织物疵点识别算法的研究
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摘要
在纺织工业中,质量的控制是非常重要的,而织物疵点的检测是其中最重要的一部分。疵点检测的目的是在织造和验布过程中及时发现疵点,通过修复和整理,尽可能降低由疵点导致的织物质量的下降。
本课题在对国内外织物疵点自动检测系统和理论成果学习与研究的基础上,主要利用数字图像处理与分析技术对缺经和缺纬两类织物疵点进行了研究,改进了识别算法,提高了识别速度,减少了计算时间。同时,采用VC++与Matlab混合编程的模式,提高了软件开发的效率和软件运行速度。
本课题所做的主要工作为:将利用硬件设备得到的原始织物图像输入到计算机,然后将原始织物图像转换为灰度图像,再进一步转换为索引图像。利用3×3窗口的中值滤波器对图像进行去除噪声的处理,再利用直方图规定化增强图像对比度。在完成图像预处理的基础上,对图像进行快速检测,判断该图像是否含有疵点,对于含有疵点的图像进行小波变换,得到垂直和水平细节图像,并进行归一化得到织物各个特征值的曲线。将带疵点的织物图像的特征值曲线和正常织物的特征值曲线进行比较得到不同的特征值对不同类型疵点的响应是不同的,从而判断出疵点的存在。
通过对运行结果分析,本课题提出的疵点识别算法能够准确检测平纹机织物中的缺经、缺纬的疵点。
In the textile industry, quality control is very important, and the examination of fabric is an important part. The purpose of the fabric defects identification is to discover them in the time in the weaving and examing of the cloth process and reduce the fabric quality drop which causes by the defects as far as possible through the repair and the reorganization.
Based on the study and research of the development of the automatic deceting technology, the detection of fabric defects by digital image processing and analyzing technique is studied,two kinds of fabric defects end out and miss pick are researched in the thsis, then improve the identification algorithm,enhance the identification speed,reduce the computing time. At the same time,to make the program run fast and more effective in the developing,the co-programming with VC++ and Matlab is used in this thesis.
The main work done in the thsis : primitive fabric images obtained by the hardware equipment are input to the computer,then the primitive fabric images will be converted to grayscale images, further converted to index image, and then use the 3×3 window in the value of image filter to remove noise treatment, reuse of the histogram provides enhanced image contrast. Completed on the basis of image preprocessing, the image rapid detection, the judgement of the presence of faults image, the image containing defects wavelet transform, the details are vertical and horizontal images, and normalization of fabric are all eigenvalues curve , and the fabric belt defect image and the characteristics of the normal curve of the fabric eigenvalue curve comparisons of different characteristics of different types of faults on the response is different, and thus judged the existence of a defect.
Through the analysis of the results, the defects identification algorithm proposed in this thesis can exactly detect the defects of end out and miss pick on the tabby fabric.
本课题在对国内外织物疵点自动检测系统和理论成果学习与研究的基础上,主要利用数字图像处理与分析技术对缺经和缺纬两类织物疵点进行了研究,改进了识别算法,提高了识别速度,减少了计算时间。同时,采用VC++与Matlab混合编程的模式,提高了软件开发的效率和软件运行速度。
本课题所做的主要工作为:将利用硬件设备得到的原始织物图像输入到计算机,然后将原始织物图像转换为灰度图像,再进一步转换为索引图像。利用3×3窗口的中值滤波器对图像进行去除噪声的处理,再利用直方图规定化增强图像对比度。在完成图像预处理的基础上,对图像进行快速检测,判断该图像是否含有疵点,对于含有疵点的图像进行小波变换,得到垂直和水平细节图像,并进行归一化得到织物各个特征值的曲线。将带疵点的织物图像的特征值曲线和正常织物的特征值曲线进行比较得到不同的特征值对不同类型疵点的响应是不同的,从而判断出疵点的存在。
通过对运行结果分析,本课题提出的疵点识别算法能够准确检测平纹机织物中的缺经、缺纬的疵点。
In the textile industry, quality control is very important, and the examination of fabric is an important part. The purpose of the fabric defects identification is to discover them in the time in the weaving and examing of the cloth process and reduce the fabric quality drop which causes by the defects as far as possible through the repair and the reorganization.
Based on the study and research of the development of the automatic deceting technology, the detection of fabric defects by digital image processing and analyzing technique is studied,two kinds of fabric defects end out and miss pick are researched in the thsis, then improve the identification algorithm,enhance the identification speed,reduce the computing time. At the same time,to make the program run fast and more effective in the developing,the co-programming with VC++ and Matlab is used in this thesis.
The main work done in the thsis : primitive fabric images obtained by the hardware equipment are input to the computer,then the primitive fabric images will be converted to grayscale images, further converted to index image, and then use the 3×3 window in the value of image filter to remove noise treatment, reuse of the histogram provides enhanced image contrast. Completed on the basis of image preprocessing, the image rapid detection, the judgement of the presence of faults image, the image containing defects wavelet transform, the details are vertical and horizontal images, and normalization of fabric are all eigenvalues curve , and the fabric belt defect image and the characteristics of the normal curve of the fabric eigenvalue curve comparisons of different characteristics of different types of faults on the response is different, and thus judged the existence of a defect.
Through the analysis of the results, the defects identification algorithm proposed in this thesis can exactly detect the defects of end out and miss pick on the tabby fabric.
引文
[1]Schmalfub 著,俞雯 译,自动在线纺织检验,国际纺织学报,1999,3:52～56
[2]张乃尧,阎平凡,神经网络与模糊控制,北京:清华大学出版社,2004:45～49
[3]R Meier,J Uhlmann,R Leuenberger,Uster.Fabriscan automatic quality inspection system for fabrics,Melliand English,2003,5:E96-E97
[4]卿湘运,段红,魏俊民,基于局部熵的织物疵点检测与识别研究,纺织学报,2004,25(5):57～59
[5]努尔顿,左保齐,丝织物疵点智能化判别,丝绸,2003,4(10):34～36
[6]李立轻,黄秀宝,图像处理用于织物疵点自动检测的研究进展,东华大学学报(自然科学版),2002,28(4):118～121
[7]纺织品机织物疵点术语,中华人民共和国纺织行业标准,FZ01018-92
[8]Yoshio Shimizu,Expert system to inspect fabric defects by pattern recognition,Sen-Gakkaishi,2000,46(1):460-469
[9]F S Cohen,Z Fan,S Attali,Automated inspection of textile using textural models,IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,13(8):803-808
[10]Sar Sardy,Lamyarni Ibrahim,Yoshizumi Ysauda,An application of vision system for the identification and defect detection on woven fabrics by using artificial neural network,Proceedines of 1993 international ioint conference on neural networks,1999,3:2141-2144
[11]Y F Zhang,R R bresee,Fabric defect detection and classification using image analysis,Textile Research Journal,2000,65(1):1-9
[12]I-Shou Tsai,Ming-Chuan Hu,Automatic inspection of fabric defects using an artificial neural network technique,Textile Research Journal,1996,66(7):472-482
[13]S Rajasekaran,Trainning-free counter propagation neural for pattern recognition offabric defects,Textile Research Journal,1997,67(6):401-405
[14]Chi-ho Chan,Grantham K.H Pang,Fabric defect detection by Fourier analysis,IEEE Transactions on Industry Applications,2000,36(5):1267-1276
[15]M C Hu,I S Tsai,The inspection of fabric defects by using wavelet transform,Journal of textile Institute,2003,91(3):420-433
[16]H Sari-Sarrsf,J S Goddard,Vision system for on-loom fabric inspection,IEEE Transactionson Industry Applications,2002,35(6):1252-1259
[17]彭玉华,小波变换与工程应用,北京:科学出版社,2007年1月
[18]马云芳,王小华,基于小波理论的织物纹理特征提取,杭州电子工业学院学报,2002,22(6):85～88
[19]周世生,高等色彩学,北京:印刷工业出版社,1997
[20]飞思科技产品研发中心,MATLAB6.5辅助小波分析与应用,北京:电子工业出版社,2003年1月
[21]蔡汉添,小波变换域中图像噪声平滑技术,光学技术,1998,6
[22]李辉,蒋秀明等,Matlab语言在数字图像中值滤波中的应用研究,天津工业大学学报,2003,22(1):7～88
[23]吴小清,邓中民,王芳,徐造坤,基于Matlab的机织物密度自动检测,纺织科技进展,2006,6:70～74
[24]高成,Matlab接口技术与应用,北京:国防工业出版社,2005年1月
[25]张志涌,精通MATLAB6.5版,北京航空航天大学出版社,2003年3月
[26]尹立民,王兴东,Visual C++6.0应用编程150例,北京:电子工业出版社,2004年3月
[27]李立轻,基于计算机视觉的织物疵点自动检测研究,博士学位论文,东华大学,2003年5月
[28]卿湘运,基于图像的织物疵点检测与识别的研究,硕士学位论文,浙江工程学院,2003年12月
[29]祁林,织物疵点的计算机软件识别方法研究,硕士学位论文,武汉理工大学,2006年4月
[30]贡玉南,织物疵点的图像分析方法研究,博士学位论文,东华大学,1999年12月
[31]姜玲,织物疵点检测算法的研究,硕士学位论文,北京服装学院,2006年3月
[32]徐增波,贡玉南,黄秀宝,基于二维连续小波变换的织物疵点检测,中国纺织大学学报,2000,26(2):lO～13
[33]屈萍鸽,刘曙光,张慧,小波分析在织物缺陷检测中的应用,纺织高校基础科学学报,2004,17(4):164～167
[34]李立轻,黄秀宝,用于疵点检测的织物自适应正交小波的实现,东华大学学报,2002,28(2):77～8l
[35]丁雪荣,宋广礼,计算机图像处理技术在纺织品测试中的应用,计算机应用,2005,33(6):59～61
[36]姜平,周根荣,基于计算机视觉的织物疵点自动检测,传感器与微系统,2006,25(1):73～75
[37]步红刚,黄秀宝,基于计算机视觉的织物疵点分类的近期进展,东华大学学报(自然科学版),2006,32(4):143～146
[38]Kenneth.R.Castleman,数字图象处理,北京:电子工业出版社,2001年
[39]章毓晋,图像分割,北京:科学出版社,2001:1～195
[40]胡昌华、张军波、夏军、张伟,基于MATLAB的系统分析与设计-小波分析,西安:西安电子科技大学出版社,2000年
[41]陈武凡等,小波分析及其在图象处理中的应用,北京:科学出版社,2002:1～120,152～193
[42]D A Karras,B G Mertzios.Improved Defect Detection Using Novel Wavelet Feature Extraction Involving Principal Component Analysis and Neural Network Techniques.Lecture Notes in Computer Science,2002(2257):638-647
[43]Albert Boggess,FrancisJ.Narcowich,小波与傅立叶分析基础(英文版),北京:电子工业出版社,1～278
[44]努尔顿,左保齐,图像识别与纺织品检验,苏州大学学报,2003,23(2):49～54
[45]Miguel Angel Garcia,Domenec Puig.Pixel Classification by Diver2genceBased Integration of Multiple Texture Methods and Its Ap2plication to Fabric Defect Detection.Lecture Notes in Computer Science,2003,(2781):132-139
[46]倪广菊,张毅,计算机图像处理与分析在纺织品测试技术中的应用,中国纤检,2003,11:8～10
[47]董长虹,高志,余啸海,Matlab小波分析工具箱原理与应用,北京:国防工业出版社,2004
[48]汪成龙、李晶、高晓丁,高效能的织物疵点识别算法及其实现,棉纺织技术,2004,32(6):23～26
[49]段红,卿湘运,织物疵点检测与识别的方法研究,测试技术学报,2005,19(1):48～51
[50]崔锦泰,小波分析导论,西安:西安交通大学出版社,1995
[2]张乃尧,阎平凡,神经网络与模糊控制,北京:清华大学出版社,2004:45～49
[3]R Meier,J Uhlmann,R Leuenberger,Uster.Fabriscan automatic quality inspection system for fabrics,Melliand English,2003,5:E96-E97
[4]卿湘运,段红,魏俊民,基于局部熵的织物疵点检测与识别研究,纺织学报,2004,25(5):57～59
[5]努尔顿,左保齐,丝织物疵点智能化判别,丝绸,2003,4(10):34～36
[6]李立轻,黄秀宝,图像处理用于织物疵点自动检测的研究进展,东华大学学报(自然科学版),2002,28(4):118～121
[7]纺织品机织物疵点术语,中华人民共和国纺织行业标准,FZ01018-92
[8]Yoshio Shimizu,Expert system to inspect fabric defects by pattern recognition,Sen-Gakkaishi,2000,46(1):460-469
[9]F S Cohen,Z Fan,S Attali,Automated inspection of textile using textural models,IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,13(8):803-808
[10]Sar Sardy,Lamyarni Ibrahim,Yoshizumi Ysauda,An application of vision system for the identification and defect detection on woven fabrics by using artificial neural network,Proceedines of 1993 international ioint conference on neural networks,1999,3:2141-2144
[11]Y F Zhang,R R bresee,Fabric defect detection and classification using image analysis,Textile Research Journal,2000,65(1):1-9
[12]I-Shou Tsai,Ming-Chuan Hu,Automatic inspection of fabric defects using an artificial neural network technique,Textile Research Journal,1996,66(7):472-482
[13]S Rajasekaran,Trainning-free counter propagation neural for pattern recognition offabric defects,Textile Research Journal,1997,67(6):401-405
[14]Chi-ho Chan,Grantham K.H Pang,Fabric defect detection by Fourier analysis,IEEE Transactions on Industry Applications,2000,36(5):1267-1276
[15]M C Hu,I S Tsai,The inspection of fabric defects by using wavelet transform,Journal of textile Institute,2003,91(3):420-433
[16]H Sari-Sarrsf,J S Goddard,Vision system for on-loom fabric inspection,IEEE Transactionson Industry Applications,2002,35(6):1252-1259
[17]彭玉华,小波变换与工程应用,北京:科学出版社,2007年1月
[18]马云芳,王小华,基于小波理论的织物纹理特征提取,杭州电子工业学院学报,2002,22(6):85～88
[19]周世生,高等色彩学,北京:印刷工业出版社,1997
[20]飞思科技产品研发中心,MATLAB6.5辅助小波分析与应用,北京:电子工业出版社,2003年1月
[21]蔡汉添,小波变换域中图像噪声平滑技术,光学技术,1998,6
[22]李辉,蒋秀明等,Matlab语言在数字图像中值滤波中的应用研究,天津工业大学学报,2003,22(1):7～88
[23]吴小清,邓中民,王芳,徐造坤,基于Matlab的机织物密度自动检测,纺织科技进展,2006,6:70～74
[24]高成,Matlab接口技术与应用,北京:国防工业出版社,2005年1月
[25]张志涌,精通MATLAB6.5版,北京航空航天大学出版社,2003年3月
[26]尹立民,王兴东,Visual C++6.0应用编程150例,北京:电子工业出版社,2004年3月
[27]李立轻,基于计算机视觉的织物疵点自动检测研究,博士学位论文,东华大学,2003年5月
[28]卿湘运,基于图像的织物疵点检测与识别的研究,硕士学位论文,浙江工程学院,2003年12月
[29]祁林,织物疵点的计算机软件识别方法研究,硕士学位论文,武汉理工大学,2006年4月
[30]贡玉南,织物疵点的图像分析方法研究,博士学位论文,东华大学,1999年12月
[31]姜玲,织物疵点检测算法的研究,硕士学位论文,北京服装学院,2006年3月
[32]徐增波,贡玉南,黄秀宝,基于二维连续小波变换的织物疵点检测,中国纺织大学学报,2000,26(2):lO～13
[33]屈萍鸽,刘曙光,张慧,小波分析在织物缺陷检测中的应用,纺织高校基础科学学报,2004,17(4):164～167
[34]李立轻,黄秀宝,用于疵点检测的织物自适应正交小波的实现,东华大学学报,2002,28(2):77～8l
[35]丁雪荣,宋广礼,计算机图像处理技术在纺织品测试中的应用,计算机应用,2005,33(6):59～61
[36]姜平,周根荣,基于计算机视觉的织物疵点自动检测,传感器与微系统,2006,25(1):73～75
[37]步红刚,黄秀宝,基于计算机视觉的织物疵点分类的近期进展,东华大学学报(自然科学版),2006,32(4):143～146
[38]Kenneth.R.Castleman,数字图象处理,北京:电子工业出版社,2001年
[39]章毓晋,图像分割,北京:科学出版社,2001:1～195
[40]胡昌华、张军波、夏军、张伟,基于MATLAB的系统分析与设计-小波分析,西安:西安电子科技大学出版社,2000年
[41]陈武凡等,小波分析及其在图象处理中的应用,北京:科学出版社,2002:1～120,152～193
[42]D A Karras,B G Mertzios.Improved Defect Detection Using Novel Wavelet Feature Extraction Involving Principal Component Analysis and Neural Network Techniques.Lecture Notes in Computer Science,2002(2257):638-647
[43]Albert Boggess,FrancisJ.Narcowich,小波与傅立叶分析基础(英文版),北京:电子工业出版社,1～278
[44]努尔顿,左保齐,图像识别与纺织品检验,苏州大学学报,2003,23(2):49～54
[45]Miguel Angel Garcia,Domenec Puig.Pixel Classification by Diver2genceBased Integration of Multiple Texture Methods and Its Ap2plication to Fabric Defect Detection.Lecture Notes in Computer Science,2003,(2781):132-139
[46]倪广菊,张毅,计算机图像处理与分析在纺织品测试技术中的应用,中国纤检,2003,11:8～10
[47]董长虹,高志,余啸海,Matlab小波分析工具箱原理与应用,北京:国防工业出版社,2004
[48]汪成龙、李晶、高晓丁,高效能的织物疵点识别算法及其实现,棉纺织技术,2004,32(6):23～26
[49]段红,卿湘运,织物疵点检测与识别的方法研究,测试技术学报,2005,19(1):48～51
[50]崔锦泰,小波分析导论,西安:西安交通大学出版社,1995
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