Vegetation segmentation robust to illumination variations based on clustering and morphology modelling
Vegetation segmentation robust to illumination variations based on clustering and morphology modelling
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DOI：
10.1016/j.biosystemseng.2014.06.015
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发表时间：
2014-09-01
影响因子：
5.1
通讯作者：
Li, Cuina
中科院分区：
文献类型：
--
作者：
Bai, Xiaodong;Cao, Zhiguo;Li, Cuina
图像植被分割是计算机视觉在农业应用中的一个重要问题。在本文中，我们提出了一种基于粒子群优化（PSO）聚类和 CIE L*a*b* 颜色空间形态建模的新植被分割方法。在离线学习阶段，提出了一种新的聚类数确定方法。其次，利用形态膨胀和侵蚀工具建立植被颜色模型。在在线分割阶段，使用基于 PSO 的 k 均值将植被图像聚类为植被类和非植被类。然后，利用建立的颜色模型来区分植被类别并给出分割结果。在实验中，该方法被应用于水稻全相机图像的200个较小区域和棉花全相机图像的100个较小区域。分割质量均值分别达到88.1%和91.7%。此外，该方法与三种著名的植被分割方法和两种皮肤分割方法进行了比较。实验表明，所提出的方法产生了最高的分割质量平均值和最低的分割质量标准偏差。此外，还分析了使用不同结构元素类型构建的植被颜色模型。 (C) 2014 年 IAgrE。由爱思唯尔有限公司出版。保留所有权利。
Vegetation segmentation from images is an essential issue in the application of computer vision in agriculture. In this paper, we present a new vegetation segmentation method based on Particle Swarm Optimisation (PSO) clustering and morphology modelling in CIE L*a*b* colour space. At the off-line learning stage, a new method is put forward to determine the clustering number. Secondly, the tools of morphological dilation and erosion are employed to establish the vegetation colour model. At the online segmentation stage, the PSO-based k-means is used to cluster the vegetation image into vegetation classes and non-vegetation classes. Afterwards, the established colour model is used to distinguish the vegetation classes and give the segmentation result. In the experiments, the proposed method was applied to segment 200 smaller regions of the full camera images of rice and 100 smaller regions of the full camera images of cotton. The means of segmentation qualities reached 88.1% and 91.7% respectively. Moreover, the proposed method was compared with three well-known vegetation segmentation methods and two skin segmentation methods. Experiments demonstrate that the proposed method yielded the highest mean of segmentation qualities and lowest standard deviations of segmentation qualities. In addition, the vegetation colour models built with different structuring element types are analysed. (C) 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.