,36(2):531-534.
[3]CHAN C H,PANG G K H.Fabric defect detection by Fourier analysis[J].IEEE Transactions on Industry Applications,2000,36(5):1267-1276.
[4]MAK K L,PENG P,YIU K F C.Fabric defect detection using morphological filters[J].Image&Vision Computing,2009,27(10):1585-1592.
[5]CHETVERIKOV D,HANBURY A.Finding defects in texture using regularity and local orientation[J].Pattern Recognition,2002,35(10):2165-2180.
[6]ALATA O,RAMANANJARASOA C.Unsupervised textured image segmentation using 2-D quarter plane autoregressive model with four prediction supports[J].Pattern Recognition Letters,2005,26(8):1069-1081.
[7]杜磊,李立轻,汪军,等.几种基于图像自适应阈值分割的织物疵点检测方法比较[J].纺织学报,2014,35(6):56-60.DU Lei,LI Liqing,WANG Jun,et al.Comparison of several fabric defect detection methods based on image adaptive threshold segmentation[J].Journal of Textile Research,2014,35(6):56-60.
[8]NGNA H Y T,PANG G K H,YUNG S P,et al.Wavelet based methods on patterned fabric defect detection[J].Pattern Recognition,2005,38(4):559-576.
[9]VERMAAK H,NSENGIYUMVAL P,LUWES N.Using the dual-tree complex wavelet transform for improved fabric defect detection[J].Journal of Sensors,2016,2016(7):1-8.
[10]BISSI L,BARUFF G,PLACIDIP,et al.Automated defect detection in uniform and structured fabrics using Gabor filters and PCA[J].Journal of Visual Communication and Image Representation,2013,24(7):838-845.
[11]JING J,FAN X,LI P.Automated fabric defect detection based on multiple gabor filters and KPCA[J].International Journal of Multimedia&Ubiquitous Engineering,2016,11(6):93-106.
[12]JING J,LIU S,LI P,et al.The fabric defect detection based on CIE L*a*b*color space using 2-D Gabor filter[J].Journal of the Textile Institute Proceedings&Abstracts,2016,107(10):1305-1313.
[13]杨晓波.基于GMRF模型的统计特征畸变织物疵点识别[J].纺织学报,2013,34(4):137-142.YANG Xiaobo.Fabric defect detection of statistic aberration feature based on GMRF model[J].Journal of Textile Research,2013,34(4):137-142.
[14]TAMURA H,MORI S,YAMAWAKI T.Textural features corresponding to visual perception[J].IEEETransactions on Systems Man and Cybernetics,1978,8(6):460-473.
[15]SACHS J,GAO L.Item-level and subscale-level factoring of Biggs'learning process questionnaire(LPQ)in a mainland Chinese sample[J].British Journal of Educational Psychology,2011,70(3):405-418.
[16]CHIKKERUR S,GOVINDARAJU V,CARTWRIG AN.Fingerprint image enhancement using STFT analysis[J].Pattern Recognition,2007,40(1):198-211.
[17]周建,王静安,高卫东.应用局部纹理特征的无监督织物瑕疵检测算法[J].纺织学报,2016,37(12):43-48.ZHOU Jian,WANG Jingan,GAO Weidong.An unsupervised fabric defect Detection algorithm based on Local texture feature[J].Journal of Textile Research,2016,37(12):43-48.
[18]王钢,周建,汪军,等.基于奇异值分解的机织物瑕疵检测算法[J].纺织学报,2014,35(7):61-66.WANG Gang,ZHOU Jian,WANG Jun,et al.Flaw detection algorithm for woven fabrics based on singular value decomposition[J].Journal of Textile Research,2014,35(7):61-66.