面粉麸星的识别与测量
|
摘要
面粉中麸星的含量多少是面粉等级评定的一个重要指标,也是反映面粉生产工艺水平的重要指标。本文开发的“面粉麸星的识别与检测”系统可用于面粉生产企业的在线监测,使其能实时监测面粉生产情况。同时,该系统也可以作为面粉检测机构的检测仪器,用于检测送检的面粉样品。
麸星的识别和测量是建立在数字图像处理和模式识别相关理论基础上的。文中首先详细介绍了系统的结构框图,然后介绍了各个部分的功能和实现方法,最后具体介绍了系统的实现算法,讨论了不同的算法对系统结果的影响。系统的实现算法主要包括面粉图像的预处理和麸星的识别与测量算法。图像的预处理中首先将RGB真彩色面粉麸星图像转换为灰度图像,然后讨论了中值滤波、高斯滤波、形态学滤波、拉普拉斯锐化等算法对图像处理的影响,最终采用高斯平滑滤波加上拉普拉斯锐化的处理算法。在预处理的基础上对图像中的麸星进行检测,讨论了包括模板匹配法、线性判别法、K均值聚类法等各种方法。最后利用麸星与面粉灰度值不同的特点,采用将面粉图像分成多个小块的方法,对单个小块计算灰度值求得图像的最佳阈值,计算图像中麸星的数目和大小。在此基础上,对图像进行对比度分析,完成麸星相关信息获取工作,在显示设备上显示获得的数据,最终完成了麸星的定量检测工作。
该系统主要从三个方面检测面粉的质量:面粉中麸星的数目、麸星所占面粉的比例、面粉中各个麸星的大小。文章最后对面粉麸星的识别与测量系统优缺点做出总结,并参照其他在线检测仪器特点,提出了有待改进的地方。
The brans contained in the flour is an important indicator to evaluation the flour's rank, which also reflects the technique of production level. In the article, the system of "the identification and detection of brans in flour " can be used for flour production to monitor the production of flour for real time on-line. It also can be used as flour testing organizations detection apparatus to detect the samples flour.
The system is based on the relevant theories of digital image processing and pattern recognition. First, the article described the structure of the system block diagram in detail, then introduced the functions of every parts of the system and how to achieve the functions. The System algorithm mainly includes image pre-processing algorithm and the recognition and measurement of brans in flour algorithm. First step of image pre-processing algorithm is convert the RGB true Color image to gray image, and then discussed the median filtering, Gaussian filtering, morphological filtering, Laplacian sharpening, etc. algorithm, and finally using the Gaussian smoothing filter combined with the Laplacian sharpening. Based on the pre-processing image, the paper discussed different algorithm to detect and measure brans in flour, including template matching method, linear discriminant method, K-means clustering method and other methods. Finally, according to the flour and brans have different gray values, dividing the image into small blocks, then calculating the gray value of each single block, and decided which is the best threshold of the image. Next step is calculating the number and size of the brans in the image. After that is to analyze the contrast of the image, obtaining useful information, displaying the data obtained on the display device.
The system detected the quality of flour in three aspects : the number of brans, brans share the proportion of flour, the size of each bran. At the end of the article, there is a sum of the advantages and disadvantages of the system, and taking into account the characteristics of other online testing instruments, there gives some improvements.
麸星的识别和测量是建立在数字图像处理和模式识别相关理论基础上的。文中首先详细介绍了系统的结构框图,然后介绍了各个部分的功能和实现方法,最后具体介绍了系统的实现算法,讨论了不同的算法对系统结果的影响。系统的实现算法主要包括面粉图像的预处理和麸星的识别与测量算法。图像的预处理中首先将RGB真彩色面粉麸星图像转换为灰度图像,然后讨论了中值滤波、高斯滤波、形态学滤波、拉普拉斯锐化等算法对图像处理的影响,最终采用高斯平滑滤波加上拉普拉斯锐化的处理算法。在预处理的基础上对图像中的麸星进行检测,讨论了包括模板匹配法、线性判别法、K均值聚类法等各种方法。最后利用麸星与面粉灰度值不同的特点,采用将面粉图像分成多个小块的方法,对单个小块计算灰度值求得图像的最佳阈值,计算图像中麸星的数目和大小。在此基础上,对图像进行对比度分析,完成麸星相关信息获取工作,在显示设备上显示获得的数据,最终完成了麸星的定量检测工作。
该系统主要从三个方面检测面粉的质量:面粉中麸星的数目、麸星所占面粉的比例、面粉中各个麸星的大小。文章最后对面粉麸星的识别与测量系统优缺点做出总结,并参照其他在线检测仪器特点,提出了有待改进的地方。
The brans contained in the flour is an important indicator to evaluation the flour's rank, which also reflects the technique of production level. In the article, the system of "the identification and detection of brans in flour " can be used for flour production to monitor the production of flour for real time on-line. It also can be used as flour testing organizations detection apparatus to detect the samples flour.
The system is based on the relevant theories of digital image processing and pattern recognition. First, the article described the structure of the system block diagram in detail, then introduced the functions of every parts of the system and how to achieve the functions. The System algorithm mainly includes image pre-processing algorithm and the recognition and measurement of brans in flour algorithm. First step of image pre-processing algorithm is convert the RGB true Color image to gray image, and then discussed the median filtering, Gaussian filtering, morphological filtering, Laplacian sharpening, etc. algorithm, and finally using the Gaussian smoothing filter combined with the Laplacian sharpening. Based on the pre-processing image, the paper discussed different algorithm to detect and measure brans in flour, including template matching method, linear discriminant method, K-means clustering method and other methods. Finally, according to the flour and brans have different gray values, dividing the image into small blocks, then calculating the gray value of each single block, and decided which is the best threshold of the image. Next step is calculating the number and size of the brans in the image. After that is to analyze the contrast of the image, obtaining useful information, displaying the data obtained on the display device.
The system detected the quality of flour in three aspects : the number of brans, brans share the proportion of flour, the size of each bran. At the end of the article, there is a sum of the advantages and disadvantages of the system, and taking into account the characteristics of other online testing instruments, there gives some improvements.
引文
[1]黄斌.关于减少面粉中麸星含量探讨[J].粮食与饲料工业,2001(3):7-8.
[2]李同强.面粉麸星的计算机检测[J].中国粮油学报,2005(4):Vol.20,No.2:71-74.
[3]刘建军,何中虎等.目前推广小麦品种面粉白度概况及其相关因素的研究[J].山东农业科学,2002(2):10-12.
[4]万富世,王光瑞,李宗智.我国小麦品质现状及其改良目标初探[J].中国农业科学,1989,Vol.22 No.3:14-21.
[5]GB1355-1986小麦粉国家标准.
[6]Refael,C.Gonzalez,Richard E.Woods著,阮秋琦,阮宇智等译,数字图像处理(第二版)[M].北京:电子工业出版,2007:228-230,200-230.
[7]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[8]孙即祥等.现代模式识别[M].长沙:国防科技大学出版社,2002.
[9]徐江丰,张涌,汤心溢.红外目标检测的形态滤波改进算法及其DSP实现[J].激光与红外,2006(8):716-717.
[10]董鸿燕,李吉成,沈振康.基于形态滤波和顺序滤波的小目标检测[J].红外技术,2004(1):21-24.
[11]Barmann F,Biegler Konig F.On a class of efficient learning algorithm for neural networks.[J].Neural Networks,1992,Vol.5:36-47.
[12]杭州奥泰图像系统集成公司.嵌入式图像采集卡使用说明系列/奥泰图像SDK开发包5.1.4正式版:中国,2006.
[13]英德斯科技公司.PCM-6551主板使用手册:中国,2006.
[14]《电脑编程与技巧维护》杂志社.Visual C/C++编程精选集锦(数据库及图形图像分册)[M].北京:科学出版社,2003:12-18,112-115,141-157,177-184
[15]谷保山.Visual C++6.0编程与实例[M].北京:科学出版社,1999.
[16]Herbert Schildt著,张林娣译.C++基础教程[M].北京:清华大学出版社,2002.
[17]王洪涛.深入剖析Visual C++(编程技术及应用实例)[M].北京:人民邮电出版社,2003.
[18]张宏林.精通Visual C++数字图像处理典型算法及实现(第2版)[M].北京:人民邮电出版社,2008.
[19]谢凤英,赵丹培.Visual C++数字图像处理[M].北京:电子工业出版社,2008.
[20]百度百科.YUV[OL].[2008.10.2].http://baike.baidu.com/view/189685.htm.
[21]程开富.CCD图像传感器的原理与应用概述[J].集成电路通讯,2003,21(3):41-44.
[22]朱伟,韩服善.光电传感器在自动化生产线上的应用[J].电子工程师,2004,30(8):72-73.
[23]温佩芝,史泽林,于海斌.基于形态学的海面背景红外点目标检测方法[J].光电工程,2003(12):55-58.
[24]Murthy C A,Pal S K.Histogram thresholding by minimizing gray level fuzziness.[C].Information Sciences,1992,60:107-135.
[25]Witkin,A P.Scale-Space Filtering[C].Proceeding of the 8th International Joint Conference on Artificial Interiligence,1983(2):1019-1022.
[26]杨辉,彭良玉,陆世专.基于小波域高斯混合模型与中值滤波的图像去噪研究[J].图像与多媒体技术,2007(5):61-63.
[27]侯阿临,徐欣等.基于Top-Hat预处理和小波能量分析的车牌定位算法[J].吉林大学学报(信息科学版),2007(5):343-346.
[28]Pesaresi M,Benediktsson J.A New Approach for the Morphological Segmentation of High Resolution Satellite Imagery[J].IEEE Transactions on Geoscience and Remote Sensing.2001,39(2):309-320.
[29]Dougherty E R,Astola J.An Introduction to Non-linear Image Processing[J].SPIE,1994,33(4):25-35.
[30]沈世旻.计算机视觉和模式识别在车牌识别中的应用[D].华中科技大学硕士论文,2004.
[32]王佳男.采用基于区域的自动种子区域生长法的彩色图像分割方法[D].东北师范大学硕士论文,2007.
[33]方芳.基于区域生长与聚类的彩色图像分割方法的改进[D].燕山大学硕士论文,2006.
[34]蒋苏蓉.色度空间上基于子块区域生长的彩色图像分割方法[D].华中师范大学硕士论文,2003.
[35]马玉平.基于遥感信息的华北冬小麦区域生长模型及其模拟研究[D].中国气象科学研究院硕士论文,2004.
[36]赵东卫.基于区域生长的三维地震数据场的面可视化方法研究[D].南京理工大学硕士论文,2007.
[37]Tremeau A,and Borel N.A Region Growing and Merging Algorithm to Color Segmentation.[J].Pattern Recognition,1997,30,30:1191-1203.
[38]Sclaroff S,Liu L Deformable Shape Detector and Description via Model-Based Grouping[J].IEEE Tran,on Pattern Analysis and Machine Intelligence,2001(23):75-488.
[39]SAHOO P K.A Survey of Thresholding Technic[J].Computer Vision Graphics and Image Processing,1995(3):370-377.
[40]舒红平,蒋建民.基于灰度最优阈值的图像分割方法及应用[J].重庆工商大学学报(自然科学版),2003(12):81-83.
[41]Kapur JN,Sahoo P K,Wong A K C.A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram.[C].CVGIP,1985,Vol.29:273-285.
[42]Newman T S,Jain A K.A survey of automated visual inspection[J].Computer Vision and Image Understanding,1995,Vol.61:231-262.
[43]D.J.Montana.Genetic Search of a Generalized Hough Transform Space,Adaptive and learning system[C].Proceedings of SPIE,1992,Vo1.1706:14-21.
[44]Chen J M.Vision based shape recognition and analysis of machined parts[J].International Journal of Production Research,1995,Vol.33:101-135.
[45]张佩芬,李伟,杨力等.混合模式识别系统研究[J].信息与控制,1997,Vol.26:121-128.
[46]任洪娥,王磊.基于投影特征匹配法的车牌汉字识别方法[J].黑龙江科技消息,年份不详.
[47]于连杰,纪玉波.基于CAGH检测和投影法的车牌定位算法[J].科学技术与工程,2008(7),Vol.8No.14:3990-3992.
[48]高喆,康耀红,雷景生.基于统计投影法的图像检索[J].海南大学学报自然科学版,2008(3),Vol.26 No.1:43-46.
[49]薛琴,陈玮.模式识别在轮对踏面磨耗检测中的应用[J].微计算机信息,2007,Vol.23 No.8-1:258-261.
[50]周彩霞,匡纲要等.基于差影法粗分割与多模板匹配的人脸检测[J].计算机工程与设计,2004(10),vol.25 No.10:1648-1650.
[51]陈廉清,崔治,王龙山.基于差影和模板匹配的微小轴承表面缺陷检测[J].中国机械工程,2006(5),第17卷第10期:1018-1021.
[52]Wu Wen Yen,Wang Mao Jiun,Lui Chi Ming.Automated inspection of printed circuit boards through machine vision[J].Computer in Industry,1996(2):103-111.
[53]杨晓胜.基于模式识别的鞋底花纹自动识别与分类[D].大连海事大学硕士论文,2005.
[54]田娟,郑郁正.模板匹配技术在图像识别中的应用[J].传感器与微系统,2008(1),Vol.27:112-114.
[55]崔政,李壮.两种改进的模板匹配识别算法[J].计算机工程与设计,2006,27(6):1083-1085.
[56]李弼程,邵美珍,黄浩.模式识别原理与应用[M].西安:西安电子科技大学出版社,2008:8-9,45-52,111-112.
[57]傅蓉,申洪.基于色度学准则分析的免疫组化彩色图像C均值聚类分割技术研究[J].中国体视学与图像分析,2007(3),Vol.12 No.1:6-10.
[58]Lim Y W,Lee S.U.On the color image segmentation algorithm based on the thresholding and fuzzy C-means techniques.[J].Pattern Recognition,1990,Vol.37:935-952.
[59]叶四民,陈福祥.指纹图像预处理中的二值化技术[J].自动化与仪器仪表,2001(2):30-32.
[60]杜晓晨,刘建平.改进的模糊阈值图像分割方法[J].光电工程,2005(10):51-53.
[61]HUANGL K,WANGMJ.Image thresholding by minimizing the measures of fuzziness[J].Pattern Recognition,1995,28(1):41-51.
[62]舒红平,蒋建民.基于灰度最优阈值的图像分割方法及应用[J].重庆工商大学学报(自然科学版),2003(12):81-83.
[63]GB 5504-85《粮食、油料检验小麦加工精度检验法》
[64]杨朝柱,张磊,司红起.小麦面粉白度研究进展[J].麦类作物学报,2002,Vol.22 No.3:74-77.
[65]American Association of Cereal Chemists,St Paul,MN.AACC Method 14-16[S].Approved April 1961;AACC Method 14-23[S].Approved October 1974.
[66]Yasunaga T,Uemura M.Evaluation of color characteristics of obtained from various types and varieties of wheat[J].Cereal Chem.,1962,39(3):171-183.
[67]Lin Z J,Miskelly DM,Moss H J.Suitability of various australian wheat for Chinese-style steamed bread[J].J.Sci.Food Agric.,1990,53:203-213.
[68]Dowell F E.Automated color classification of singlewheat kernels using visible and near-infrared reflectance[J].Cereal Chem.,1998,75(1):142-144.
[2]李同强.面粉麸星的计算机检测[J].中国粮油学报,2005(4):Vol.20,No.2:71-74.
[3]刘建军,何中虎等.目前推广小麦品种面粉白度概况及其相关因素的研究[J].山东农业科学,2002(2):10-12.
[4]万富世,王光瑞,李宗智.我国小麦品质现状及其改良目标初探[J].中国农业科学,1989,Vol.22 No.3:14-21.
[5]GB1355-1986小麦粉国家标准.
[6]Refael,C.Gonzalez,Richard E.Woods著,阮秋琦,阮宇智等译,数字图像处理(第二版)[M].北京:电子工业出版,2007:228-230,200-230.
[7]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[8]孙即祥等.现代模式识别[M].长沙:国防科技大学出版社,2002.
[9]徐江丰,张涌,汤心溢.红外目标检测的形态滤波改进算法及其DSP实现[J].激光与红外,2006(8):716-717.
[10]董鸿燕,李吉成,沈振康.基于形态滤波和顺序滤波的小目标检测[J].红外技术,2004(1):21-24.
[11]Barmann F,Biegler Konig F.On a class of efficient learning algorithm for neural networks.[J].Neural Networks,1992,Vol.5:36-47.
[12]杭州奥泰图像系统集成公司.嵌入式图像采集卡使用说明系列/奥泰图像SDK开发包5.1.4正式版:中国,2006.
[13]英德斯科技公司.PCM-6551主板使用手册:中国,2006.
[14]《电脑编程与技巧维护》杂志社.Visual C/C++编程精选集锦(数据库及图形图像分册)[M].北京:科学出版社,2003:12-18,112-115,141-157,177-184
[15]谷保山.Visual C++6.0编程与实例[M].北京:科学出版社,1999.
[16]Herbert Schildt著,张林娣译.C++基础教程[M].北京:清华大学出版社,2002.
[17]王洪涛.深入剖析Visual C++(编程技术及应用实例)[M].北京:人民邮电出版社,2003.
[18]张宏林.精通Visual C++数字图像处理典型算法及实现(第2版)[M].北京:人民邮电出版社,2008.
[19]谢凤英,赵丹培.Visual C++数字图像处理[M].北京:电子工业出版社,2008.
[20]百度百科.YUV[OL].[2008.10.2].http://baike.baidu.com/view/189685.htm.
[21]程开富.CCD图像传感器的原理与应用概述[J].集成电路通讯,2003,21(3):41-44.
[22]朱伟,韩服善.光电传感器在自动化生产线上的应用[J].电子工程师,2004,30(8):72-73.
[23]温佩芝,史泽林,于海斌.基于形态学的海面背景红外点目标检测方法[J].光电工程,2003(12):55-58.
[24]Murthy C A,Pal S K.Histogram thresholding by minimizing gray level fuzziness.[C].Information Sciences,1992,60:107-135.
[25]Witkin,A P.Scale-Space Filtering[C].Proceeding of the 8th International Joint Conference on Artificial Interiligence,1983(2):1019-1022.
[26]杨辉,彭良玉,陆世专.基于小波域高斯混合模型与中值滤波的图像去噪研究[J].图像与多媒体技术,2007(5):61-63.
[27]侯阿临,徐欣等.基于Top-Hat预处理和小波能量分析的车牌定位算法[J].吉林大学学报(信息科学版),2007(5):343-346.
[28]Pesaresi M,Benediktsson J.A New Approach for the Morphological Segmentation of High Resolution Satellite Imagery[J].IEEE Transactions on Geoscience and Remote Sensing.2001,39(2):309-320.
[29]Dougherty E R,Astola J.An Introduction to Non-linear Image Processing[J].SPIE,1994,33(4):25-35.
[30]沈世旻.计算机视觉和模式识别在车牌识别中的应用[D].华中科技大学硕士论文,2004.
[32]王佳男.采用基于区域的自动种子区域生长法的彩色图像分割方法[D].东北师范大学硕士论文,2007.
[33]方芳.基于区域生长与聚类的彩色图像分割方法的改进[D].燕山大学硕士论文,2006.
[34]蒋苏蓉.色度空间上基于子块区域生长的彩色图像分割方法[D].华中师范大学硕士论文,2003.
[35]马玉平.基于遥感信息的华北冬小麦区域生长模型及其模拟研究[D].中国气象科学研究院硕士论文,2004.
[36]赵东卫.基于区域生长的三维地震数据场的面可视化方法研究[D].南京理工大学硕士论文,2007.
[37]Tremeau A,and Borel N.A Region Growing and Merging Algorithm to Color Segmentation.[J].Pattern Recognition,1997,30,30:1191-1203.
[38]Sclaroff S,Liu L Deformable Shape Detector and Description via Model-Based Grouping[J].IEEE Tran,on Pattern Analysis and Machine Intelligence,2001(23):75-488.
[39]SAHOO P K.A Survey of Thresholding Technic[J].Computer Vision Graphics and Image Processing,1995(3):370-377.
[40]舒红平,蒋建民.基于灰度最优阈值的图像分割方法及应用[J].重庆工商大学学报(自然科学版),2003(12):81-83.
[41]Kapur JN,Sahoo P K,Wong A K C.A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram.[C].CVGIP,1985,Vol.29:273-285.
[42]Newman T S,Jain A K.A survey of automated visual inspection[J].Computer Vision and Image Understanding,1995,Vol.61:231-262.
[43]D.J.Montana.Genetic Search of a Generalized Hough Transform Space,Adaptive and learning system[C].Proceedings of SPIE,1992,Vo1.1706:14-21.
[44]Chen J M.Vision based shape recognition and analysis of machined parts[J].International Journal of Production Research,1995,Vol.33:101-135.
[45]张佩芬,李伟,杨力等.混合模式识别系统研究[J].信息与控制,1997,Vol.26:121-128.
[46]任洪娥,王磊.基于投影特征匹配法的车牌汉字识别方法[J].黑龙江科技消息,年份不详.
[47]于连杰,纪玉波.基于CAGH检测和投影法的车牌定位算法[J].科学技术与工程,2008(7),Vol.8No.14:3990-3992.
[48]高喆,康耀红,雷景生.基于统计投影法的图像检索[J].海南大学学报自然科学版,2008(3),Vol.26 No.1:43-46.
[49]薛琴,陈玮.模式识别在轮对踏面磨耗检测中的应用[J].微计算机信息,2007,Vol.23 No.8-1:258-261.
[50]周彩霞,匡纲要等.基于差影法粗分割与多模板匹配的人脸检测[J].计算机工程与设计,2004(10),vol.25 No.10:1648-1650.
[51]陈廉清,崔治,王龙山.基于差影和模板匹配的微小轴承表面缺陷检测[J].中国机械工程,2006(5),第17卷第10期:1018-1021.
[52]Wu Wen Yen,Wang Mao Jiun,Lui Chi Ming.Automated inspection of printed circuit boards through machine vision[J].Computer in Industry,1996(2):103-111.
[53]杨晓胜.基于模式识别的鞋底花纹自动识别与分类[D].大连海事大学硕士论文,2005.
[54]田娟,郑郁正.模板匹配技术在图像识别中的应用[J].传感器与微系统,2008(1),Vol.27:112-114.
[55]崔政,李壮.两种改进的模板匹配识别算法[J].计算机工程与设计,2006,27(6):1083-1085.
[56]李弼程,邵美珍,黄浩.模式识别原理与应用[M].西安:西安电子科技大学出版社,2008:8-9,45-52,111-112.
[57]傅蓉,申洪.基于色度学准则分析的免疫组化彩色图像C均值聚类分割技术研究[J].中国体视学与图像分析,2007(3),Vol.12 No.1:6-10.
[58]Lim Y W,Lee S.U.On the color image segmentation algorithm based on the thresholding and fuzzy C-means techniques.[J].Pattern Recognition,1990,Vol.37:935-952.
[59]叶四民,陈福祥.指纹图像预处理中的二值化技术[J].自动化与仪器仪表,2001(2):30-32.
[60]杜晓晨,刘建平.改进的模糊阈值图像分割方法[J].光电工程,2005(10):51-53.
[61]HUANGL K,WANGMJ.Image thresholding by minimizing the measures of fuzziness[J].Pattern Recognition,1995,28(1):41-51.
[62]舒红平,蒋建民.基于灰度最优阈值的图像分割方法及应用[J].重庆工商大学学报(自然科学版),2003(12):81-83.
[63]GB 5504-85《粮食、油料检验小麦加工精度检验法》
[64]杨朝柱,张磊,司红起.小麦面粉白度研究进展[J].麦类作物学报,2002,Vol.22 No.3:74-77.
[65]American Association of Cereal Chemists,St Paul,MN.AACC Method 14-16[S].Approved April 1961;AACC Method 14-23[S].Approved October 1974.
[66]Yasunaga T,Uemura M.Evaluation of color characteristics of obtained from various types and varieties of wheat[J].Cereal Chem.,1962,39(3):171-183.
[67]Lin Z J,Miskelly DM,Moss H J.Suitability of various australian wheat for Chinese-style steamed bread[J].J.Sci.Food Agric.,1990,53:203-213.
[68]Dowell F E.Automated color classification of singlewheat kernels using visible and near-infrared reflectance[J].Cereal Chem.,1998,75(1):142-144.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |