基于图像特征的小麦胚芽鞘识别
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摘要
利用图像颜色特征,首先分割小麦种子图像进而确定小麦种子轮廓矩,根据其轮廓距确定小麦种子质心坐标,然后根据小麦胚芽鞘图像颜色特征对胚芽鞘图像进行分割、获取小麦胚芽鞘图像,其次利用Zhang-Suen并行快速细化算法对小麦胚芽鞘进行细化获取胚芽鞘骨骼线,进而获取骨骼线图像(单像素)上所有点对胚芽鞘骨骼线进行多段直线曲线近似,最后根据小麦种子轮廓质心坐标、胚芽鞘骨骼线近似曲线和切割距离(给定)确定胚芽鞘的姿态和对小麦胚芽鞘切割点位置进行定位。通过对小麦胚芽鞘30幅图片进行图像处理验证。结果表明,该方法能完整地提取小麦种子和胚芽鞘图像、小麦胚芽鞘姿态及位置信息。基于图像颜色特征的小麦胚芽鞘识别及定位方法,为小麦胚芽鞘的识别与分析提供了准确、快捷、可视的技术手段,对于构建胚芽鞘智能识别、定位的视觉系统及自动化切割装置的研究意义重大。
Using color features of the image,wheat seed image was firstly divided and wheat seed contour moment was determined. The centroid coordinates of wheat seed were determined according to the contour distance. Then the coleoptile image was divided according to color image of wheat coleoptile image to obtain wheat coleoptile. Followed by Zhang-Suen parallel refinement algorithm,wheat coleoptile was refined to obtain the coleoptile skeletal line,and then obtain all the points on the skeletal line image( single pixel) to perform a multi-segment linear curve approximation of the coleoptile skeletal line. Finally,orientation of the coleoptile and position of the cut point of wheat coleoptile were determined based on centroid coordinates of wheat seed outline,approximate curve of the coleoptile skeletal line,and cutting distance( given). Image processing of 30 pairs of wheat coleoptiles was verified by image processing. The results showed that this method could accurately segment wheat coleoptile image,quickly identify wheat coleoptile,attitude and other information. Identification and localization of wheat coleoptile based on image color features provided an accurate,rapid and visual method for the identification,extraction and analysis of wheat coleoptile,and it had great significance for the construction of a visual system and automatic recognition of germ sheath.
Using color features of the image,wheat seed image was firstly divided and wheat seed contour moment was determined. The centroid coordinates of wheat seed were determined according to the contour distance. Then the coleoptile image was divided according to color image of wheat coleoptile image to obtain wheat coleoptile. Followed by Zhang-Suen parallel refinement algorithm,wheat coleoptile was refined to obtain the coleoptile skeletal line,and then obtain all the points on the skeletal line image( single pixel) to perform a multi-segment linear curve approximation of the coleoptile skeletal line. Finally,orientation of the coleoptile and position of the cut point of wheat coleoptile were determined based on centroid coordinates of wheat seed outline,approximate curve of the coleoptile skeletal line,and cutting distance( given). Image processing of 30 pairs of wheat coleoptiles was verified by image processing. The results showed that this method could accurately segment wheat coleoptile image,quickly identify wheat coleoptile,attitude and other information. Identification and localization of wheat coleoptile based on image color features provided an accurate,rapid and visual method for the identification,extraction and analysis of wheat coleoptile,and it had great significance for the construction of a visual system and automatic recognition of germ sheath.
引文
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[16]王娜,王克如,谢瑞芝,等.基于Fisher判别分析的玉米叶部病害图像识别[J].中国农业科学,2009,42(11):3836-3842.WANG N,WANG K R,XIE R Z,et al.Maize leaf disease identification based on Fisher discrimination analysis[J].Scientia Agricultura Sinica,2009,42(11):3836-3842.(in Chinese with English abstract)
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[18]兰红,施启乐,王从军,等.数学形态学图像细化算法在RE中的应用研究[J].华中科技大学学报(自然科学版),2004,32(7):37-39.LAN H,SHI Q L,WANG C J,et al.The application of image thinning algorithm to reverse engineering based on mathematical morphology[J].Journal of Huazhong University of Science and Technology(Nature Science Edition),2004,32(7):37-39.(in Chinese with English abstract)
[19]刘惠敏,夏琳琳.基于两种数学形态学算法优势互补的图像细化方法[J].自动化技术与应用,2011,30(10):67-71.LIU H M,XIA L L.Method for image thinning based on composite algorithm of mathematical morphology[J].Techniques of Automation and Applications,2011,30(10):67-71.(in Chinese with English abstract)
[20]刘志敏,杨杰,施鹏飞.数学形态学的细化算法[J].上海交通大学学报,1998,32(9):15-19.LIU Z M,YANG J,SHI P F.Thinning algorithm based on mathematical morphology[J].Journal of Shanghai Jiaotong University,1998,32(9):15-19.(in Chinese with English abstract)
[21]蔡式东,杨芳.一种基于HSV空间和粗糙集的彩色图像分割方法[J].光电子技术,2011,31(1):5-9.CAI S D,YANG F.Color image segmentation based on HSVspace and rough-set theory[J].Optoelectronic Technology,2011,31(1):5-9.(in Chinese with English abstract)
[22]张铮.数字图像处理与机器视觉[M].北京:人民邮电出版社,2014.
[23]ZHANG T Y,SUEN C Y.A fast parallel algorithm for thinning digital patterns[J].Communications of the ACM,1984,27(3):236-239.
[24]HU M K.Visual pattern recognition by moment invariants[J].Information Theory,IRE Transactions on,1962,8(2):179-187.
[25]李秀军,梁爽.以玉米胚芽鞘为材料的“生长素的发现”系列实验[J].生物学通报,2016,51(9):52-54.LI X J,LIANG S.A series of experiments on the discovery of auxin using corn coleoptile as materials[J].Bulletin of Biology,2016,51(9):52-54.(in Chinese with English abstract)
[2]任守纲,马超,谢忠红,等.基于分水岭和梯度的蝴蝶兰图像分割方法[J].农业工程学报,2012,28(9):125-129.REN S G,MA C,XIE Z H,et al.Image segmentation algorithm for Phalaenopsis amabilis based on watershed algorithm and gradient[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(9):125-129.(in Chinese with English abstract)
[3]CHEN M,LUDWIG S A.Color image segmentation using fuzzy C-regression model[J].Advances in Fuzzy Systems,2017(2):4582948.
[4]ANANTH C,SENTHILKANI A S,GOMATHY S K,et al.Color image segmentation using IMOWT with 2D histogram grouping[J].Social Science Electronic Publishing,2017,35:463-468.
[5]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010,41(9):148-153.SI Y S,QIAO J,LIU G,et al.Recognition and location of fruits for apple harvesting robot[J].Transactions of the Chinese Society of Agricultural Machinery,2010,41(9):148-153.(in Chinese with English abstract)
[6]宋健,王凯,张晓琛.茄子采摘机器人目标识别与测距方法[J].实验室研究与探索,2015,34(9):54-57.SONG J,WANG K,ZHANG X C.A target recognition and ranging method for eggplant picking robot[J].Research and Exploration in Laboratory,2015,34(9):54-57.(in Chinese with English abstract)
[7]WANG K,ZHANG D,LI Y,et al.Cost-effective active learning for deep image classification[J].IEEE Transactions on Circuits&Systems for Video Technology,2017,27(12):2591-2600.
[8]ZHAO Z,JIAO L,ZHAO J,et al.Discriminant deep belief network for high-resolution SAR image classification[J].Pattern Recognition,2017,61:686-701.
[9]张浩,陈勇,汪巍,等.基于主动计算机视觉的茶叶采摘定位技术[J].农业机械学报,2014,45(9):61-65.ZHANG H,CHEN Y,WANG W,et al.Positioning method for tea picking using active computer vision[J].Transactions of the Chinese Society of Agricultural Machinery,2014,45(9):61-65.(in Chinese with English abstract)
[10]张铁中,陈利兵,宋健.草莓采摘机器人的研究:Ⅱ.基于图像的草莓重心位置和采摘点的确定[J].中国农业大学学报,2005,10(1):48-51.ZHANG T Z,CHEN L B,SONG J.Study on strawberry harvesting robot:Ⅱ.Images based identifications of strawberry barycenter and plucking position[J].Journal of China Agricultural University,2005,10(1):48-51.(in Chinese with English abstract)
[11]QIAN X,ZHAO Y,HAN J.Image location estimation by salient region matching[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2015,24(11):4348-4358.
[12]王政,张微微,张宾,等.基于聚类算法对象提取的快速定位采摘机器人设计[J].农机化研究,2016,38(7):45-49.WANG Z,ZHANG W W,ZHANG B,et al.Design for fast locating and picking robot based on clustering algorithm and object extraction[J].Journal of Agricultural Mechanization Research,2016,38(7):45-49.(in Chinese with English abstract)
[13]周海峰,朱云霞.改进主动形状模型彩色图像目标定位研究[J].计算机仿真,2012,29(9):308-311.ZHOU H F,ZHU Y X.Research on image targeting algorithm based on improved grey model and HSV color model[J].Computer Simulation,2012,29(9):308-311.(in Chinese with English abstract)
[14]唐俊,邓立苗,陈辉,等.基于机器视觉的玉米叶片透射图像特征识别研究[J].中国农业科学,2014,47(3):431-440.TANG J,DENG L M,CHEN H,et al.Research on maize leaf recognition of characteristics from transmission image based on machine vision[J].Scientia Agricultura Sinica,2014,47(3):431-440.(in Chinese with English abstract)
[15]何胜美,李仲来,何中虎.基于图像识别的小麦品种分类研究[J].中国农业科学,2005,38(9):1869-1875.HE S M,LI Z L,HE Z H.Classification of wheat cultivar by digital image analysis[J].Scientia Agricultura Sinica,2005,38(9):1869-1875.(in Chinese with English abstract)
[16]王娜,王克如,谢瑞芝,等.基于Fisher判别分析的玉米叶部病害图像识别[J].中国农业科学,2009,42(11):3836-3842.WANG N,WANG K R,XIE R Z,et al.Maize leaf disease identification based on Fisher discrimination analysis[J].Scientia Agricultura Sinica,2009,42(11):3836-3842.(in Chinese with English abstract)
[17]钱建平,杨信廷,吴晓明,等.自然场景下基于混合颜色空间的成熟期苹果识别方法[J].农业工程学报,2012,28(17):137-142.QIAN J P,YANG X T,WU X M,et al.Mature apple recognition based on hybrid color space in natural scene[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(17):137-142.(in Chinese with English abstract)
[18]兰红,施启乐,王从军,等.数学形态学图像细化算法在RE中的应用研究[J].华中科技大学学报(自然科学版),2004,32(7):37-39.LAN H,SHI Q L,WANG C J,et al.The application of image thinning algorithm to reverse engineering based on mathematical morphology[J].Journal of Huazhong University of Science and Technology(Nature Science Edition),2004,32(7):37-39.(in Chinese with English abstract)
[19]刘惠敏,夏琳琳.基于两种数学形态学算法优势互补的图像细化方法[J].自动化技术与应用,2011,30(10):67-71.LIU H M,XIA L L.Method for image thinning based on composite algorithm of mathematical morphology[J].Techniques of Automation and Applications,2011,30(10):67-71.(in Chinese with English abstract)
[20]刘志敏,杨杰,施鹏飞.数学形态学的细化算法[J].上海交通大学学报,1998,32(9):15-19.LIU Z M,YANG J,SHI P F.Thinning algorithm based on mathematical morphology[J].Journal of Shanghai Jiaotong University,1998,32(9):15-19.(in Chinese with English abstract)
[21]蔡式东,杨芳.一种基于HSV空间和粗糙集的彩色图像分割方法[J].光电子技术,2011,31(1):5-9.CAI S D,YANG F.Color image segmentation based on HSVspace and rough-set theory[J].Optoelectronic Technology,2011,31(1):5-9.(in Chinese with English abstract)
[22]张铮.数字图像处理与机器视觉[M].北京:人民邮电出版社,2014.
[23]ZHANG T Y,SUEN C Y.A fast parallel algorithm for thinning digital patterns[J].Communications of the ACM,1984,27(3):236-239.
[24]HU M K.Visual pattern recognition by moment invariants[J].Information Theory,IRE Transactions on,1962,8(2):179-187.
[25]李秀军,梁爽.以玉米胚芽鞘为材料的“生长素的发现”系列实验[J].生物学通报,2016,51(9):52-54.LI X J,LIANG S.A series of experiments on the discovery of auxin using corn coleoptile as materials[J].Bulletin of Biology,2016,51(9):52-54.(in Chinese with English abstract)