Agriculture extra-green image segmentation based on particle swarm optimization and k-means clustering

被引：0
|
作者：
Zhao, Bo [1 ]
Song, Zhenghe [2 ]
Mao, Wenhua [1 ]
Mao, Enrong [2 ]
Zhang, Xiaochao [1 ]
机构：
[1] Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
[2] College of Engineering, China Agricultural University, Beijing 100083, China
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2009年 / 40卷 / 08期
关键词：
Image enhancement - Agriculture - Particle swarm optimization (PSO);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In order to solve the disadvantage of image segmentation by K-means clustering to extra-green character used to be adopted in agricultural images, an image segmentation method based on the particle swarm optimization and the K-means clustering was proposed. Firstly, image pixels value was fast clustered with the K-means clustering. Regarding the results as the position of a particle, PSO can be used and the new class centers also can be re-calculated with the K-means clustering. Subsequently, the position of all particles got updated and the optimal threshold was obtained. Experimental results proved that the improved algorithm was an effective method for segmenting the object accurately from images, and applicable for various kinds of agricultural images with extra-green character.
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收藏
页码：166 / 169
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