Abstract
Understanding the point clouds of plants is crucial for the plant phenotyping. However, it is challenging due to a number of factors such as complicated structures and imaging noise. The primary objective of this project is to simplify the complicated 3D structure of the plant point cloud data into 1D curved skeleton. The simplified skeleton will be helpful for the structural analysis and understanding of plants of interest and the measurements of their traits such as the areas, perimeters of leaves, curvatures, and the lengths between different
nodes. To this end, we propose a novel method to voxelize the given plant point cloud, extract the skeleton voxels, and find the nearest neighbors to connect the skeleton points as a connected representation. A number of different plant
point clouds are used to validate and compare the proposed voxelization thinning method with a state-of-the-art one. Better results have been obtained.
nodes. To this end, we propose a novel method to voxelize the given plant point cloud, extract the skeleton voxels, and find the nearest neighbors to connect the skeleton points as a connected representation. A number of different plant
point clouds are used to validate and compare the proposed voxelization thinning method with a state-of-the-art one. Better results have been obtained.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 23rd European Signal Processing Conference (EUSIPCO) |
| Place of Publication | Nice |
| Publisher | European Association for Signal Processing |
| Pages | 2686-2690 |
| Number of pages | 5 |
| ISBN (Print) | 9780992862633 |
| Publication status | Published - 2015 |