基于MBLBPV算法的布匹瑕疵检测方法
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摘要
针对布匹瑕疵检测,在传统局部二值模式(Local Binary Pattern, LBP)与局部二值模式方差(LBP Variance,LBPV)的基础上,提出一种基于多尺度分块局部二值模式方差(Multi-Scale Block Local Binary Patterns Variance, MBLBPV)的检测算法。首先,采用适当尺度大小的子区域灰度均值代替单像素灰度值,提取LB P特征,以降低噪声影响;然后,融合图像区域对比度信息,并将其作为编码值的权重,提取图像MBLBPV特征,并基于该特征实现瑕疵的检测。实验结果表明,相对于传统方法,MBLBPV抗噪力强、检测正确率更高。
Based on the truditional local binary pattern( LBP) and the LBP variance( LBPV), a novel algorithm called multi-scale local binary patterns variance( MBLBPV) is presented for fabric defect detection.Firstly, the average gray level of a subarea of the appropriate scale is used to replace the gray level of a single pixel and the LBP feature is extracted to reduce the noise influence. Then, the image region contrast information is fused and used as the weight of the encoded value to extract the MBLBPV features, and the fabric defects are detected based on the extracted features. Experimental results show that MBLBPV has higher detection accuracy and is more robust to image noise than the traditional approaches.
Based on the truditional local binary pattern( LBP) and the LBP variance( LBPV), a novel algorithm called multi-scale local binary patterns variance( MBLBPV) is presented for fabric defect detection.Firstly, the average gray level of a subarea of the appropriate scale is used to replace the gray level of a single pixel and the LBP feature is extracted to reduce the noise influence. Then, the image region contrast information is fused and used as the weight of the encoded value to extract the MBLBPV features, and the fabric defects are detected based on the extracted features. Experimental results show that MBLBPV has higher detection accuracy and is more robust to image noise than the traditional approaches.
引文
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[15]徐平华,丁雪梅,吴雄英,等.基于LBP-V的真皮表层纹理特征提取[J].皮革科学与工程,2016,26(3):49-54.
[2]史艳琼,卢荣胜,张腾达,等.周期纹理背景中的表面缺陷检测技术[J].测控技术,2016,35(1):17-20.
[3]李文羽,程隆棣.基于机器视觉和图像处理的织物疵点检测研究新进展[J].纺织学报,2014,35(3):158-164.
[4]张玉继,雷威,李文博.织物疵点自动检测方法研究进展[J].纺织科技进展,2017,196(5):5-8.
[5] Chen Z H,Feng X X. The design of optimal real Gabor filters and their applications in fabric defect detection[J]. Coloration Technology,2015,131(4):279-287.
[6] Li W C,Tsai D M. Wavelet-based defect detection in solarwafer images with inhomogeneous texture[J]. Pattern Recognition,2012,45(2):742-756.
[7] Ojala T,Pietikainen M,Harwood D. A Comparative Study of Texture Measures with Classification Based on Feature Distributions[J]. Pattern Recognition, 1996,29(1):51-59.
[8]孙君顶,周业勇.局部二值模式及其扩展方法研究与展望[J].计算机应用与软件,2016,33(1):203-210.
[9] Tajeripour F,Kabir E,Sheikhi A. Fabric defect detection using modified local binary patterns[J]. EURASIP Journal on Advances in Signal Processing,2008,2008(1).
[10]付蓉,石美红,陈惠娟.自适应局部二值模式算法及其在织物疵点检测中的应用[J].纺织高校基础科学学报,2010,23(1):99-104.
[11]项明,姚雪存,江有福.LBPV算法在织物瑕疵检测中的应用[J].丝绸,2014,51(2):35-39.
[12] Guo Z H,Zhang L,Zhang D. Rotation invariant texture classification using LBP variance(LBPV)with global matching[J]. Pattern recognition,2010,43(3):706-719.
[13] Ojala T, Pietikainen M,Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,2002,24(7):971-987.
[14]杨海燕,刘国栋.基于MB-LBP算子和Multilinear PCA算法的人脸识别[J].计算机应用研究,2012,29(12).
[15]徐平华,丁雪梅,吴雄英,等.基于LBP-V的真皮表层纹理特征提取[J].皮革科学与工程,2016,26(3):49-54.