计算机工程与应用Issue(21)：226-229,4.DOI:10.3778/j.issn.1002-8331.1303-0397
粒子群与K均值混合聚类的棉花图像分割算法
Image segmentation algorithm of cotton based on PSO and K-means hybrid clustering
摘要
Abstract
Image segmentation of cotton is the key step of the cotton picker robot vision system. In the complex environment of the cotton fields of the strong light, shadow, etc. accurately and effectively splitting cotton, helps to determine its position in three-dimensional space. In accordance with the characteristics of cotton pictures, a method of Particle Swarm Optimization(PSO) and K-means hybrid clustering in YCbCr color space is proposed. This approach reinforces the exploitation of global optimum of the PSO algorithm. In order to avoid the premature convergence and speed up the convergence, traditional K-means algorithm is used to explore the local search space more efficiently dynamically according to the variation of the particle swarm’s fitness variance. The experiment results show that this method can segment cotton image with the complex background, and is more effective than the traditional PSO and K-means algorithm.关键词
棉花分割/YCbCr颜色空间/K均值算法/粒子群算法Key words
cotton segmentation/YCbCr color space/K-means algorithm/Particle Swarm Optimization(PSO)algorithm分类
信息技术与安全科学引用本文复制引用
时颢,赖惠成,覃锡忠..粒子群与K均值混合聚类的棉花图像分割算法[J].计算机工程与应用,2013,(21):226-229,4.基金项目
新疆维吾尔自治区科学基金资助项目（No.2011211A010）。 （）
