自适应加权算子结合主曲线提取玉米叶片点云骨架  

Point cloud skeleton extraction of maize leaves based on adaptive weighting operator and principal curve

作　　者：胡建平[1] 刘凯 郭新宇[2,3,4] 吴升 温维亮[2,3,4] Hu Jianping;Liu Kai;Guo Xinyu;Wu Sheng;Wen Weiliang(College of Science,Northeast Electric Power University,Jilin 132012,China;Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;Beijing Key Laboratory of Digital Plant,Beijing 100097,China)

机构地区：[1]东北电力大学理学院,吉林132012 [2]北京市农林科学院信息技术研究中心,北京100097 [3]国家农业信息化工程技术研究中心,北京100097 [4]数字植物北京市重点实验室,北京100097

出　　处：《农业工程学报》2022年第2期166-174,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：北京市农林科学院创新能力建设专项(KJCX20210413);吉林省教育厅科学技术研究项目(JJKH20210098KJ);国家自然科学基金(32071891,61672149);财政部和农业农村部:国家现代农业产业技术体系资助。

摘　　要：为从玉米叶片点云数据(尤其是缺失点云数据)中准确提取骨架特征,该研究设计了一种考虑玉米叶片形状结构和数据完整性的自适应加权算子来计算玉米叶片点云的骨架约束点集,并引入主曲线对骨架约束点集进行拟合得到优化的玉米叶片点云骨架。首先使用K均值聚类将玉米叶片分为若干变化较为平缓的部分;然后通过改进的距离场方法提取点云的截面相关点集;再利用设计的包括空间距离、法向差异和点云完整性的自适应加权算子,提取骨架约束点集;最后计算骨架约束点集的主曲线得到最终的玉米叶片点云骨架。通过对30个具有典型形状特征的玉米叶片点云数据的测试结果表明,本文方法所提取的骨架能较好地反映玉米叶片的三维形状结构,利用所提取骨架计算的叶长与实测叶长的平均绝对百分比误差(Mean Absolute Percent Error,MAPE)为2.10%,均方根误差(Root Mean Square Error,RMSE)为2.21 cm,标准化均方根误差(Normalized Root Mean Square Error,NRMSE)为2.89%。该方法可实现玉米叶片点云骨架的自动提取,对缺失的点云数据具有较好的鲁棒性,无需后期手动调整,能够为表型大数据处理、自动化表型解析等提供技术支持。Three-dimensional(3D)skeleton is one of the most important representations of plant leaf morphology.Accurate extraction of leaf skeleton from 3D point clouds has been an essential way to obtain 3D plant phenotypes in recent years.In this study,a novel adaptive weighting operator was presented to calculate the point set of skeleton constraints from the 3D point clouds of maize leaf,particularly considering the leaf shape and data completeness.A principal curve was also selected to fit the skeleton constraint point set for a better skeleton.As such,it was likely to accurately extract the skeleton features from the point clouds of maize leaf,especially for the missing point cloud data.The extraction was composed of four steps.1)An input point cloud of maize leaf was generally divided into 3 or 4 parts by the classical K-means clustering for the normal of the input point cloud,where the shape of each part changed slowly.2)Each part of the point cloud was then reordered and segmented into many cross sections.A distance field was estimated to realize the segmentation,where the distance field was calculated by the distance from each point to the orthogonal plane perpendicular to the direction of leaf elongation.Such a distance field was also more efficient and accurate than the Euclidean distance.3)A novel adaptive weighting operator was applied to the related point set of each cross section,in order to extract the skeleton constraint points.The adaptive weighting operator included the weight of spatial distance,normal difference,and point cloud completeness.Specifically,the weight of spatial distance was used to quantitatively describe the sparsity of the point cloud.The weight of normal difference was used to rearrange the spatial positions of constraint points,according to the difference between the normal of the point and the orientation of the leaf.The weight of point cloud completeness was to measure the missing level of leaf points.4)A skeleton of the input point cloud was obtained to compute the principal curve

关 键 词：三维 植物 提取 玉米叶片 骨架 主曲线 自适应加权算子 

分 类 号：S126[农业科学—农业基础科学]