基于Kinect的三维玉米植株骨架提取

doi:10.16182/j.issn1004731x.joss.201703008

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (3): 524-530.doi: 10.16182/j.issn1004731x.joss.201703008

基于Kinect的三维玉米植株骨架提取

方志力^1,2, 温维亮¹, 郭新宇¹^※, 吴建伟³

1.北京农业信息技术研究中心/数字植物北京重点实验室/国家农业信息化工程技术研究中心/农业部农业信息技术重点实验室/北京农业物联网工程技术研究中心,北京 100097;
2.上海海洋大学信息学院,上海 201306;
3.北京派得伟业科技发展有限公司,北京 100097

收稿日期:2015-06-01 修回日期:2015-08-23 出版日期:2017-03-08 发布日期:2020-06-02
作者简介:方志力(1986-),男,河南周口,硕士,研究方向为计算机图形学、Kinect应用。
基金资助:
国家自然科学基金(31171454),国家863计划(2013AA102404),北京市科技计划(D15110000 4215004, Z151100003215008)

Skeleton Extraction from Three-dimensional Maize Based on Kinect

Fang Zhili^1,2, Wen Weiliang¹, Guo Xinyu¹^※, Wu Jianwei³

1. Beijing Research Center for Information Technology in Agriculture/ Beijing Key Lab of Digital Plant/ National Engineering Research Center for Information Technology in Agriculture/ Key Laboratory of Agri-information Ministry of Agriculture, P R China/ Beijing Agriculture Internet of Things Engineering Technology Research Center, Beijing 100097, China;
2. Shanghai Ocean University Information College,Shanghai 201306, China;
3. Beijing PAIDE Science and Technology Development Co. Ltd., Beijing 100097, China

Received:2015-06-01 Revised:2015-08-23 Online:2017-03-08 Published:2020-06-02

摘要/Abstract

摘要： 针对玉米植株三维骨架的快速获取问题,提出了一种基于Kinect自动获取玉米植株三维骨架的方法,采用由Kinect转换的点云数据,利用阈值分割的方法删除背景数据,根据玉米植株三维点云模型的高度分层,对每层点组采用二次循环迭代方法聚类,对每层中各子类求取中心点。中心点的集合即认为是玉米植株的骨架点。采用二次B样条曲线插值拟合连线。实验结果表明：方法获得的玉米植株三维线性骨架与通过三维数字化仪获得的三维线性骨架一致性较好,能够满足实验要求。

关键词: Kinect, 三维点云, 玉米骨架, 聚类分析, 二次B样条曲线

Abstract: Aiming at acquiring three-dimensional skeleton of maize plant efficiently, an automatic three-dimensional maize plant skeleton extraction method based on Kinect data was proposed. Kinect data was transformed into point cloud data. Backgroundpoint cloud was deleted by threshold segmentation and the point cloud was stratified according to the height of the 3D model. Each layer was clustered by two circle iteration and the center points of each cluster could be obtained. The set of center points could be considered as the skeleton points of maize. The quadratic B-spline interpolation curve was used to obtain relatively accurate and smooth linear skeleton of maize plant. Experimental result shows that the 3D maize plant linear skeleton obtained by this method has a good consistency of 3D digitizer skeleton and can meet the requirements of rapid experimental data acquirement.

Key words: Kinect, three-dimensional point cloud, maizeskeleton, cluster, quadratic B-spline

中图分类号:

TP391.41

方志力, 温维亮, 郭新宇, 吴建伟. 基于Kinect的三维玉米植株骨架提取[J]. 系统仿真学报, 2017, 29(3): 524-530.

Fang Zhili, Wen Weiliang, Guo Xinyu, Wu Jianwei. Skeleton Extraction from Three-dimensional Maize Based on Kinect[J]. Journal of System Simulation, 2017, 29(3): 524-530.

参考文献

[1] 赵春江, 杨亮, 郭新宇, 等. 基于立体视觉的玉米植株三维骨架重建[J]. 农业机械学报, 2010, 41(4), 157-162.
(Zhao C, Yang L, Guo X, et al.Corn Skeleton Reconstruction Base on Stereo Vision[J]. Transactions of The Chinese Society of Agricultural Machinery, 2010, 41(4): 157-162.)
[2] Su Z, Zhao Y, Zhao C, et al.Skeleton extraction for tree models[J]. Mathematical and computer modeling (S0895-7177), 2011, 54(3/4): 1115-1120.
[3] Jaehwan M, Sunghee C.Kinematic skeleton extraction from 3D articulated models[J]. Computer-Aided Design (S0010-4485), 2014, 46(1): 221-226.
[4] Nag M, Szekely G, Kilinis R, et al.3D Voronoi Skeletons and Their Usage for the Characterization and Recognition of 3D Organ Shape[J]. Computer Vision and Image Understanding (S1077-3142), 1997, 66(2): 147-161.
[5] Guo X, Lu S, Wen W, et al.Parametrical keyframe animation for maize growth simulation[C]// World Automation Congress (WAC). USA: IEEE, 2010: 233-238.
[6] 赵春江, 王功明, 郭新宇, 等. 基于交互式骨架模型的玉米根系三维可视化研究[J]. 农业工程学报. 2007, 23(9): 1-6.
(Zhao C, Wang G, Guo X, et al.3D visualization of corn root system based on interactive framework model[J]. Transactions of the Chinese Society of Agricultural, 2007, 23(9): 1-6.)
[7] 伍艳莲, 汤亮, 曹卫星, 等. 骨架驱动的作物叶片变形模拟[J]. 计算机工程与应用, 2011, 47(13): 11-14.
(Wu Y, Tang L, Cao W, et al.Corp leaf deformation driven by skeleton[J]. Computer Engineering and Application, 2011, 47(13): 11-14.)
[8] Heickal H, Zhang T, Hasanuzzaman M.Computer Vision-Based Real-Time 3D Gesture Recognition Using Depth Image[J]. International Journal of Image and Graphics (S0219-4678), 2015, 15(1): 1-6.
[9] Chunk P, Andrew W.Using the Xbox Kinect^TM sensor for gesture recognition[J]. Journal of Computing Sciences in Colleges (S1937-4771), 2013, 28(5): 226-227.
[10] 古月. 使用Kinect生成点云数据 [EB/OL] (2013-03-09) [2014-12-23] http://blog.csdn.net/hcx25909 /article/details/8654684
[11] Athma B, Yu Q, Liu X, et al.Efficient agglomerative hierarchical clustering[J]. Expert Systems with Applications (S0957-4174), 2015, 42(5): 2785-2797.
[12] Wan Y, Yin S.Three-dimensional curve fitting based on cubic B-spline interpolation curve[C]// Image and Signal Processing (CISP), 2014 7th International Congress on. USA: IEEE, 2014: 765-770.
[13] Talebitooti R, Shojaeefard M.Shape design optimization of cylindrical tank using b-spline curves[J]. Computers and Fluids (S0045-7930), 2015, 109: 100-112.
[14] Lin H, Xiong Y, Liao H.Semi-structured B -spline for blending two B-spline surfaces[J]. Computers and Mathematics with Applications (S0898-1221), 2014, 68(7): 706-718.

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基于Kinect的三维玉米植株骨架提取

Skeleton Extraction from Three-dimensional Maize Based on Kinect

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