| 基于双轮廓同步跟踪的果树枝干提取及三维重建 |
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| 引用本文： | 贺磊盈,武传宇,杜小强.基于双轮廓同步跟踪的果树枝干提取及三维重建[J].农业工程学报,2014,30(7):182-189. |
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| 作者姓名： | 贺磊盈 武传宇 杜小强 |
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| 作者单位： | 1. 浙江理工大学机械与自动控制学院，杭州 310018;1. 浙江理工大学机械与自动控制学院，杭州 3100182. 浙江省种植装备技术重点实验室，杭州 310018;1. 浙江理工大学机械与自动控制学院，杭州 3100182. 浙江省种植装备技术重点实验室，杭州 310018 |
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| 基金项目： | 国家自然科学基金（51005215, 51175476）；浙江省自然科学基金（R1110502）；高等学校博士学科点专项科研基金（20113318110001）；浙江理工大学农业机械研究团队资助 |
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| 摘 要： | 自适应果实振动收获是利用机器视觉技术识别果树的几何参数,从而分析其动力学特性并用来自动调整振动设备的参数,达到高效低损伤的作业目的。该文以自然生长的无叶山核桃树为研究对象,根据果实自适应振动收获方式的需要,研究了一种基于双轮廓同步跟踪提取果树枝干并利用双目视觉技术进行三维重建的方法。首先结合自适应阈值分割算法和轮廓跟踪技术提取果树枝干区域,细化后得到枝干骨架并用二叉树结构描述。然后根据极线约束和拓扑结构建立双视图中树枝的对应关系。考虑到果树树枝形状的连续性,在三维重建过程中引入了曲率约束,从而提高了重建的效果。最后利用植物学的营养管道输送模型,结合线性回归方法参数化树枝半径。试验结果显示,重建的三维果树枝干形态与真实果树在视觉上很接近,估计的半径与测量半径之间的相对误差小于9%。该研究可为果树动力学模型的创建提供树体的3D结构参数,从而为果实的自适应振动收获技术提供参考。
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| 关 键 词： | 机器视觉 模型 线性回归 三维重建 轮廓跟踪 二叉树结构 曲率约束 |
| 收稿时间： | 2013/8/29 0:00:00 |
| 修稿时间： | 2014/2/18 0:00:00 |
Fruit tree extraction based on simultaneous tracking of two edges for 3D reconstruction |
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| He Leiying,Wu Chuanyu and Du Xiaoqiang.Fruit tree extraction based on simultaneous tracking of two edges for 3D reconstruction[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(7):182-189. |
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| Authors: | He Leiying Wu Chuanyu and Du Xiaoqiang |
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| Institution: | 1. School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China;1. School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China2. Zhejiang Province Key Laboratory of Transplanting Equipment and Technology, Hangzhou 310018, China;1. School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China2. Zhejiang Province Key Laboratory of Transplanting Equipment and Technology, Hangzhou 310018, China |
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| Abstract: | Abstract: In order to develop adaptive equipment for the mechanical harvest of fruit, a 3D model of a fruit tree was required for analyzing its dynamic properties which can help in tuning vibratory parameters. A Chinese hickory tree without leaves was selected for this study. The objective of this paper was to extract trunks of a fruit tree from images for 3D reconstruction based on a stereo vision with two images. All tree branches in an image were divided into two parts, up-side branches and down-side branches respectively, according to their different background. The up-side branches can be easily segmented by an auto-threshold binarization algorithm. The down-side branches were extracted by simultaneous tracking of the two edges. The branch in image can be treated as a series of scanning beams defined by two end points with a certain width. The objective of the tracking process was to find an optimal path from the seed scanning beam to the root scanning. At each tracking step, nm candidate scanning beams with certain costs were generated under edge constraints, and only n scanning beams with lowest cost were reserved for next tracking step. After successfully tracking, a branch can be extracted by filling its two edges obtained from the optimal path. Thinning the branch, a binary tree was employed to describe the topology structure of the tree. In view of the continuity of shape and width of the branch, intersection points and bifurcate points of the skeleton were repaired heuristically. Under the topology and epipolar constraints, branch correspondences in two images can be found easily. Likewise, for a pair of corresponding branches, points in one branch can be found by their corresponding ones in another branch by epipolar constraints. To improve the accuracy of a reconstructed 3D model of trunk, curve-based reconstruction with curvature constraint instead of point-based reconstruction was employed. Taking into consideration of the Pipe Model of botany, the radius of a real branch was regressed as a linear equation with the variable of its length. Finally, an example of reconstructed 3D model of tree created by Pro/E software was shown. The result demonstrated that the reconstructed 3D tree is realistic visually. However, some branches were not recovered due to heavy occlusion with others. To solve these problems, more images with different views of the tree are needed and a data-fusing algorithm should be provided to combine the obtained results. For a certain branch, a comparison of the estimated radiuses between the 3D reconstruction and the measurement was made, and the relative derivation was less than 9%. The main reason was the imprecise assumption that the real cross section of branch was not an absolute circle. |
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| Keywords: | computer vision models linear regression 3D reconstruction contour tracking binary tree curvature constraint |
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