Point Cloud Segmentation Method of Maize Ear

doi:10.16182/j.issn1004731x.joss.201712013

Abstract

Abstract: Three-dimensional (3D) segmentation of maize ear is the fundamental research of 3D ear phenotype. A method of 3D segmentation for maize ear was proposed in this paper. Firstly, 3D point cloud of maize ear was acquired by using SmartScan 3D scanner. Then the point cloud was simplified by point cloud resampling to improve the efficiency of subsequent algorithm. And a contraction transformation was employed to increase the distance of neighboring grains in Euclidean space by estimating the normal of each point through k-neighbor algorithm. Finally, the point cloud of maize ear was segmented by Euclidean clustering. Results showed that the segmentation rate of grains could achieve more than 90%. Our method could provide technology support for the research of 3D ear phenotype analysis.

Key words: maize, point cloud segmentation, ear analysis, Euclidean clustering

CLC Number:

TP391.4

Wen Weiliang, Guo Xinyu, Yang Tao, Zhao Deda, Miao Teng, Zhu Hongyu, Dong Chengyu. Point Cloud Segmentation Method of Maize Ear[J]. Journal of System Simulation, 2017, 29(12): 3030-3035.

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