基于小波域的贾卡经编针织物图像花纹分割技术研究
|
摘要
贾卡经编针织物由于贾卡导纱针的不同偏移规律可形成“厚”、“薄”和“网孔”等垫纱效应，再配以其它工艺参数的多元变化，形成独特的多层次视觉效应，精致而丰富的花纹图案，以及柔软舒适的质感，而深受市场和消费者喜爱，产品适用于高档内衣、蕾丝辅料以及室内软装饰等纺织品中。在贾卡织物设计过程中，尽管目前经编CAD系统的二维绘图和工艺处理功能已基本完善，但对于贾卡织物图像的花纹分割等前期处理功能，还需借助于第三方图像处理软件的套索和魔术棒等工具近乎手工地完成织物图像的分割和提取工作。该过程十分耗时且繁琐，将近占用了整个设计过程的70%时间。可见如何改变这种耗时费力的设计方式，快速、准确地获得贾卡经编针织物的花纹图案是经编CAD系统一大亟待于解决的问题。
论文以贾卡经编针织物为研究对象，采用计算机数字图像处理技术，对织物图像的纹理特征提取和分割方法展开了系统研究。各章节主要内容概述如下：
第一章介绍了论文研究目的和意义，详细概述了国内外文献研究现状和现有纺织CAD系统中织物图像分割技术，分析了贾卡织物图像花纹分割难点所在，并提出了论文主要研究内容和创新点。
第二章阐述了贾卡经编机机构组成、起花原理以及贾卡经编针织物分类和特点，分析了贾卡织物图像纹理和噪音信息形成的原因，并着重研究了高斯滤波和双边滤波算法基本原理、不同的参数配置对贾卡织物图像影响。其中，双边滤波采用动态加权系数，在削弱噪音信息，平滑织物图像的同时，也保护了边缘细节，更适合于贾卡织物图像的预处理。
第三章实现了经预处理后的贾卡织物图像的多分辨率尺度分解。首先介绍了小波变换基础理论，探讨了贾卡织物图像小波金字塔式结构和小波树型结构两种分解模式，并提出了一种基于图像能量等纹理特征的分解模式判别法则，使贾卡织物图像纹理特征得以充分利用。通过多分辨率尺度分解可以简化贾卡织物图像模型，降低计算工作量，同时为后续的分割模型提供多层次的细节特征，尤其是局部细节特征。
第四章主要内容为基于小波域的贾卡织物图像改进K均值聚类花纹分割方法。首先概述了传统图像分割方法和K均值聚类基本工作原理，并针对传统K均值聚类算法在贾卡织物图像分割过程中存在的问题，例如随机选取初始聚类中心，对贾卡织物图像中的大量噪声信息过于敏感等问题提出改进。在改进的K均值算法中，采用小波多分辨率分解对噪声信息去相关性，密度函数和数据邻域等算法优化初始中心点的选择方法，并根据各尺度上频带特征矢量的离散程度给予不同的权重值，增强或削弱特征分量在K均值聚类过程中的作用。本章最后进行算法的比较实验，结果证明该分割算法与改进前相比，准确率更高，适宜花纹数量较少，质地纹理较为细腻的贾卡经编针织物图像。改进的K均值聚类算法除了作为一种单独的分割算法外，同时能在后续马尔可夫随机场（Markov Random Field，MRF）模型中的低分辨率尺度的花纹分割中发挥作用。
第五章主要内容为贾卡织物图像多分辨率MRF建模与花纹分割。首先阐述了传统MRF模型及其在贾卡织物图像分割中的应用。再者，针对传统MRF模型中存在的势函数取值过于依赖人工经验，特征场模型中对于图像中的噪音信号问题考虑不足，以及期望最大算法的迭代终止条件等问题，论文提出一种自适应权重的贾卡织物图像多分辨率建模与分割算法，其中通过一自适应权重函数来调整特征场模型和标号场模型在分割过程中的控制权比重，随着分辨率的增大，权重函数取值逐渐减小，分割过程的主导地位由特征场能量控制转向标号场能量控制，分割结果逐步得到精细化，同时可以明显削弱依据经验给定的势函数取值对分割结果的影响。同时，面对多尺度MRF建模过程中，常采用空域非因果方式建模，以及计算过程负载较大难以达到全局优化等问题，提出一种基于MRF层次模型的贾卡织物图像花纹分割算法，算法中采用融合伽马分布和高斯分布的有限通用混合模型来逼近图像的小波系数，零均值隐状态的混合概率分布来描述织物的噪音信息，标号场模型融合尺度间的因果方式和尺度内的非因果方式建模，并采用SMAP参数估计准则通过非迭代方法得到在最高分辨率尺度上的分割结果。本章最后通过与不同算法比较实验，结果表明该算法计算速度和分割准确率均能达到一定的设计要求，适用面较广。
第六章对全文做了总结和展望。给出了论文主要结论的同时，提出了进一步完善研究的设想。
Due to the factors such as weave structure, fiber and dyeing-and-finishing, jacquardwarp-knitted fabric is multifarious in patterns and exquisite in workmanship, for which thejacquard fabric is very popular and widely used in high-grade lingerie, garment accessory andfurnishing fabric. Although the function of2D drawing and process configuration in currentCAD system of warp knitting is nearly perfect, the pretreatment such as pattern separation ofjacquard fabric is done by using simple mapping tool such as lasso and magic wand. So it isan extremely repetitious, laborious and time-consuming work ranging from a few hours toseveral days, which occupies too much time and will increase production costs. In this case, todevelop a rapid, efficient and automatic pattern separation system for jacquard warp-knittedfabric is rather urgent.
This paper focuses on texture characteristics and pattern separation methods of jacquardwarp-knitted fabric by means of computer image processing. The content of each chapter isbriefly introduced as follows.
In Chapter1, the research purposes and significance are introduced briefly. Overseas anddomestic research statuses, the current pattern segmentation methods in warp knitting CADsystem are summarized. Then the difficulty in pattern segmentation is analyzed. The researchtopics and innovative points of this paper are proposed.
In Chapter2, the machine structure, weaving mechanism and the classification ofjacquard warp-knitted fabrics are introduced. Then the underlying reason for fabric textureand noise signals of jacquard warp-knitted fabric are analyzed. Finally, an algorithm whichcan smoothen the fabric image, weaken noise signal and protect the detail information ofmarginal region is proposed.
In Chapter3, the multi-resolution wavelet decomposition of the pretreated jacquardwarp-knitted fabric image is described. Firstly, the background of wavelet transform and thetwo decomposition models such as pyramid structure and tree structure are introduced briefly.Then a discriminant rule of decomposition model for jacquard warp-knitted fabric is proposed.The analysis result indicates that multi-resolution wavelet decomposition can simplify themodel jacquard fabric, lessen the computational burden, and provide the multi-level detailcharacteristic, especially the local detail characteristic.
In Chapter4, this paper focuses on the modified K-means clustering algorithm inwavelet transform for jacquard warp-knitted fabric. Firstly, the traditional image segmentationmethods and the mechanism of K-means clustering are introduced. The problems oftraditional K-means clustering for jacquard warp-knitted fabric are analyzed, such as randomchoice of initial clustering center and the susceptivity to noise information of jacquard fabric.Then a modified K-means clustering algorithm is proposed, which includes waveletmulti-resolution decomposition, the optimized initial clustering center and weighting factorbased on dispersion degree. The modified K-means clustering algorithm is not only aindependent algorithm, but also plays an important role in successive chapter.
In Chapter5, the concentrates on multi-resolution MRF model for pattern separation of jacquard warp-knitted fabric. Firstly, the traditional MRF model is introduced briefly.Secondly, on account of the problems of potential function, which relays too much onartificial expertise, and feature field which takes insufficient account of noise signals, thepaper proposes a multiresolution Markov random field with adaptive weighting in waveletdomain. The proposed algorithm can control the ratio of feature model energy to label modelenergy using a adaptive weighting function. Thirdly, by reason of non-causal label model andthe computation burden of iterations, the paper proposes a new pattern segmentation based onhierarchical Markov random field model. In new algorithm the label model takes into accountof not only the relationship between global and local, but also the modeling methods such asinter-scale causal model and intra-scale uncausal model. Finally, the segmentation results areobtained by SMAP parameter estimation which is a un-iterative algorithm in originalresolution scale.
In Chapter6, the summary is introduced, which includes the main contributions and theproblems of the present study.
论文以贾卡经编针织物为研究对象，采用计算机数字图像处理技术，对织物图像的纹理特征提取和分割方法展开了系统研究。各章节主要内容概述如下：
第一章介绍了论文研究目的和意义，详细概述了国内外文献研究现状和现有纺织CAD系统中织物图像分割技术，分析了贾卡织物图像花纹分割难点所在，并提出了论文主要研究内容和创新点。
第二章阐述了贾卡经编机机构组成、起花原理以及贾卡经编针织物分类和特点，分析了贾卡织物图像纹理和噪音信息形成的原因，并着重研究了高斯滤波和双边滤波算法基本原理、不同的参数配置对贾卡织物图像影响。其中，双边滤波采用动态加权系数，在削弱噪音信息，平滑织物图像的同时，也保护了边缘细节，更适合于贾卡织物图像的预处理。
第三章实现了经预处理后的贾卡织物图像的多分辨率尺度分解。首先介绍了小波变换基础理论，探讨了贾卡织物图像小波金字塔式结构和小波树型结构两种分解模式，并提出了一种基于图像能量等纹理特征的分解模式判别法则，使贾卡织物图像纹理特征得以充分利用。通过多分辨率尺度分解可以简化贾卡织物图像模型，降低计算工作量，同时为后续的分割模型提供多层次的细节特征，尤其是局部细节特征。
第四章主要内容为基于小波域的贾卡织物图像改进K均值聚类花纹分割方法。首先概述了传统图像分割方法和K均值聚类基本工作原理，并针对传统K均值聚类算法在贾卡织物图像分割过程中存在的问题，例如随机选取初始聚类中心，对贾卡织物图像中的大量噪声信息过于敏感等问题提出改进。在改进的K均值算法中，采用小波多分辨率分解对噪声信息去相关性，密度函数和数据邻域等算法优化初始中心点的选择方法，并根据各尺度上频带特征矢量的离散程度给予不同的权重值，增强或削弱特征分量在K均值聚类过程中的作用。本章最后进行算法的比较实验，结果证明该分割算法与改进前相比，准确率更高，适宜花纹数量较少，质地纹理较为细腻的贾卡经编针织物图像。改进的K均值聚类算法除了作为一种单独的分割算法外，同时能在后续马尔可夫随机场（Markov Random Field，MRF）模型中的低分辨率尺度的花纹分割中发挥作用。
第五章主要内容为贾卡织物图像多分辨率MRF建模与花纹分割。首先阐述了传统MRF模型及其在贾卡织物图像分割中的应用。再者，针对传统MRF模型中存在的势函数取值过于依赖人工经验，特征场模型中对于图像中的噪音信号问题考虑不足，以及期望最大算法的迭代终止条件等问题，论文提出一种自适应权重的贾卡织物图像多分辨率建模与分割算法，其中通过一自适应权重函数来调整特征场模型和标号场模型在分割过程中的控制权比重，随着分辨率的增大，权重函数取值逐渐减小，分割过程的主导地位由特征场能量控制转向标号场能量控制，分割结果逐步得到精细化，同时可以明显削弱依据经验给定的势函数取值对分割结果的影响。同时，面对多尺度MRF建模过程中，常采用空域非因果方式建模，以及计算过程负载较大难以达到全局优化等问题，提出一种基于MRF层次模型的贾卡织物图像花纹分割算法，算法中采用融合伽马分布和高斯分布的有限通用混合模型来逼近图像的小波系数，零均值隐状态的混合概率分布来描述织物的噪音信息，标号场模型融合尺度间的因果方式和尺度内的非因果方式建模，并采用SMAP参数估计准则通过非迭代方法得到在最高分辨率尺度上的分割结果。本章最后通过与不同算法比较实验，结果表明该算法计算速度和分割准确率均能达到一定的设计要求，适用面较广。
第六章对全文做了总结和展望。给出了论文主要结论的同时，提出了进一步完善研究的设想。
Due to the factors such as weave structure, fiber and dyeing-and-finishing, jacquardwarp-knitted fabric is multifarious in patterns and exquisite in workmanship, for which thejacquard fabric is very popular and widely used in high-grade lingerie, garment accessory andfurnishing fabric. Although the function of2D drawing and process configuration in currentCAD system of warp knitting is nearly perfect, the pretreatment such as pattern separation ofjacquard fabric is done by using simple mapping tool such as lasso and magic wand. So it isan extremely repetitious, laborious and time-consuming work ranging from a few hours toseveral days, which occupies too much time and will increase production costs. In this case, todevelop a rapid, efficient and automatic pattern separation system for jacquard warp-knittedfabric is rather urgent.
This paper focuses on texture characteristics and pattern separation methods of jacquardwarp-knitted fabric by means of computer image processing. The content of each chapter isbriefly introduced as follows.
In Chapter1, the research purposes and significance are introduced briefly. Overseas anddomestic research statuses, the current pattern segmentation methods in warp knitting CADsystem are summarized. Then the difficulty in pattern segmentation is analyzed. The researchtopics and innovative points of this paper are proposed.
In Chapter2, the machine structure, weaving mechanism and the classification ofjacquard warp-knitted fabrics are introduced. Then the underlying reason for fabric textureand noise signals of jacquard warp-knitted fabric are analyzed. Finally, an algorithm whichcan smoothen the fabric image, weaken noise signal and protect the detail information ofmarginal region is proposed.
In Chapter3, the multi-resolution wavelet decomposition of the pretreated jacquardwarp-knitted fabric image is described. Firstly, the background of wavelet transform and thetwo decomposition models such as pyramid structure and tree structure are introduced briefly.Then a discriminant rule of decomposition model for jacquard warp-knitted fabric is proposed.The analysis result indicates that multi-resolution wavelet decomposition can simplify themodel jacquard fabric, lessen the computational burden, and provide the multi-level detailcharacteristic, especially the local detail characteristic.
In Chapter4, this paper focuses on the modified K-means clustering algorithm inwavelet transform for jacquard warp-knitted fabric. Firstly, the traditional image segmentationmethods and the mechanism of K-means clustering are introduced. The problems oftraditional K-means clustering for jacquard warp-knitted fabric are analyzed, such as randomchoice of initial clustering center and the susceptivity to noise information of jacquard fabric.Then a modified K-means clustering algorithm is proposed, which includes waveletmulti-resolution decomposition, the optimized initial clustering center and weighting factorbased on dispersion degree. The modified K-means clustering algorithm is not only aindependent algorithm, but also plays an important role in successive chapter.
In Chapter5, the concentrates on multi-resolution MRF model for pattern separation of jacquard warp-knitted fabric. Firstly, the traditional MRF model is introduced briefly.Secondly, on account of the problems of potential function, which relays too much onartificial expertise, and feature field which takes insufficient account of noise signals, thepaper proposes a multiresolution Markov random field with adaptive weighting in waveletdomain. The proposed algorithm can control the ratio of feature model energy to label modelenergy using a adaptive weighting function. Thirdly, by reason of non-causal label model andthe computation burden of iterations, the paper proposes a new pattern segmentation based onhierarchical Markov random field model. In new algorithm the label model takes into accountof not only the relationship between global and local, but also the modeling methods such asinter-scale causal model and intra-scale uncausal model. Finally, the segmentation results areobtained by SMAP parameter estimation which is a un-iterative algorithm in originalresolution scale.
In Chapter6, the summary is introduced, which includes the main contributions and theproblems of the present study.
引文
1. Wang R W, Wu X Y, Wang S Y. Automatic identification of ramie and cotton fibers using characteristics in longitudinal view, Part I:Locating capture of fiber images [J]. Textile Research Journal,2009,79(14):1251-1259.
2. Wang R W, Wu X Y, Wang S Y. Automatic identification of ramie and cotton fibers using characteristics in longitudinal view, Part Ⅱ:Fiber stripes analysis [J]. Textile Research Journal,2009,79(17):1547-1556.
3. Wang R W, Yan J L, Wu X Y. Automatic Identification of Lyocell and Cotton Fibers Using Cluster Analysis [J]. Textile Research Journal,2010,80(13):1330-1334.
4. Liu X, Wen Z, Su Z. Automatic slub detection using Gabor filters [J]. International Journal of Clothing Science and Technology,2008,20(4):204-211.
5. Liu X, Wen Z, Su Z. Slub extraction in woven fabric images using Gabor filters [J]. Textile Research Journal,2009,78(4):320-325.
6. Furferi R, Governi L. Machine vision tool for real-time detection of defects on textile raw fabrics [J]. Textile Research Journal,2008;99(1):57-66.
7. Abouelela A, Abbas H M, Eldeeb H. Automatic vision system for localizing structural defects in textile fabrics[J]. Pattern Recognition Letters,2005,26:1435-1443.
8. Zhang Y H, Yuen C W M, Wong W K. An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network [J]. Textile Research Journal,2011;81(17):1772-1785.
9. Shady E, Gowayed Y, Abouiiana M. Detection and classi-fication of defect in knitted fabric structures [J]. Textile Research Journal,2006,76(4):295-300.
10. Chan C-H, Pang G K H. Fabric defect detection by Fourier analysis[J]. IEEE Transactions on Industry Application,2000,36(5):1267-1276.
11. Kuo C-F J, Hsu C-T M, Chen W H. Automatic detection system for printed fabric defects [J]. Textile Research Journal,2012,82(6):591-601.
12. Ghazi S R, Latifi M, Shaikhzadeh N S. Computer vision-aided fabric inspection system for on-circular knitting machine[J]. Textile Research Journal,2005,75(6):492-497.
13. Kuo C-F J, Lee C-J. A back-propagation neural network for recognizing fabric defects[J]. Textile Research Journal,2003,73(2):147-151.
14. Kuo C-F J, Lee C-J, Tsai C-C. Using a neural network to identify fabric defects in dynamic cloth inspection[J]. Textile Research Journal,2003,73(3):238-244.
15. Xu B G. Identifying Fabric Structures with Fast Fourier Transform Techniques [J]. Textile Research Journal,1996;66(8):496-506.
16. Pan R R, Gao W D, Liu J H. Automatic Detection of Structure Parameters of Yarn-dyed Fabric[J]. Textile Research Journal,2010;80(17):1819-1832.
17. Bel P D, Xu B G, Boykin D. Automatic detection of seed coat fragments in cotton fabrics [J]. Textile Research Journal,2011;82(16):1711-1719.
18. Xu B G, Yao Y, Bel P. High Volume Measurements of Cotton Maturity by a Customized Microscopic System.[J]. Textile Research Journal,2009;79(10):937-946.
19. W Yu, Yao M, Xu B.3-D surface reconstruction and evaluation and wrinkled fabrics by stereo vision[J]. Textile Research Journal,2009,79(1):36-46.
20. Li L Y, Zhu M J, Wei X J. Pilling Performance of Cashmere Knitted Fabric of Woollen Ring Yarn and Mule Yarn [J]. Fibres&Textiles in Eastern Europe,2014,22(1):74-75.
21. Behera B K, Mohan Madan T E. Objective measurement of pilling by image processing technique [J]. International Journal of Clothing Science and Technology,2006,17(5):279-291.
22. Yun S Y, Kim S, Park C K. Development of an objective fabric pilling evaluation method. II. Fabric pilling grading using artificial neural network [J]. Fibres and Polymers,2013,14(12):2157-2162.
23. Kim S C, Kang T J. Fabric surface roughness evaluation using wavelet-fractal method, Part II:Fabric pilling evaluation[J]. Textile Research Journal,2005,75(11):761-770.
24Yun S Y, Kim S, Park C K. Development of an objective fabric pilling evaluation method. I.Characterization of pilling using image analysis [J]. Fibres and Polymers,2013,2013,14(5):832-837.
25Kim S C, Kang T J. Image analysis of standard pilling photographs using wavelet reconstruction[J].Textile Research Journal,2005,75(12):801-811.
26Tabasum S, Zuber M, Jamil T. Antimicrobial and pilling evaluation of the modified cellulosic fabricsusing polyurethane acrylate copolymers [J]. International Journal of Biological Macromolecules,2013,56(5):99-105.
27Chen X, Huang X B. Evaluating fabric pilling with light-projected image analysis[J]. TextileResearch Journal,2004,74(11):977-981.
28. Xu B G, Lin S. Automatic Color Identification in Printed Fabric Images by A Fuzzy-Neural Network [J].AATCC Review,2002,2(9):42-45.
29. Kuo C-F J, Shih C Y, Lee J Y. Repeat pattern segmentation of printed fabrics by hough transformmethod [J]. Textile Research Journal,2005,75(11):779-783.
30Lachkar A, Benslimane R, D'Orazio L. A system for textile design patterns retrieval. PartI: Design patterns extraction by adaptive and efficient color image segmentation method [J]. TextileResearch Journal,2006,97(4):301-312.
31. Kuo C-F J, Su T L, Huang Y J. Computerized color separation system for printed fabrics by usingbackward-propagation neural network [J]. Textile Research Journal,2007,8(5):529-536.
32. Kuo C-F J, Shih C Y. Printed fabric computerized automatic color separating system [J]. TextileResearch Journal,2011,81(7):706-713.
33. Kuo C-F J, Hsu C-T M, Shih C Y. Automatic pattern recognition and color separation of embroideryfabrics [J]. Textile Research Journal,2011,81(11):1145-1157.
34. Kuo C-F J, Jian B L, Wu H C. Automatic machine embroidery image color analysis system. Part I:Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation [J]. Textile ResearchJournal,2012,82(6):571-583.
35. Jiang G M, Zhang D, Cong H L, etc. Automatic Identification of Jacquard Warp-knitted Fabric PatternsBased on the Wavelet Transform [J]. Fibres&Textiles in Eastern Europe,2014,22(2):53-56.
36.官伟波,王晋棠.针织物彩色花纹图像的自动分割[J].针织工业,2001(5):21-22.
37.官伟波.纬编提花织物花纹组织自动识别的研究[D]:[硕士学位论文].无锡:江南大学,2002.
38.薛卫,高卫东,刘基宏.条格织物组织识别中的图像分割方法[J].棉纺织技术,2003,31(6):12-15.
39.周平,汪亚明,赵匀.一种基于自适应颜色压缩的织物图案提取方法[J].计算机工程与应用,2004(16):217-220.
40.周平,汪亚明,赵匀.基于分量组合及其位压缩的纺织图案提取方法[J].纺织学报,2005,26(5):11-13.
41.冯志林.拓扑纹理图像的关键预处理技术研究[D]:[博士学位论文].杭州:浙江大学,2005.
42.吴海虹.织物组织识别系统中关键技术的研究[D]:[博士学位论文].杭州:浙江大学,2006.
43.徐敏.织物图像的分割算法研究[D]:[硕士学位论文].杭州:浙江大学,2007.
44.诸葛振荣,徐敏,刘洋飞.基于Mean Shift的织物图像分割算法[J].纺织学报,2007,28(10):108-111.
45.杨志芳.提花割圈系统中花型识别织物仿真的研究[D]:[硕士学位论文].南昌:南昌航空大学,2008.
46.刘建立,左保齐.基于遗传算法的织物印花图案的分割[J].计算机工程与设计,2008,29(15):3966-3971.
47.刘桂芬.机织物提花图案分割与拼接方法的研究[D]:[硕士学位论文].天津:天津工业大学,2009.
48.郝保明.基于半监督聚类的织物图像分割算法研究[D]:[硕士学位论文].杭州:浙江理工大学,2009.
49.张荣华,潘如如,刘基宏.基于FCM的针织物花纹自动分割[J].针织工业,2010(1):12-14.
50.张丹,蒋高明,丛洪莲.带地网贾卡经编针织物花纹图案的自动识别[J].纺织学报,2010,31(10):45-49.
51.张丹,蒋高明,丛洪莲.基于小波变换的贾卡经编织物多区域自动识别[J].纺织学报,2011,32(9):136-140.
52.张丹.贾卡经编针织物花纹图案自动识别[D]:[硕士学位论文].无锡:江南大学,2011.
53.李鹏飞,龙观水,景军锋. EM算法在纹理织物图像分割中的应用[J].西安工程大学学报,2012.26(2):195-198.
54.韩永华,刘成霞,汪亚明.基于小波分解系数的织物图像分类分割[J].纺织学报,2012,33(11):57-60.
55.张宝山. CAD技术在面料图案设计中的应用[J].纺织导报,2010(3):100-101.
1.蒋高明.经编针织物生产技术——经编理论与典型产品[M].北京：中国纺织出版社，2010：284-290.
2.丛洪莲.贾卡经编针织物结构与仿真研究[D]:[博士学位论文].无锡:江南大学,2009.
3.钱浩,蒋高明.成圈型贾卡经编针织物仿真探讨[J].上海纺织科技.2006(11):101-103.
4.王伟伟,蒋高明,丛洪莲.压纱型贾卡经编织物的计算机仿真[J].国际纺织导报,2009(6):31-33.
5.张巧丽,蒋高明,丛洪莲.经编浮纹织物的工艺分析与设计[J].针织工业,2009(8):20-22.
6.张姿叶,蒋高明,丛洪莲.浮纹型贾卡经编针织物的计算机仿真[J].纺织学报,2011(32):22-24.
7.张巧丽,蒋高明,丛洪莲.浮纹型贾卡经编针织物的工艺设计与仿真[J].国际纺织导报,2009(3):38-41.
8.宋广礼,蒋高明.针织物组织与产品设计[M].北京:中国纺织出版社，2008:290-300.
9.程龙,蒋高明.经编浮纹贾卡提花原理[J].纺织学报,2014(35):40-45.
10. Hawkins J K. Texture properties for pattern recognition[M]. Academic Press,1969:88-90.
11. Cross G, Jain A. Markov random field texture models[J]. IEEE Transaction on System,1983(1):25-39.
12. Bovik A C, Clark M, Geisler W S. Multichannel texture analysis using localized spatial filters[J]. IEEETransaction on System,1990(12):55-73.
13. Muerle J L. Some thoughts on texture discrimination by computer[M]. Academic Press,1970:120-125.
14. Haralick R M, Shanxnugam K, Dinstein I. Texture feature for image classification[J]. IEEE Transactionon System,1973(SMC-3):610-621.
15Lin H C, Wang L L, Yang S N. Automatic determination of the spread parameter in Gaussian[J]. PatternRecognition Letters smoothing,1996(17):1247-1252.
16Krystek M. Gauss filtering algorithm for roughness measurements[J]. Prec Eng,1996(19):198-200.
17Sovira T, Jason L D, Alan J. Performance of three recursive algorithms for fast space-variant Gaussianfiltering[J]. Real-Time Imaging,2003(9):215-228.
18Steve R G. On the Discrete Representation of the Laplacian of Gaussian[J]. Pattern Recognition,1999(32):1463~1472.
19Li X P, Chen T W. Efficient Synthesis of parameterized Gaussian-like filters by approximation.Signal Processing[J].1995(41):119-134.
20林茂六,刘治宇.高速宽带取样——滤波数字化系统中高斯滤波器的设计[J].仪器仪表学报，2002(2):121-123.
21Ferrari L A, Sankar P V. Recursive Algorithms for implementing digital image filters[J]. IEEETransactions on Pattern Analysis,1987,9(3):461-466.
22Panda R, Chatterji B N. Least squares generalized B-spline signal and image processing[J]. Signalprocessing,2001(81):2005-2017.
23Hardie R C, Barner K E. Rank conditioned rank selection filters for signal restoration[J]. IEEETransactions on Image Processing,1994,3(2):192-206.
24曾文涵,高咏生,谢铁邦.三维表面粗糙度高斯滤波快速算法[J].计量学报,2003,24(1):10-13.
25许景波.高斯滤波器逼近理论与应用研究[D]:[博士学位论文].哈尔滨:哈尔滨工业大学,2009.
26Kindermann S, Osher S, Jones P W. Deblurring and denoising of image by nonlocal functional[J].Multiscale Modeling and Simulation,2005,4(4):1091-1115.
27Mahmoudi M. Fast image and video denoising via nonlocal means of similar neighbourhoods[J]. IEEEsignal processing letters,2005(12):839-841.
28Zhang M, Gunturk B K. Multiresolution bilateral filter for image denoising [J]. IEEE TransactionsImage Processing,2008,17(12):2324-2333.
29Sand P, Teller S. Long-range motion estimation using point trajectories[J]. International Journal ofComputer Vision,2008,80(1):72-91.
30Zhang M, Gunturk B K. Compression artifact reduction with adaptive bilateral filtering[J]. SPIEElectronic Image,2009(6),257-261.
31Zheng Y Y, Fu H B. Bilateral normal filtering for mesh denoising[J]. IEEE Transactions onVisualization and Computer Graphics,2011,17(10):1521-1530.
32Ramanath R, Snyder W E. Adaptive demosaicking[J]. Journal of Electronic Imaging,2003,12(4):633-642.
33Hung K W. Fast image interpolation suing the bilateral filter[J]. Image Processing,2012,6(7):877-890.
34李俊峰.双边滤波算法的快速实现及其在图像处理的应用[D]:[硕士学位论文].广州:南方医科大学,2013.
35王玉灵.基于双边滤波的图像处理算法研究[D]:[硕士学位论文].西安:西安电子科技大学,2010.
1Florian L, Thierry B, Michael U. A new SURE approach to image denoising: Interscale orthonormalwavelet thresholding[J]. IEEE Transactions on Image Processing,2007,16(3):593-606.
2Galford G L, Mustard J F, Melillo J. Wavelet analysis of MODIS time series to detect expansion andintensification of row-crop agriculture in Brazil[J]. Remote Sensing of Environment,2008,112(2):576-587.
3Garófano G J R, Venancio C G, Suazo C A T. Application of the wavelet image analysis technique tomonitor cell concentration in bioprocesses[J]. Brazilian Journal of Chemical Engineering,2005,22(4):573-583.
4Lee S H, Lim J S. Minimum Feature Selection for Epileptic Seizure Classification using Wavelet-basedFeature Extraction and a Fuzzy Neural Network[J]. Applied Mathematics&Information Sciences,2014,8(3):1295-1300.
5Abdulhamit S. EEG signal classification using wavelet feature extraction and a mixture of expertmodel[J]. Expert Systems with Applications,2007,32(4):1084-1093.
6Jordi C, Luis R, Juan O. Fault detection in induction machines using power spectral density in waveletdecomposition[J]. IEEE Transactions on Industrial Electronics,2008,55(2):633-643.Mario F, Jose B D, Robert N. Majorization-minimization algorithms for wavelet-based image restoration[J].IEEE Transactions on Image Processing,2007,16(12):2980-2991.
7He L, Carin L. Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing[J]. IEEETransactions on Signal Processing,2009,57(9):3488-3497.
8Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform andapproximate entropy[J]. Expert Systems with Applications,2009,36(2):2027-2036.
9Li Y Z, Jia H F. The construction of multivariate periodic wavelet bi-frames[J]. Journal of MathematicalAnalysis and Applications,2014,412(2):852-865.
10Chen Q, Cheng Z. A study on compactly supported orthogonal vector-valued wavelets and waveletpackets[J]. Chaos Solitons&Fractals,2007,31(4):1024-1034.
11Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques-An introduction, review andcomparison[J]. Isprs Journal of Photogrammetry and Remote Sensing,2007,62(4):249-263.
12JIMéNEZ R F, TORRES P, GüNTHER B. Wavelet and Fourier analysis of ventricular and mainarteries pulsations in anesthetized dogs[J]. Biological Research,2004,37(3):431-444.
13Zhao J, Liu T, Liu S. Identification of space-dependent permeability in nonlinear diffusion equationfrom interior measurements using wavelet multiscale method[J]. Inverse Problems in Science andEngineering,2014,22(4):507-529.
14Qiu J D, Huang J H, Shi S P. Using the Concept of Chou's Pseudo Amino Acid Composition to PredictEnzyme Family Classes: An Approach with Support Vector Machine Based on Discrete WaveletTransform[J]. Protein and Peptide Letters,2010,17(6):715-722.
15Ray S, Sahu P K. Application of Semiorthogonal B-Spline Wavelets for the Solutions of Linear SecondKind Fredholm Integral Equations[J]. Applied Mathematics&Information Sciences,2014,8(3):1179-1184.
16Ding W, Wu F, Wu X. Adaptive directional lifting-based wavelet transform for image coding[J]. IEEETransactions on Image Processing,2007,16(2):416-427.
17Cahng T, Kuo C C J. Texture analysis and classification with tree structured wavelet transform[J]. IEEETransaction on image processing,1993,2(4):429-441.
18徐孟春,王相海.基于不完全小波树型结构的图像纹理特征研究[J].中国图象图形学报,2009,14(7):1341-1346.
1Vesanto J, Alhoniemi E. Clustering of the self-organizing map[J]. IEEE Transactions on NeuralNetworks,2000,11(3):586-600.
2Kanungo T, Mount D M, Netanyahu N S. An efficient k-means clustering algorithm: Analysis andimplementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
3Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic[J].Journal of The Royal Statistical Society Series B-statistical Methodology,2001,63(2):411-423.
4Jain Anil K. Data clustering:50years beyond K-means[J]. Pattern Recognition Letters,2010,31(8):651-666.
5Fred ALN, Jain A K. Combining multiple clusterings using evidence accumulation[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2005,27(6):835-850.
6Yuan C, Xiong Z L, Zhou X H. Study of Infrared Image Edge Detection Based on Sobel Operator[J].Laser and Infrared,2009,39(1):85-87.
7付光远.一种基于Sobel分解算子的图像边缘检测并行算法[J].微电子学与计算机,2006,23(9):132-134.
8Zhang Q, Yeo T S, Tan H S. Imaging of a moving target with rotating parts based on the Houghtransform[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(1):291-299.
9Shi J B, Malik J. Normalized cuts and image segmentation[J]. Transactions on Pattern Analysis andMachine Intelligence,2000,22(8):888-905.
10Zhang Y Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov randomfield model and the expectation-maximization algorithm[J]. IEEE Transactions on Medical Imaging,2001,20(1):45-57.
11Likas A, Vlassis N, Verbeek J J. The global k-means clustering algorithm[J]. Pattern Recognition,2003,36(2):451-461.
12Kanungo T, Mount D M, Netanyahu N S. An efficient k-means clustering algorithm: Analysis andimplementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
13Huang Z X. Extensions to the k-means algorithm for clustering large data sets with categorical values[J].Data Mining and Knowledge Discovery,1998,2(3):283-304.
14Pena J M, Lozano J A, Larranaga P. An empirical comparison of four initialization methods for theK-Means algorithm[J]. Pattern Recognition Letters,1999,20(10):1027-1040.
15Su M S, Chou C H. A modified version of the K-means algorithm with a distance based on clustersymmetry[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):674-680.
16Huang JZX, Ng MK, Rong HQ. Automated variable weighting in k-means type clustering[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2005,27(5):657-668.
1Li J, Gray R M, Olshen R A. Multiresolution image classification by hierarchical modeling withtwo-dimensional hidden Markov models [J]. IEEE Trans Inf Theory,2000,46(5):1826–1841.
2Emmanouil A, Dionisis C, Spyros K. A wavelet-based Markov random field segmentation model insegmenting m icroarray experiments [J]. Comput Meth Prog Bio,2011,3:307–315.
3Kim T H, Eom I K, Kim Y S. Multiscale Bayesian texture segmentation using neural networks andMarkov random fields [J]. Neural Comput Appl,2009,18:141–155.
4Zhang Y H, Zhang Y S, He Z F. Multiscale fusion of wavelet-domain hidden Markov tree through graphcut [J]. Image Vision Comput,2009,29(3):1402–1410.
5Dusan G, Mihai D. Gauss-Markov model for wavelet-based SAR image despeckling [J]. IEEE SignalProc Let,2006,6:365-368.
6Romberg J K, Choi H, Baraniuk R G. Bayesian tree-structured image modeling using wavelet-domainhidden Markov models [J]. IEEE Image Process,2011,7:1056-1068.
7Fan G and Xia XG. Wavelet-based texture analysis and synthesis using hidden Markov models [J]. IEEETrans Circuits Syst Fundam Theory Appl,2003,50(1):106–120.
8Zhang Y H, He Z Y, Zhang Y S. Global optimization of wavelet-domain hidden Markovtree for imagesegmentation [J]. Pattern Recogn,2011,44:2811-2818.
9Cehux G, Forbes F, Peyrard N. EM procedures Using mean field-like approximations for Markovmodel-based image segmentation [J]. Pattern Recogn,2003,36:131-143.
10Chen M, Strobl J. Multispectral textured image segmentation using a multi-resolution fuzzy Markovrandom field model on variable scales in the wavelet domain[J]. International Journal of Remote Sensing,2013,34(13):4550-4569.
11Zheng C, Wang L G, Chen R Y. Image Segmentation Using Multiregion-Resolution MRF Model [J].IEEE Geoscience and Remote Sensing Letters,2013,10(4):816-820.
12Sun J, Zhu H Y, Xu Z B. Poisson image fusion based on Markov random field fusion model [J].Information Fusion,2013,14(3):241-254.
13Osadebey M, Bouguila N, Arnold D. The Clique Potential of Markov Random Field in a RandomExperiment for Estimation of Noise Levels in2D Brain MRI [J]. International Journal of Imaging Systemsand Technology,2013,23(4):304-313.
14Chen Y S, Amini A A. A MAP framework for tag line detection in SPAMM data using Markov randomfields on the B-spline solid [J]. IEEE Transactions on Medical Imaging,2002,21(9):1110-1122.
15Van L K, Maes F, Vandermeulen D. Automated segmentation of multiple sclerosis lesions by modeloutlier detection [J]. IEEE Transactions on Medical Imaging,2001,20(8):677-688.
16Kersten J, Gaehler M, Voigt S. A General Framework for Fast and Interactive Classification of OpticalVHR Satellite Imagery Using Hierarchical and Planar Markov Random Fields [J]. PhotogrammetrieFernerkundung Geoinformation,2010,6:439-449.
17Nowozin S, Lampert C H. Global Interactions in Random Field Models: A Potential Function EnsuringConnectedness [J]. Siam Journal on Imaging Sciences,2010,3(4):1048-1074.
18Ashraf A B, Gavenonis S C, Daye D. A Multichannel Markov Random Field Framework for TumorSegmentation With an Application to Classification of Gene Expression-Based Breast Cancer RecurrenceRisk [J]. IEEE Transactions on Medical Imaging,2013,32(4):637-648.
19Costa J P D, Galland F, Roueff A, Germain C. Unsupervised segmentation based on Von Misescircular distributions for orientation estimation in textured images [J]. Journal of Electronic Imaging,2012,21(2):021102-1-021102-7.
20Lehman F. Turbo segmentation of textured images [J]. IEEE T PATTERN ANAL,2011,33(1):16-29.
21Yang L Y, Wang X Y, Wang Q Y, Zhang X J. LS-SVM based image segmentation using color andtexture information [J]. J Vis Commun Image R,2012,23:1095–1112.
22Yu Q Y, Clausi D A. Image Segmentation Using Edge Penalties and Region Growing [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2008,30(12):2126-2139.
23Derpanis K G, Wildes R P, Tsotsos J K. Definition and recovery of kinematic features for recognition ofAmerican sign language movements [J]. Image and Vision Computing,2008,26(12):1650-1662.
24Le C S, Salzenstein F, Collet C. Fuzzy pairwise Markov chain to segment correlated noisy data [J].Signal Processing,2008,88(10):2526-2541.
25Chen M, Wu W, Yang X M. Hidden-Markov-Model-Based Segmentation Confidence Applied toContainer Code Character Extraction [J]. IEEE Transactions on Intelligent Transportation Systems,2011,
12(4):1147-1156.
26Monaco J P, Madabhushi A. Class-specific weighting for Markov random field estimation: Applicationto medical image segmentation [J]. Medical Image Analysis,2012,16(8):1477-1489.
27Yang F G, Jiang T Z. Pixon-based image segmentation with Markov random fields [J]. IEEETransactions on Image Processing,2003,12(12):1552-1559.
28Xu M, Chen H, Varshney P K. An Image Fusion Approach Based on Markov Random Fields [J]. IEEETransactions on Geoscience and Remote Sensing,2011,49(12):5116-5127.
29Bratsolis E, Sigelle M. Fast SAR image restoration, segmentation, and detection of high-reflectanceregions [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2890-2899.
30Yang Q J, Liang J M, Hu Z J. Auroral Sequence Representation and Classification Using HiddenMarkov Models [J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(12):5049-5060.
2. Wang R W, Wu X Y, Wang S Y. Automatic identification of ramie and cotton fibers using characteristics in longitudinal view, Part Ⅱ:Fiber stripes analysis [J]. Textile Research Journal,2009,79(17):1547-1556.
3. Wang R W, Yan J L, Wu X Y. Automatic Identification of Lyocell and Cotton Fibers Using Cluster Analysis [J]. Textile Research Journal,2010,80(13):1330-1334.
4. Liu X, Wen Z, Su Z. Automatic slub detection using Gabor filters [J]. International Journal of Clothing Science and Technology,2008,20(4):204-211.
5. Liu X, Wen Z, Su Z. Slub extraction in woven fabric images using Gabor filters [J]. Textile Research Journal,2009,78(4):320-325.
6. Furferi R, Governi L. Machine vision tool for real-time detection of defects on textile raw fabrics [J]. Textile Research Journal,2008;99(1):57-66.
7. Abouelela A, Abbas H M, Eldeeb H. Automatic vision system for localizing structural defects in textile fabrics[J]. Pattern Recognition Letters,2005,26:1435-1443.
8. Zhang Y H, Yuen C W M, Wong W K. An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network [J]. Textile Research Journal,2011;81(17):1772-1785.
9. Shady E, Gowayed Y, Abouiiana M. Detection and classi-fication of defect in knitted fabric structures [J]. Textile Research Journal,2006,76(4):295-300.
10. Chan C-H, Pang G K H. Fabric defect detection by Fourier analysis[J]. IEEE Transactions on Industry Application,2000,36(5):1267-1276.
11. Kuo C-F J, Hsu C-T M, Chen W H. Automatic detection system for printed fabric defects [J]. Textile Research Journal,2012,82(6):591-601.
12. Ghazi S R, Latifi M, Shaikhzadeh N S. Computer vision-aided fabric inspection system for on-circular knitting machine[J]. Textile Research Journal,2005,75(6):492-497.
13. Kuo C-F J, Lee C-J. A back-propagation neural network for recognizing fabric defects[J]. Textile Research Journal,2003,73(2):147-151.
14. Kuo C-F J, Lee C-J, Tsai C-C. Using a neural network to identify fabric defects in dynamic cloth inspection[J]. Textile Research Journal,2003,73(3):238-244.
15. Xu B G. Identifying Fabric Structures with Fast Fourier Transform Techniques [J]. Textile Research Journal,1996;66(8):496-506.
16. Pan R R, Gao W D, Liu J H. Automatic Detection of Structure Parameters of Yarn-dyed Fabric[J]. Textile Research Journal,2010;80(17):1819-1832.
17. Bel P D, Xu B G, Boykin D. Automatic detection of seed coat fragments in cotton fabrics [J]. Textile Research Journal,2011;82(16):1711-1719.
18. Xu B G, Yao Y, Bel P. High Volume Measurements of Cotton Maturity by a Customized Microscopic System.[J]. Textile Research Journal,2009;79(10):937-946.
19. W Yu, Yao M, Xu B.3-D surface reconstruction and evaluation and wrinkled fabrics by stereo vision[J]. Textile Research Journal,2009,79(1):36-46.
20. Li L Y, Zhu M J, Wei X J. Pilling Performance of Cashmere Knitted Fabric of Woollen Ring Yarn and Mule Yarn [J]. Fibres&Textiles in Eastern Europe,2014,22(1):74-75.
21. Behera B K, Mohan Madan T E. Objective measurement of pilling by image processing technique [J]. International Journal of Clothing Science and Technology,2006,17(5):279-291.
22. Yun S Y, Kim S, Park C K. Development of an objective fabric pilling evaluation method. II. Fabric pilling grading using artificial neural network [J]. Fibres and Polymers,2013,14(12):2157-2162.
23. Kim S C, Kang T J. Fabric surface roughness evaluation using wavelet-fractal method, Part II:Fabric pilling evaluation[J]. Textile Research Journal,2005,75(11):761-770.
24Yun S Y, Kim S, Park C K. Development of an objective fabric pilling evaluation method. I.Characterization of pilling using image analysis [J]. Fibres and Polymers,2013,2013,14(5):832-837.
25Kim S C, Kang T J. Image analysis of standard pilling photographs using wavelet reconstruction[J].Textile Research Journal,2005,75(12):801-811.
26Tabasum S, Zuber M, Jamil T. Antimicrobial and pilling evaluation of the modified cellulosic fabricsusing polyurethane acrylate copolymers [J]. International Journal of Biological Macromolecules,2013,56(5):99-105.
27Chen X, Huang X B. Evaluating fabric pilling with light-projected image analysis[J]. TextileResearch Journal,2004,74(11):977-981.
28. Xu B G, Lin S. Automatic Color Identification in Printed Fabric Images by A Fuzzy-Neural Network [J].AATCC Review,2002,2(9):42-45.
29. Kuo C-F J, Shih C Y, Lee J Y. Repeat pattern segmentation of printed fabrics by hough transformmethod [J]. Textile Research Journal,2005,75(11):779-783.
30Lachkar A, Benslimane R, D'Orazio L. A system for textile design patterns retrieval. PartI: Design patterns extraction by adaptive and efficient color image segmentation method [J]. TextileResearch Journal,2006,97(4):301-312.
31. Kuo C-F J, Su T L, Huang Y J. Computerized color separation system for printed fabrics by usingbackward-propagation neural network [J]. Textile Research Journal,2007,8(5):529-536.
32. Kuo C-F J, Shih C Y. Printed fabric computerized automatic color separating system [J]. TextileResearch Journal,2011,81(7):706-713.
33. Kuo C-F J, Hsu C-T M, Shih C Y. Automatic pattern recognition and color separation of embroideryfabrics [J]. Textile Research Journal,2011,81(11):1145-1157.
34. Kuo C-F J, Jian B L, Wu H C. Automatic machine embroidery image color analysis system. Part I:Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation [J]. Textile ResearchJournal,2012,82(6):571-583.
35. Jiang G M, Zhang D, Cong H L, etc. Automatic Identification of Jacquard Warp-knitted Fabric PatternsBased on the Wavelet Transform [J]. Fibres&Textiles in Eastern Europe,2014,22(2):53-56.
36.官伟波,王晋棠.针织物彩色花纹图像的自动分割[J].针织工业,2001(5):21-22.
37.官伟波.纬编提花织物花纹组织自动识别的研究[D]:[硕士学位论文].无锡:江南大学,2002.
38.薛卫,高卫东,刘基宏.条格织物组织识别中的图像分割方法[J].棉纺织技术,2003,31(6):12-15.
39.周平,汪亚明,赵匀.一种基于自适应颜色压缩的织物图案提取方法[J].计算机工程与应用,2004(16):217-220.
40.周平,汪亚明,赵匀.基于分量组合及其位压缩的纺织图案提取方法[J].纺织学报,2005,26(5):11-13.
41.冯志林.拓扑纹理图像的关键预处理技术研究[D]:[博士学位论文].杭州:浙江大学,2005.
42.吴海虹.织物组织识别系统中关键技术的研究[D]:[博士学位论文].杭州:浙江大学,2006.
43.徐敏.织物图像的分割算法研究[D]:[硕士学位论文].杭州:浙江大学,2007.
44.诸葛振荣,徐敏,刘洋飞.基于Mean Shift的织物图像分割算法[J].纺织学报,2007,28(10):108-111.
45.杨志芳.提花割圈系统中花型识别织物仿真的研究[D]:[硕士学位论文].南昌:南昌航空大学,2008.
46.刘建立,左保齐.基于遗传算法的织物印花图案的分割[J].计算机工程与设计,2008,29(15):3966-3971.
47.刘桂芬.机织物提花图案分割与拼接方法的研究[D]:[硕士学位论文].天津:天津工业大学,2009.
48.郝保明.基于半监督聚类的织物图像分割算法研究[D]:[硕士学位论文].杭州:浙江理工大学,2009.
49.张荣华,潘如如,刘基宏.基于FCM的针织物花纹自动分割[J].针织工业,2010(1):12-14.
50.张丹,蒋高明,丛洪莲.带地网贾卡经编针织物花纹图案的自动识别[J].纺织学报,2010,31(10):45-49.
51.张丹,蒋高明,丛洪莲.基于小波变换的贾卡经编织物多区域自动识别[J].纺织学报,2011,32(9):136-140.
52.张丹.贾卡经编针织物花纹图案自动识别[D]:[硕士学位论文].无锡:江南大学,2011.
53.李鹏飞,龙观水,景军锋. EM算法在纹理织物图像分割中的应用[J].西安工程大学学报,2012.26(2):195-198.
54.韩永华,刘成霞,汪亚明.基于小波分解系数的织物图像分类分割[J].纺织学报,2012,33(11):57-60.
55.张宝山. CAD技术在面料图案设计中的应用[J].纺织导报,2010(3):100-101.
1.蒋高明.经编针织物生产技术——经编理论与典型产品[M].北京：中国纺织出版社，2010：284-290.
2.丛洪莲.贾卡经编针织物结构与仿真研究[D]:[博士学位论文].无锡:江南大学,2009.
3.钱浩,蒋高明.成圈型贾卡经编针织物仿真探讨[J].上海纺织科技.2006(11):101-103.
4.王伟伟,蒋高明,丛洪莲.压纱型贾卡经编织物的计算机仿真[J].国际纺织导报,2009(6):31-33.
5.张巧丽,蒋高明,丛洪莲.经编浮纹织物的工艺分析与设计[J].针织工业,2009(8):20-22.
6.张姿叶,蒋高明,丛洪莲.浮纹型贾卡经编针织物的计算机仿真[J].纺织学报,2011(32):22-24.
7.张巧丽,蒋高明,丛洪莲.浮纹型贾卡经编针织物的工艺设计与仿真[J].国际纺织导报,2009(3):38-41.
8.宋广礼,蒋高明.针织物组织与产品设计[M].北京:中国纺织出版社，2008:290-300.
9.程龙,蒋高明.经编浮纹贾卡提花原理[J].纺织学报,2014(35):40-45.
10. Hawkins J K. Texture properties for pattern recognition[M]. Academic Press,1969:88-90.
11. Cross G, Jain A. Markov random field texture models[J]. IEEE Transaction on System,1983(1):25-39.
12. Bovik A C, Clark M, Geisler W S. Multichannel texture analysis using localized spatial filters[J]. IEEETransaction on System,1990(12):55-73.
13. Muerle J L. Some thoughts on texture discrimination by computer[M]. Academic Press,1970:120-125.
14. Haralick R M, Shanxnugam K, Dinstein I. Texture feature for image classification[J]. IEEE Transactionon System,1973(SMC-3):610-621.
15Lin H C, Wang L L, Yang S N. Automatic determination of the spread parameter in Gaussian[J]. PatternRecognition Letters smoothing,1996(17):1247-1252.
16Krystek M. Gauss filtering algorithm for roughness measurements[J]. Prec Eng,1996(19):198-200.
17Sovira T, Jason L D, Alan J. Performance of three recursive algorithms for fast space-variant Gaussianfiltering[J]. Real-Time Imaging,2003(9):215-228.
18Steve R G. On the Discrete Representation of the Laplacian of Gaussian[J]. Pattern Recognition,1999(32):1463~1472.
19Li X P, Chen T W. Efficient Synthesis of parameterized Gaussian-like filters by approximation.Signal Processing[J].1995(41):119-134.
20林茂六,刘治宇.高速宽带取样——滤波数字化系统中高斯滤波器的设计[J].仪器仪表学报，2002(2):121-123.
21Ferrari L A, Sankar P V. Recursive Algorithms for implementing digital image filters[J]. IEEETransactions on Pattern Analysis,1987,9(3):461-466.
22Panda R, Chatterji B N. Least squares generalized B-spline signal and image processing[J]. Signalprocessing,2001(81):2005-2017.
23Hardie R C, Barner K E. Rank conditioned rank selection filters for signal restoration[J]. IEEETransactions on Image Processing,1994,3(2):192-206.
24曾文涵,高咏生,谢铁邦.三维表面粗糙度高斯滤波快速算法[J].计量学报,2003,24(1):10-13.
25许景波.高斯滤波器逼近理论与应用研究[D]:[博士学位论文].哈尔滨:哈尔滨工业大学,2009.
26Kindermann S, Osher S, Jones P W. Deblurring and denoising of image by nonlocal functional[J].Multiscale Modeling and Simulation,2005,4(4):1091-1115.
27Mahmoudi M. Fast image and video denoising via nonlocal means of similar neighbourhoods[J]. IEEEsignal processing letters,2005(12):839-841.
28Zhang M, Gunturk B K. Multiresolution bilateral filter for image denoising [J]. IEEE TransactionsImage Processing,2008,17(12):2324-2333.
29Sand P, Teller S. Long-range motion estimation using point trajectories[J]. International Journal ofComputer Vision,2008,80(1):72-91.
30Zhang M, Gunturk B K. Compression artifact reduction with adaptive bilateral filtering[J]. SPIEElectronic Image,2009(6),257-261.
31Zheng Y Y, Fu H B. Bilateral normal filtering for mesh denoising[J]. IEEE Transactions onVisualization and Computer Graphics,2011,17(10):1521-1530.
32Ramanath R, Snyder W E. Adaptive demosaicking[J]. Journal of Electronic Imaging,2003,12(4):633-642.
33Hung K W. Fast image interpolation suing the bilateral filter[J]. Image Processing,2012,6(7):877-890.
34李俊峰.双边滤波算法的快速实现及其在图像处理的应用[D]:[硕士学位论文].广州:南方医科大学,2013.
35王玉灵.基于双边滤波的图像处理算法研究[D]:[硕士学位论文].西安:西安电子科技大学,2010.
1Florian L, Thierry B, Michael U. A new SURE approach to image denoising: Interscale orthonormalwavelet thresholding[J]. IEEE Transactions on Image Processing,2007,16(3):593-606.
2Galford G L, Mustard J F, Melillo J. Wavelet analysis of MODIS time series to detect expansion andintensification of row-crop agriculture in Brazil[J]. Remote Sensing of Environment,2008,112(2):576-587.
3Garófano G J R, Venancio C G, Suazo C A T. Application of the wavelet image analysis technique tomonitor cell concentration in bioprocesses[J]. Brazilian Journal of Chemical Engineering,2005,22(4):573-583.
4Lee S H, Lim J S. Minimum Feature Selection for Epileptic Seizure Classification using Wavelet-basedFeature Extraction and a Fuzzy Neural Network[J]. Applied Mathematics&Information Sciences,2014,8(3):1295-1300.
5Abdulhamit S. EEG signal classification using wavelet feature extraction and a mixture of expertmodel[J]. Expert Systems with Applications,2007,32(4):1084-1093.
6Jordi C, Luis R, Juan O. Fault detection in induction machines using power spectral density in waveletdecomposition[J]. IEEE Transactions on Industrial Electronics,2008,55(2):633-643.Mario F, Jose B D, Robert N. Majorization-minimization algorithms for wavelet-based image restoration[J].IEEE Transactions on Image Processing,2007,16(12):2980-2991.
7He L, Carin L. Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing[J]. IEEETransactions on Signal Processing,2009,57(9):3488-3497.
8Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform andapproximate entropy[J]. Expert Systems with Applications,2009,36(2):2027-2036.
9Li Y Z, Jia H F. The construction of multivariate periodic wavelet bi-frames[J]. Journal of MathematicalAnalysis and Applications,2014,412(2):852-865.
10Chen Q, Cheng Z. A study on compactly supported orthogonal vector-valued wavelets and waveletpackets[J]. Chaos Solitons&Fractals,2007,31(4):1024-1034.
11Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques-An introduction, review andcomparison[J]. Isprs Journal of Photogrammetry and Remote Sensing,2007,62(4):249-263.
12JIMéNEZ R F, TORRES P, GüNTHER B. Wavelet and Fourier analysis of ventricular and mainarteries pulsations in anesthetized dogs[J]. Biological Research,2004,37(3):431-444.
13Zhao J, Liu T, Liu S. Identification of space-dependent permeability in nonlinear diffusion equationfrom interior measurements using wavelet multiscale method[J]. Inverse Problems in Science andEngineering,2014,22(4):507-529.
14Qiu J D, Huang J H, Shi S P. Using the Concept of Chou's Pseudo Amino Acid Composition to PredictEnzyme Family Classes: An Approach with Support Vector Machine Based on Discrete WaveletTransform[J]. Protein and Peptide Letters,2010,17(6):715-722.
15Ray S, Sahu P K. Application of Semiorthogonal B-Spline Wavelets for the Solutions of Linear SecondKind Fredholm Integral Equations[J]. Applied Mathematics&Information Sciences,2014,8(3):1179-1184.
16Ding W, Wu F, Wu X. Adaptive directional lifting-based wavelet transform for image coding[J]. IEEETransactions on Image Processing,2007,16(2):416-427.
17Cahng T, Kuo C C J. Texture analysis and classification with tree structured wavelet transform[J]. IEEETransaction on image processing,1993,2(4):429-441.
18徐孟春,王相海.基于不完全小波树型结构的图像纹理特征研究[J].中国图象图形学报,2009,14(7):1341-1346.
1Vesanto J, Alhoniemi E. Clustering of the self-organizing map[J]. IEEE Transactions on NeuralNetworks,2000,11(3):586-600.
2Kanungo T, Mount D M, Netanyahu N S. An efficient k-means clustering algorithm: Analysis andimplementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
3Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic[J].Journal of The Royal Statistical Society Series B-statistical Methodology,2001,63(2):411-423.
4Jain Anil K. Data clustering:50years beyond K-means[J]. Pattern Recognition Letters,2010,31(8):651-666.
5Fred ALN, Jain A K. Combining multiple clusterings using evidence accumulation[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2005,27(6):835-850.
6Yuan C, Xiong Z L, Zhou X H. Study of Infrared Image Edge Detection Based on Sobel Operator[J].Laser and Infrared,2009,39(1):85-87.
7付光远.一种基于Sobel分解算子的图像边缘检测并行算法[J].微电子学与计算机,2006,23(9):132-134.
8Zhang Q, Yeo T S, Tan H S. Imaging of a moving target with rotating parts based on the Houghtransform[J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(1):291-299.
9Shi J B, Malik J. Normalized cuts and image segmentation[J]. Transactions on Pattern Analysis andMachine Intelligence,2000,22(8):888-905.
10Zhang Y Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov randomfield model and the expectation-maximization algorithm[J]. IEEE Transactions on Medical Imaging,2001,20(1):45-57.
11Likas A, Vlassis N, Verbeek J J. The global k-means clustering algorithm[J]. Pattern Recognition,2003,36(2):451-461.
12Kanungo T, Mount D M, Netanyahu N S. An efficient k-means clustering algorithm: Analysis andimplementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
13Huang Z X. Extensions to the k-means algorithm for clustering large data sets with categorical values[J].Data Mining and Knowledge Discovery,1998,2(3):283-304.
14Pena J M, Lozano J A, Larranaga P. An empirical comparison of four initialization methods for theK-Means algorithm[J]. Pattern Recognition Letters,1999,20(10):1027-1040.
15Su M S, Chou C H. A modified version of the K-means algorithm with a distance based on clustersymmetry[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):674-680.
16Huang JZX, Ng MK, Rong HQ. Automated variable weighting in k-means type clustering[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2005,27(5):657-668.
1Li J, Gray R M, Olshen R A. Multiresolution image classification by hierarchical modeling withtwo-dimensional hidden Markov models [J]. IEEE Trans Inf Theory,2000,46(5):1826–1841.
2Emmanouil A, Dionisis C, Spyros K. A wavelet-based Markov random field segmentation model insegmenting m icroarray experiments [J]. Comput Meth Prog Bio,2011,3:307–315.
3Kim T H, Eom I K, Kim Y S. Multiscale Bayesian texture segmentation using neural networks andMarkov random fields [J]. Neural Comput Appl,2009,18:141–155.
4Zhang Y H, Zhang Y S, He Z F. Multiscale fusion of wavelet-domain hidden Markov tree through graphcut [J]. Image Vision Comput,2009,29(3):1402–1410.
5Dusan G, Mihai D. Gauss-Markov model for wavelet-based SAR image despeckling [J]. IEEE SignalProc Let,2006,6:365-368.
6Romberg J K, Choi H, Baraniuk R G. Bayesian tree-structured image modeling using wavelet-domainhidden Markov models [J]. IEEE Image Process,2011,7:1056-1068.
7Fan G and Xia XG. Wavelet-based texture analysis and synthesis using hidden Markov models [J]. IEEETrans Circuits Syst Fundam Theory Appl,2003,50(1):106–120.
8Zhang Y H, He Z Y, Zhang Y S. Global optimization of wavelet-domain hidden Markovtree for imagesegmentation [J]. Pattern Recogn,2011,44:2811-2818.
9Cehux G, Forbes F, Peyrard N. EM procedures Using mean field-like approximations for Markovmodel-based image segmentation [J]. Pattern Recogn,2003,36:131-143.
10Chen M, Strobl J. Multispectral textured image segmentation using a multi-resolution fuzzy Markovrandom field model on variable scales in the wavelet domain[J]. International Journal of Remote Sensing,2013,34(13):4550-4569.
11Zheng C, Wang L G, Chen R Y. Image Segmentation Using Multiregion-Resolution MRF Model [J].IEEE Geoscience and Remote Sensing Letters,2013,10(4):816-820.
12Sun J, Zhu H Y, Xu Z B. Poisson image fusion based on Markov random field fusion model [J].Information Fusion,2013,14(3):241-254.
13Osadebey M, Bouguila N, Arnold D. The Clique Potential of Markov Random Field in a RandomExperiment for Estimation of Noise Levels in2D Brain MRI [J]. International Journal of Imaging Systemsand Technology,2013,23(4):304-313.
14Chen Y S, Amini A A. A MAP framework for tag line detection in SPAMM data using Markov randomfields on the B-spline solid [J]. IEEE Transactions on Medical Imaging,2002,21(9):1110-1122.
15Van L K, Maes F, Vandermeulen D. Automated segmentation of multiple sclerosis lesions by modeloutlier detection [J]. IEEE Transactions on Medical Imaging,2001,20(8):677-688.
16Kersten J, Gaehler M, Voigt S. A General Framework for Fast and Interactive Classification of OpticalVHR Satellite Imagery Using Hierarchical and Planar Markov Random Fields [J]. PhotogrammetrieFernerkundung Geoinformation,2010,6:439-449.
17Nowozin S, Lampert C H. Global Interactions in Random Field Models: A Potential Function EnsuringConnectedness [J]. Siam Journal on Imaging Sciences,2010,3(4):1048-1074.
18Ashraf A B, Gavenonis S C, Daye D. A Multichannel Markov Random Field Framework for TumorSegmentation With an Application to Classification of Gene Expression-Based Breast Cancer RecurrenceRisk [J]. IEEE Transactions on Medical Imaging,2013,32(4):637-648.
19Costa J P D, Galland F, Roueff A, Germain C. Unsupervised segmentation based on Von Misescircular distributions for orientation estimation in textured images [J]. Journal of Electronic Imaging,2012,21(2):021102-1-021102-7.
20Lehman F. Turbo segmentation of textured images [J]. IEEE T PATTERN ANAL,2011,33(1):16-29.
21Yang L Y, Wang X Y, Wang Q Y, Zhang X J. LS-SVM based image segmentation using color andtexture information [J]. J Vis Commun Image R,2012,23:1095–1112.
22Yu Q Y, Clausi D A. Image Segmentation Using Edge Penalties and Region Growing [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2008,30(12):2126-2139.
23Derpanis K G, Wildes R P, Tsotsos J K. Definition and recovery of kinematic features for recognition ofAmerican sign language movements [J]. Image and Vision Computing,2008,26(12):1650-1662.
24Le C S, Salzenstein F, Collet C. Fuzzy pairwise Markov chain to segment correlated noisy data [J].Signal Processing,2008,88(10):2526-2541.
25Chen M, Wu W, Yang X M. Hidden-Markov-Model-Based Segmentation Confidence Applied toContainer Code Character Extraction [J]. IEEE Transactions on Intelligent Transportation Systems,2011,
12(4):1147-1156.
26Monaco J P, Madabhushi A. Class-specific weighting for Markov random field estimation: Applicationto medical image segmentation [J]. Medical Image Analysis,2012,16(8):1477-1489.
27Yang F G, Jiang T Z. Pixon-based image segmentation with Markov random fields [J]. IEEETransactions on Image Processing,2003,12(12):1552-1559.
28Xu M, Chen H, Varshney P K. An Image Fusion Approach Based on Markov Random Fields [J]. IEEETransactions on Geoscience and Remote Sensing,2011,49(12):5116-5127.
29Bratsolis E, Sigelle M. Fast SAR image restoration, segmentation, and detection of high-reflectanceregions [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2890-2899.
30Yang Q J, Liang J M, Hu Z J. Auroral Sequence Representation and Classification Using HiddenMarkov Models [J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(12):5049-5060.