摘要
针对名优茶嫩芽自动采摘问题,采用SVM学习算法实现对名优茶嫩芽图像自动分割。通过提取嫩芽像素点与背景像素点的RGB及(R-B)特征,将4个特征按重要性组合为3个特征组,分别是RGB特征组、RGB+(R-B)特征组和G+(R-B)特征组,利用3个特征组分别构建SVM嫩芽分割模型。在收集的多幅图像上的实验表明G+(R-B)特征组构建的分割模型分割得到的嫩芽图像较为完整,且用时均低于0.5 s,满足名优茶嫩芽自动采摘的要求。
Aiming at the problem of automatic picking of famous tea buds,the SVM learning algorithm is used to realize the automatic segmentation of the image of famous tea buds.By extracting the RGB and(R-B)features of the bud pixels and the background pixels,the four features are combined into three feature groups according to their importance.They are RGB feature group,RGB+(R-B)feature group and G+(R-B)feature group.Three feature groups were used to construct SVM buds segmentation models.Experiments on several collected images show that the segmentation model constructed by G+(R-B)feature group can obtain relatively complete bud images,and the segmentation time is less than 0.5 s,which meets the requirements of automatic picking of famous tea buds.
作者
陈妙婷
杨广蕾
秦鹏涛
CHEN Miaoting;YANG Guanglei;QIN Pengtao(Qingdao University of Science and Technology,Weifang 261500,China)
出处
《现代信息科技》
2021年第2期89-92,共4页
Modern Information Technology
关键词
自动采摘
SVM
图像分割
automatic picking
SVM
image segmentation
