首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于颜色特征的绿色作物图像分割算法
引用本文:张志斌,罗锡文,臧 英,厚福祥,徐晓东.基于颜色特征的绿色作物图像分割算法[J].农业工程学报,2011,27(7):183-189.
作者姓名:张志斌  罗锡文  臧 英  厚福祥  徐晓东
作者单位:1. 内蒙古大学计算机学院,呼和浩特,010021
2. 华南农业大学南方农业机械装备关键技术省部共建教育部重点实验试验室,广州,510642
3. 内蒙古农业大学能源与交通工程学院,呼和浩特,010018
基金项目:内蒙古大学科研启动基金(209053);国家自然科学基金(Nos. 60878026, U0931001);948计划项目(2010-s27);高等学校博士点基金(No. 20094404120001):广东省科技计划(No. 2009B020314003)
摘    要:绿色作物的识别是农业机械视觉系统的重要研究内容之一,该文采用RGB颜色系统,基于统计分析提出了一种绿色作物图像分割方法。从简单物体光照颜色模型方面,分析了RGB颜色空间中作物绿色“恒量”(Gvalue>Rvalue and Gvalue>Bvalue)的存在性,构建了作物图像分割相对错误率评估模型。并与传统颜色索引方法Excess Green (ExG)+auto-threshold进行了对比分析。试验结果表明,在正常光照条件下:1)采用的算法对田间不同作物-土壤组图像分割的相对错误率均有显著影响;其中,相对ExG+auto-threshold算法,采用RGB算法的结果图像中大多能保留油菜、大豆和甘蔗的形态学特征;2)采用的算法、光照变化以及算法与光照变化的交互作用均对室外美人蕉图像分割的相对错误率有显著影响;其中,相对ExG+auto-threshold算法,采用RGB算法的结果图像中大多能去除背景噪声。单因子方差分析进一步表明,光照变化对采用ExG+auto-threshold算法分割图像的阈值有显著影响。该文提出的RGB算法相对传统的ExG+auto-threshold绿色索引,对于早期生长的绿色作物是一种有效、简单的图像分割方法,对作物-土壤、光照变化不敏感。

关 键 词:农业机械,导航,绿色作物,图像分割,机器视觉
收稿时间:2010/8/30 0:00:00
修稿时间:4/9/2011 12:00:00 AM

Segmentation algorithm based on color feature for green crop plants
Zhang Zhibin,Luo Xiwen,Zang Ying,Hou Fuxiang and Xu Xiaodong.Segmentation algorithm based on color feature for green crop plants[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(7):183-189.
Authors:Zhang Zhibin  Luo Xiwen  Zang Ying  Hou Fuxiang and Xu Xiaodong
Abstract:Crop rows recognition is a principal issue in agricultural machinery vision system. In this paper, the color constant in RGB space was found to segment the green plant from the background by using statistical analysis based on the classical simple illumination model. For early green vegetation, the value of green component Gvalue is always greater than that of the other red Rvalue and blue Bvalue components (Gvalue>Rvalue and Gvalue >Bvalue, inferred to as RGB); and a relative segmenting error ratio was designed to evaluate the performances of the RGB presented in this paper and the Excess green + auto-threshold(ExG+atuo-threshold). In Experiment 1, the single factor variance analysis showed the algorithms (RGB and ExG+auto-threshold) had significant effect on the relative segmentation error ratio of the plant-soil images, and the corresponding segmented image using RGB were found that most of them could preserve morphological feature of plant compared with ExG+auto-threshold. And in Experiment 2, the double factor variance analysis showed that the algorithms, illuminant variations and their interaction had significant effect on the relative segmentation error ratio of the Canna images grasped consecutively, and the illuminant variations affected the threshold values of ExG+k-auto. The corresponding segmented images using RGB were found that most of them could delete the background noises compared with ExG+auto-threshold. And therefore, the RGB is a simple but efficient segmentation algorithm, and insensible to plant-soil and illuminant variations compared with ExG+auto-threshold.
Keywords:agricultural machinery  navigation  green vegetation  image segmentation  machine vision
本文献已被 万方数据 等数据库收录!
点击此处可从《农业工程学报》浏览原始摘要信息
点击此处可从《农业工程学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号