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当前位置: 首页 > 文章 > 植株点云超体聚类分割方法 农业机械学报 2018 (12) 172-179
Position: Home > Articles > Segmentation Method of Supervoxel Clusterings and Salient Map Transactions of the Chinese Society for Agricultural Machinery 2018 (12) 172-179

植株点云超体聚类分割方法

作  者：
刘慧;刘加林;沈跃;潘成凯
单  位：
江苏大学电气信息工程学院
关键词：
三维点云;Kinect;显著性特征图;超体素;Nanoflann;深度信息
摘  要：
针对传统的超体聚类分割对植株存在过分割率高、实时性差的问题,提出一种融合显著性特征图的超体聚类分割方法。首先,采用Kinect V2实时获取目标植株的彩色图像和深度图像,将RGB彩色空间图像转换为CIELab彩色空间图像,计算每个像素的显著性特征值,获取彩色特征图,并融合亮度特征图和方向特征图构建显著性特征图;然后,将显著性特征图和深度图像同步对齐,获得显著性点云,八叉树网格初始化点云,并通过Mean-Shift算法获取满足概率密度阈值的网格点云,取最大概率密度点作为种子点,基于点对之间的欧氏距离和特征相似度作为区域生长相似性准则,生成超体素块;最后,通过LCCP算法对显著性点云进行聚类分割。实验结果表明,改进的显著性超体聚类分割方法可以大幅提高目标前景分割的准确性和快速性,有效克服背景噪声和离群点。
译  名：
Segmentation Method of Supervoxel Clusterings and Salient Map
作  者：
LIU Hui;LIU Jialin;SHEN Yue;PAN Chengkai;School of Electrical and Information Engineering,Jiangsu University;
单  位：
LIU Hui%LIU Jialin%SHEN Yue%PAN Chengkai%School of Electrical and Information Engineering,Jiangsu University
关键词：
3D point cloud;;Kinect;;salient map;;supervoxel;;Nanoflann;;depth information
摘  要：
The image segmentation of target plant plays an important role in the automation of plant target detection and variable spray. The application of a single two-dimensional feature to object orientation,tracing and other occasions cannot meet the requirements of modern agriculture. However,in the segmentation of the three dimensional characteristics of plants,the traditional supervoxel clustering segmentation has the problem of high segmentation rate and poor real-time performance of plant. To solve this problem,a super voxel segmentation method was proposed,which fused saliency maps. Firstly,the color and depth maps of target plant were acquired in real time by using Kinect V2,and the RGB(RGB color model) color space images were converted into CIELab(CIELab color model) color space images.The eigenvalues of each pixel were calculated,and then the color feature map was obtained. After obtaining three feature graphs,fusion color feature graph,luminance feature graph and direction feature graph were used to construct a significant feature graph,and then the saliency map and the depth map were synchronously aligned to obtain the significant point cloud. The octree grid was used to initialize point cloud,and the grid point cloud was obtained,which satisfied the probability density threshold through Mean-Shift algorithm,and taking the maximum probability density point as the seed point,based on the Euclidean distance between points and CIELab similarity criterion as regional growth,the super voxels were generated. Finally,the locally convex connected patches(LCCP) algorithm was used to cluster the salient point cloud. The experimental results showed that the improved supervoxels based on salient point cloud-locally convex connected patches(SSV-LCCP) algorithm method can greatly improve the accuracy and rapidity of the target foreground segmentation,and effectively overcome the background noise and outliers.

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