基于轮廓分割的草莓叶片三维建模

为精确构建原位草莓植株三维空间结构,以高架栽培模式生长环境下的草莓植株为研究对象,提出了一种基于多源图像轮廓分割的草莓植株结构形态三维重建算法。通过改进的多源图像融合算法,建立多源图像映射关系,融合预处理后的多源信息得到待分割强度图;计算待分割强度图矢量场卷积的局部中心,选出多目标的初始轮廓控制点,将参数的活动轮廓模型应用于待分割强度图像进行叶片的分割;采用标记的方法将分割轮廓映射至距离点云集,设计以单个叶片为单位的平面拟合选择机制,最终完成草莓三维模型的重建及显示。为验证该算法的有效性,将三维重建后的有效叶片数,平均单叶长度及叶片距离差作为评价指标,实验结果表明,有效叶片数正确率为85.6%,平均单叶长度模型正确计算率为88.4%,叶片距离差正确计算率为82.4%,研究结果可应用于原位草莓植株的空间位置测量,可为农业机器人局部视觉场景中植株空间结构的构建提供参考。

3D reconstruction of strawberry leaves based on contour segmentation

Abstract: Based on the data measured by instruments, the plant three-dimensional reconstruction is an important part of the plant digital research. Through the establishment of the plant three-dimensional model, not only can the growth rule of the plants in real environment be researched by measuring the crop parameters quickly, but also the spatial structure of local visual scene of plant can be explored by analyzing plant leaf distribution feature. In order to construct three-dimensional structure of situ strawberry plants precisely for further study in plant spatial structure, the paper took strawberry plants of elevated cultivation environment as the research object and proposed a three-dimensional strawberry canopy morphology reconstruction algorithm based on multi-source image contour segmentation. We divided the main algorithm into 3 parts: multi-source image pre-processing, coarse segmentation of intensity image and model fitting. To make full use of the advantages of color images in color segmentation, color image and intensity image in different resolution are registered and merged. The paper used the improved multi-source information fusion algorithm of strawberry plants based on feature. By using feature-based multi-source information fusion algorithm, feature information of each source image is extracted and analyzed. Then the invariable feature points are selected. Later by checking the similarity of the feature points and adding appropriate parameter constraints, the registration information is obtained. Multi-source image mapping relationship is established by applying registration information. Then by fusing the preprocessed images, the information complement of color image and intensity map is realized and finally the intensity image to be split is obtained by the image preprocessing. Calculating local center of the vector field of intensity image to be segmented means calculating vector field for each pixel. Then the largest local pixels are picked out. Later the vector direction of each pixel is divided into 3 categories by a symbolic function. Clustering potential scattering point set in an array and the local control point are determined by applying a given threshold. Finally by applying the active contour model of the parameters and the central control point to the segmented intensity image we get a coarse segmentation image of the blade. A method of model reconstruction based on surface fitting was proposed for further processing. Intensity image''s segmentation contour is regard as the edge contour, and the contour interior point cloud is extracted. The method of region marking is used to mark the used point cloud which belongs to original depth point cloud data, and by checking the number of unmarked point clouds we can know whether the extraction is completed. The plane fitting selection mechanism based on point cloud is designed to compare the minimum mean square deviation of the surface and the plane after fitting, and the optimal fitting model is selected. All the optimal models are displayed in a coordinate system, and the points are colored one by one. Finally, the reconstruction and display of the three-dimensional model of strawberry are finished. To verify the effectiveness of the algorithm, the paper took the distance difference between average single-leaf length and leaf distance as an evaluation index. Experimental results showed that the correction rate of number of blades was 85.6%, that of single-leaf model was 88.4% and that of distance difference was 82.4%. The results can be applied to the spatial position measurement of in situ strawberry plants. The research provides a new method for the construction of plant spatial structure in local vision scenes of agricultural robots.