Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.
Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.
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DOI：
10.1186/s12859-015-0665-2
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发表时间：
2015-08-08
影响因子：
3
通讯作者：
Mahlein AK
中科院分区：
文献类型：
--
作者：
Wahabzada M;Paulus S;Kersting K;Mahlein AK
从三维点云数据中分割植物器官是植物表型分析和植物生长观测的重要任务。需要自动化解决方案来提高最近高通量植物表型分析管道的效率。然而，植物的几何特性随时间、观测尺度和不同植物类型而变化。本研究的主要目标是开发一种全自动、快速、可靠的数据驱动的植物器官分割方法。在目标是快速了解数据或没有标记数据可用或实现成本高昂的情况下，使用无监督聚类方法自动分割植物器官至关重要。为此，我们提出并比较了数据驱动的方法，这些方法易于实现，并且可以使用标准算法。由于归一化的直方图，从三维点云，可以被看作是从概率单纯形的样本，我们建议从单纯形空间映射到欧氏空间使用Aitchisons对数比变换的数据，或到单位球的正象限使用平方根变换。这反过来又为广泛的常用分析技术铺平了道路，这些技术基于使用欧几里得距离测量数据点之间的相似性。我们调查的实际情况下，分组的三维点云和经验表明，他们导致高精度的单子叶和双子叶植物物种与不同的拍摄架构的聚类结果的方法的性能。一个自动分割的三维点云演示在目前的工作。在几秒钟内，对工厂数据的第一次洞察可能会发生偏差-即使是来自非标记数据。该方法适用于不同的植物种类，具有较高的精度。该分析级联可以在未来的高通量表型分型方案中实施，并将支持对暴露于压力或不同环境方案中的不同植物基因型的性能进行评估。本文的在线版本（doi：10.1186/s12859-015-0665-2）包含补充材料，可供授权用户使用。
Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated – even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios. The online version of this article (doi:10.1186/s12859-015-0665-2) contains supplementary material, which is available to authorized users.