形态学多尺度重建结合凹点匹配分割枸杞图像
|
摘要
针对枸杞分级过程中因图像噪声、光照不均匀和粘连等造成枸杞难以准确分割的问题,提出了一种基于形态学多尺度开闭重建结合凹点匹配的分割方法。首先提取原始图像的红色分量去除枸杞光照阴影噪声,利用形态学多尺度混合开闭重建对红色分量图像进行重建,平滑枸杞内部而保留轮廓边缘信息;然后采用8邻域跟踪算法提取粘连枸杞轮廓边缘;最后运用圆形模板检测粘连枸杞的轮廓凹点,以凹点间最短欧氏距离为匹配条件连接凹点对,并对匹配错误的凹点对进行修正,实现粘连枸杞分割。试验结果表明,该文方法分割准确率较高,而过分割率较低,相比标记控制的分水岭和直接凹点匹配分割等方法,对粘连枸杞分割效果较好,分割准确率可达到96%。该研究可为枸杞分割技术提供理论支撑。
The traditional Chinese wolfberry classification usually adopts manual grading in terms of the wolfberry characteristics of size,color,surface defects,and so on.It is a time-consuming and inefficient work.Fortunately,machine vision provides an efficient and fast way to improve the classification efficiency and accuracy.During the process of wolfberry classification by machine vision,the first and important task is to segment wolfberry particles from the image,and then classify them into different grades according to their characteristics.However,the accuracy of wolfberry image segmentation process is often hindered by a number of constraints including noise,inhomogeneous intensity,complex adherent and overlapped particles,which easily cause the decline of segmentation accuracy,and subsequently affect the wolfberry classification effect.For the purpose to improve the accuracy and efficiency of wolfberry image segmentation,a method for efficient segmentation of adherent wolfberries based on morphological multi-scale reconstruction and concave points matching is hereby proposed.Firstly,the red component of the original color image is extracted to partially remove the shadow noise around or inside the wolfberries,and then the red component image is reconstructed by morphological multi-scale mixture opening-closing reconstruction to further smoothen the interior of wolfberries while preserving the contour edge information.Since such reconstruction operation can effectively retain the interesting contour edge of wolfberry particles and eliminate the irregular details,the influence of wolfberries edge contours blur and location offset on the subsequent classification will be greatly reduced.The binary regions of wolfberries are extracted from the reconstructed image by the method of maximum between-cluster variance,and the holes in the interior of wolfberries are filled by morphological filling operator.In the filled binary image,there are 2 kinds of wolfberries.One kind consists of single non-adherent wolfberries particles,and can be extracted by morphological area opening operation without further processing.The other kind mainly contains adherent or overlapped wolfberries particles,and needs to further segment,so 8-neighborhood tracking algorithm is used to extract the edge of single pixel contours of the adherent wolfberries.Taking into account that the shape of wolfberry is ellipsoid,the concave points usually locate in the edges where they are touched or overlapped with each other.Therefore the circular template is used to detect these edge concave points.For the incorrect concave point's pairs matched by the shortest Euclidean distance as fitting condition,they can be modified according to the unequal pixel distance between the middle point of the connecting line and the boundary point since the length-to-width ratio of the wolfberry is obvious.When all the concave points' pairs of the adherent wolfberries are confirmed,adherent wolfberries are clearly segmented.The final segmentation results are the combination of single non-adherent and adherent or overlapped wolfberries.The simulation results show that this method can achieve more accurate segmentation results and lower over-segmentation rate compared with the methods of mark-controlled watershed,direct concave points matching,and watershed combined with concave point segmentation,and is especially suitable for the segmentation of adherent wolfberries.The highest accurate segmentation rate is 96% while over-segmentation rate less than 2%.
The traditional Chinese wolfberry classification usually adopts manual grading in terms of the wolfberry characteristics of size,color,surface defects,and so on.It is a time-consuming and inefficient work.Fortunately,machine vision provides an efficient and fast way to improve the classification efficiency and accuracy.During the process of wolfberry classification by machine vision,the first and important task is to segment wolfberry particles from the image,and then classify them into different grades according to their characteristics.However,the accuracy of wolfberry image segmentation process is often hindered by a number of constraints including noise,inhomogeneous intensity,complex adherent and overlapped particles,which easily cause the decline of segmentation accuracy,and subsequently affect the wolfberry classification effect.For the purpose to improve the accuracy and efficiency of wolfberry image segmentation,a method for efficient segmentation of adherent wolfberries based on morphological multi-scale reconstruction and concave points matching is hereby proposed.Firstly,the red component of the original color image is extracted to partially remove the shadow noise around or inside the wolfberries,and then the red component image is reconstructed by morphological multi-scale mixture opening-closing reconstruction to further smoothen the interior of wolfberries while preserving the contour edge information.Since such reconstruction operation can effectively retain the interesting contour edge of wolfberry particles and eliminate the irregular details,the influence of wolfberries edge contours blur and location offset on the subsequent classification will be greatly reduced.The binary regions of wolfberries are extracted from the reconstructed image by the method of maximum between-cluster variance,and the holes in the interior of wolfberries are filled by morphological filling operator.In the filled binary image,there are 2 kinds of wolfberries.One kind consists of single non-adherent wolfberries particles,and can be extracted by morphological area opening operation without further processing.The other kind mainly contains adherent or overlapped wolfberries particles,and needs to further segment,so 8-neighborhood tracking algorithm is used to extract the edge of single pixel contours of the adherent wolfberries.Taking into account that the shape of wolfberry is ellipsoid,the concave points usually locate in the edges where they are touched or overlapped with each other.Therefore the circular template is used to detect these edge concave points.For the incorrect concave point's pairs matched by the shortest Euclidean distance as fitting condition,they can be modified according to the unequal pixel distance between the middle point of the connecting line and the boundary point since the length-to-width ratio of the wolfberry is obvious.When all the concave points' pairs of the adherent wolfberries are confirmed,adherent wolfberries are clearly segmented.The final segmentation results are the combination of single non-adherent and adherent or overlapped wolfberries.The simulation results show that this method can achieve more accurate segmentation results and lower over-segmentation rate compared with the methods of mark-controlled watershed,direct concave points matching,and watershed combined with concave point segmentation,and is especially suitable for the segmentation of adherent wolfberries.The highest accurate segmentation rate is 96% while over-segmentation rate less than 2%.
引文
[1]王履程,谭筠梅,王小鹏,等.基于机器视觉的枸杞分级方法[J].计算机工程与应用,2013,49(24):16-18.Wang Lücheng,Tan Junmei,Wang Xiaopeng,et al.Wolfberry classification method based on machine vision[J].Computer Engineering and Applications,2013,49(24):16-18.(in Chinese with English abstract)
[2]李青,彭彦昆.基于机器视觉的猪胴体背膘厚度在线检测技术[J].农业工程学报,2015,31(9):256-261.Li Qing,Peng Yankun.Pork back fat thickness on-line detection methods using machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(9):256-261.(in Chinese with English abstract)
[3]陈红,夏青,左婷,等.破损花菇机器视觉检测技术[J].农业机械学报,2014,45(11):60-67.Chen Hong Xia Qing Zuo Ting,et al.Application of machine vision in detection of broken shiitake[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(11):60-67.(in Chinese with English abstract)
[4]彭江南,谢宗铭,杨丽明,等.基于Seed Identification软件的棉籽机器视觉快速精选[J].农业工程学报,2013,29(23):147-152.Peng Jiangnan,Xie Zongming,Yang Liming,et al.Quickly selection for cotton seed based on seed identification software[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(23):147-152.(in Chinese with English abstract)
[5]熊俊涛,邹湘军,刘念彭,等.基于机器视觉的荔枝果实采摘时品质检测技术[J].农业机械学报,2014,45(7):54-60.Xiong Juntao,Zou Xiangjun,Liu Nianpeng,et al.Fruit quality detection based on machine vision technology when picking litchi[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):54-60.(in Chinese with English abstract)
[6]卢军,付雪媛,苗晨琳.基于颜色和纹理特征的柑橘自动分级[J].华中农业大学报,2012,31(6):783-786.Lu Jun,Fu Xueyuan,Miao Chenlin,et al.Citrus automatic grading by using color and texture features[J].Journal of Huazhong Agricultural University,2012,31(6):783-786.(in Chinese with English abstract)
[7]李希,王天江,周鹏.一种改进的粘连颗粒图像分割算法[J].湖南大学学报:自然科学版,2012,39(12):84-88.Li Xi,Wang Tianjiang,Zhou Peng.An improved segmenting algorithm for touched particle image[J].Journal Hunan University:Natural Sciences,2012,39(12):84-88.(in Chinese with English abstract)
[8]杨华东,简淼夫.基于灰度形态重构的颗粒图像分割方法[J].南京工业大学学报:自然科学版,2005,27(3):98-102.Yang Huadong,Jian Miaofu.The segmentation algorithm of particle image based on grayscale morphological reconstruction[J].Journal of Nanjing University of Technology,2005,27(3):98-102.(in Chinese with English abstract)
[9]Nee Lim Huey,Mashor Mohd Yusoff,Hassan Rosline.White blood cell segmentation for acute leukemia bone marrow images[J].Journal of Medical Imaging&Health Informatics,2012,2(3):357-361..
[10]Quan L,Jiang E.Automatic segmentation method of touching corn kernels in digital image based on improved watershed algorithm[C]//International Conference on New Technology of Agricultural Engineering,Zibo,China,2011,5(27/28/29):34-37.
[11]王桂芹,王正勇,罗代升.基于FCM和标记分水岭的粘连岩石颗粒图像分割[J].四川大学学报:自然科学版,2012,49(2):356-360.Wang Guiqin,Wang Zhengyong,Luo Daisheng.Image segmentation of overlapping rock particles based on FCM and labeling watershed[J].Journal of Sichuan University:Natural Science Edition,2012,49(2):356-360.(in Chinese with English abstract)
[12]李文勇,李明,钱建平,等.基于形状因子和分割点定位的粘连害虫图像分割方法[J].农业工程学报,2015,31(5):175-180.Li Wenyong,Li Ming,Qian Jianping,et al.Segmentation method for touching pest images based on shape factor and separation points location[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):175-180.(in Chinese with English abstract)
[13]韦冬冬,赵豫红.基于凹点匹配的重叠图像分割算法[J].计算机与应用化学,2010,27(1):99-102.Wei Dongdong,Zhao Yuhong.An image segment algorithm for overlapped particles based on concave points matching[J].Computers and Applied Chemistry,2010,27(1):99-102.(in Chinese with English abstract)
[14]Yao Yuan,Wu Wei,Yang Tianle,et al.Head rice rate measurement based on concave point matching[J].Scientific Reports,2017,7:41353.
[15]Hobson D M,Carter R M,Yan Y.Rule based concave curvature segmentation for touching rice grains in binary digital images[C]//Instrumentation and Measurement Technology Conference,Singapore,Singapore,2009(5):1685-1689.
[16]Wei Lian,Lei Zhang.Point matching in the presence of outliers in both point sets:A concave optimization approach[J].Simulation Modelling Practice&Theory,2014,41(1):87-103.
[17]Song Hong,Zhao Qingjie,Liu Yinghong.Splitting touching cells based on concave-point and improved watershed algorithms[J].Frontiers of Computer Science,2014,8(1):156-162.
[18]谢忠红,姬长英,郭小清,等.基于凹点搜索的重叠果实定位检测算法研究[J].农业机械学报,2011,42(12):191-196.Xie Zhonghong,Ji Changying,Guo Xiaoqing,et al.Detection and location algorithm for overlapped fruits based on concave spots searching[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(12):191-196.(in Chinese with English abstract)
[19]曾庆兵,刘成良,苗玉彬,等.基于形态学图像处理的重叠葡萄果径无损测量[J].农业工程学报,2009,25(9):356-360.Zeng Qingbing,Liu Chengliang,Miao Yubin,et al.Non-destructive measurement of diameter of overlapping grape fruit based on morphological image processing[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2009,25(9):356-360.(in Chinese with English abstract)
[20]刘伟华,隋青美.基于凹点搜索的重叠粉体颗粒的自动分离算法[J].电子测量与仪器学报,2010,24(12):1095-1100.Liu Weihua,Sui Qingmei.Automatic segmentation of overlapping powder particle based on searching concavity points[J].Journal of Electronic Measurement and Instrument,2010,24(12):1095-1100.(in Chinese with English abstract)
[21]Bai X,Sun C,Zhou F,Splitting touching cells based on concave points and ellipse fitting[J].Pattern Recognition,2009,42(11):2434-2446.
[22]李擎,唐欢,迟健男,等.基于改进最大类间方差法的手势分割方法研究[J].自动化学报,2017,43(4):528-537.Li Qing,Tang Huan,Chi Jiannan,et al.Gesture segmentation with Improved maximum between-cluster variance Algorithm[J].Acta Automatic Sinica,2017,43(4):528-537.(in Chinese with English abstract)
[23]Kumar R,Pal M,Gulati T.Estimating corroded area of metallic surfaces using edge detection&hole filling[J].International Journal of Engineering Trends and Technology,2014,11(11):529-536.
[24]Gao H,Tham J Y,Xue P,et al.Complexity analysis of morphological area openings and closings with set union[J].IET Image Processing,2008,2(4):231-238.
[25]Rafael C.Gonzalez著,阮秋琦,等译.数字图像处理(第三版)[M].北京:电子工业出版社,2017.
[26]Bosworth J H,Acton S T.Morphological scale-space in image processing[J].Digital Signal Processing,2003,13(2):338-367.
[27]王小鹏,郝重阳,樊养余.基于形态学尺度空间和梯度修正的分水岭分割[J].电子与信息学报,2006,28(3):485-489.Wang Xiaopeng,Hao Chongyang,Fan Yangyu,Watershed segmentation technology based on morphological scale-space and Gradient modification[J].Journal of Electronics&Information,2006,28(3):485-489.(in Chinese with English abstract)
[28]Yan L,Park C W,Lee S R,et al.New separation algorithm for touching grain kernels based on contour segments and ellipse fitting[J].Frontiers of Information Technology&Electronic Engineering,2011,12(1):54-61.
[29]李宏辉,郝颖,吴清潇,等.基于凹点方向线的粘连药品图像分割方法[J].计算机应用研究,2013,30(9):2852-2854.Li Honghui,Hao Ying,Wu Qingxiao,et al.Based on concave point direction line adhesion drug image segmentation method[J].Application Research of Computers,2013,30(9):2852-2854.(in Chinese with English abstract)
[30]Zhong Q,Zhou P,Yao Q,et al.A novel segmentation algorithm for clustered slender-particles[J].Computers&Electronics in Agriculture,2009,69(2):118-127.
[2]李青,彭彦昆.基于机器视觉的猪胴体背膘厚度在线检测技术[J].农业工程学报,2015,31(9):256-261.Li Qing,Peng Yankun.Pork back fat thickness on-line detection methods using machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(9):256-261.(in Chinese with English abstract)
[3]陈红,夏青,左婷,等.破损花菇机器视觉检测技术[J].农业机械学报,2014,45(11):60-67.Chen Hong Xia Qing Zuo Ting,et al.Application of machine vision in detection of broken shiitake[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(11):60-67.(in Chinese with English abstract)
[4]彭江南,谢宗铭,杨丽明,等.基于Seed Identification软件的棉籽机器视觉快速精选[J].农业工程学报,2013,29(23):147-152.Peng Jiangnan,Xie Zongming,Yang Liming,et al.Quickly selection for cotton seed based on seed identification software[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(23):147-152.(in Chinese with English abstract)
[5]熊俊涛,邹湘军,刘念彭,等.基于机器视觉的荔枝果实采摘时品质检测技术[J].农业机械学报,2014,45(7):54-60.Xiong Juntao,Zou Xiangjun,Liu Nianpeng,et al.Fruit quality detection based on machine vision technology when picking litchi[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):54-60.(in Chinese with English abstract)
[6]卢军,付雪媛,苗晨琳.基于颜色和纹理特征的柑橘自动分级[J].华中农业大学报,2012,31(6):783-786.Lu Jun,Fu Xueyuan,Miao Chenlin,et al.Citrus automatic grading by using color and texture features[J].Journal of Huazhong Agricultural University,2012,31(6):783-786.(in Chinese with English abstract)
[7]李希,王天江,周鹏.一种改进的粘连颗粒图像分割算法[J].湖南大学学报:自然科学版,2012,39(12):84-88.Li Xi,Wang Tianjiang,Zhou Peng.An improved segmenting algorithm for touched particle image[J].Journal Hunan University:Natural Sciences,2012,39(12):84-88.(in Chinese with English abstract)
[8]杨华东,简淼夫.基于灰度形态重构的颗粒图像分割方法[J].南京工业大学学报:自然科学版,2005,27(3):98-102.Yang Huadong,Jian Miaofu.The segmentation algorithm of particle image based on grayscale morphological reconstruction[J].Journal of Nanjing University of Technology,2005,27(3):98-102.(in Chinese with English abstract)
[9]Nee Lim Huey,Mashor Mohd Yusoff,Hassan Rosline.White blood cell segmentation for acute leukemia bone marrow images[J].Journal of Medical Imaging&Health Informatics,2012,2(3):357-361..
[10]Quan L,Jiang E.Automatic segmentation method of touching corn kernels in digital image based on improved watershed algorithm[C]//International Conference on New Technology of Agricultural Engineering,Zibo,China,2011,5(27/28/29):34-37.
[11]王桂芹,王正勇,罗代升.基于FCM和标记分水岭的粘连岩石颗粒图像分割[J].四川大学学报:自然科学版,2012,49(2):356-360.Wang Guiqin,Wang Zhengyong,Luo Daisheng.Image segmentation of overlapping rock particles based on FCM and labeling watershed[J].Journal of Sichuan University:Natural Science Edition,2012,49(2):356-360.(in Chinese with English abstract)
[12]李文勇,李明,钱建平,等.基于形状因子和分割点定位的粘连害虫图像分割方法[J].农业工程学报,2015,31(5):175-180.Li Wenyong,Li Ming,Qian Jianping,et al.Segmentation method for touching pest images based on shape factor and separation points location[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):175-180.(in Chinese with English abstract)
[13]韦冬冬,赵豫红.基于凹点匹配的重叠图像分割算法[J].计算机与应用化学,2010,27(1):99-102.Wei Dongdong,Zhao Yuhong.An image segment algorithm for overlapped particles based on concave points matching[J].Computers and Applied Chemistry,2010,27(1):99-102.(in Chinese with English abstract)
[14]Yao Yuan,Wu Wei,Yang Tianle,et al.Head rice rate measurement based on concave point matching[J].Scientific Reports,2017,7:41353.
[15]Hobson D M,Carter R M,Yan Y.Rule based concave curvature segmentation for touching rice grains in binary digital images[C]//Instrumentation and Measurement Technology Conference,Singapore,Singapore,2009(5):1685-1689.
[16]Wei Lian,Lei Zhang.Point matching in the presence of outliers in both point sets:A concave optimization approach[J].Simulation Modelling Practice&Theory,2014,41(1):87-103.
[17]Song Hong,Zhao Qingjie,Liu Yinghong.Splitting touching cells based on concave-point and improved watershed algorithms[J].Frontiers of Computer Science,2014,8(1):156-162.
[18]谢忠红,姬长英,郭小清,等.基于凹点搜索的重叠果实定位检测算法研究[J].农业机械学报,2011,42(12):191-196.Xie Zhonghong,Ji Changying,Guo Xiaoqing,et al.Detection and location algorithm for overlapped fruits based on concave spots searching[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(12):191-196.(in Chinese with English abstract)
[19]曾庆兵,刘成良,苗玉彬,等.基于形态学图像处理的重叠葡萄果径无损测量[J].农业工程学报,2009,25(9):356-360.Zeng Qingbing,Liu Chengliang,Miao Yubin,et al.Non-destructive measurement of diameter of overlapping grape fruit based on morphological image processing[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2009,25(9):356-360.(in Chinese with English abstract)
[20]刘伟华,隋青美.基于凹点搜索的重叠粉体颗粒的自动分离算法[J].电子测量与仪器学报,2010,24(12):1095-1100.Liu Weihua,Sui Qingmei.Automatic segmentation of overlapping powder particle based on searching concavity points[J].Journal of Electronic Measurement and Instrument,2010,24(12):1095-1100.(in Chinese with English abstract)
[21]Bai X,Sun C,Zhou F,Splitting touching cells based on concave points and ellipse fitting[J].Pattern Recognition,2009,42(11):2434-2446.
[22]李擎,唐欢,迟健男,等.基于改进最大类间方差法的手势分割方法研究[J].自动化学报,2017,43(4):528-537.Li Qing,Tang Huan,Chi Jiannan,et al.Gesture segmentation with Improved maximum between-cluster variance Algorithm[J].Acta Automatic Sinica,2017,43(4):528-537.(in Chinese with English abstract)
[23]Kumar R,Pal M,Gulati T.Estimating corroded area of metallic surfaces using edge detection&hole filling[J].International Journal of Engineering Trends and Technology,2014,11(11):529-536.
[24]Gao H,Tham J Y,Xue P,et al.Complexity analysis of morphological area openings and closings with set union[J].IET Image Processing,2008,2(4):231-238.
[25]Rafael C.Gonzalez著,阮秋琦,等译.数字图像处理(第三版)[M].北京:电子工业出版社,2017.
[26]Bosworth J H,Acton S T.Morphological scale-space in image processing[J].Digital Signal Processing,2003,13(2):338-367.
[27]王小鹏,郝重阳,樊养余.基于形态学尺度空间和梯度修正的分水岭分割[J].电子与信息学报,2006,28(3):485-489.Wang Xiaopeng,Hao Chongyang,Fan Yangyu,Watershed segmentation technology based on morphological scale-space and Gradient modification[J].Journal of Electronics&Information,2006,28(3):485-489.(in Chinese with English abstract)
[28]Yan L,Park C W,Lee S R,et al.New separation algorithm for touching grain kernels based on contour segments and ellipse fitting[J].Frontiers of Information Technology&Electronic Engineering,2011,12(1):54-61.
[29]李宏辉,郝颖,吴清潇,等.基于凹点方向线的粘连药品图像分割方法[J].计算机应用研究,2013,30(9):2852-2854.Li Honghui,Hao Ying,Wu Qingxiao,et al.Based on concave point direction line adhesion drug image segmentation method[J].Application Research of Computers,2013,30(9):2852-2854.(in Chinese with English abstract)
[30]Zhong Q,Zhou P,Yao Q,et al.A novel segmentation algorithm for clustered slender-particles[J].Computers&Electronics in Agriculture,2009,69(2):118-127.