复杂背景与天气条件下的棉花叶片图像分割方法
投稿时间:2017-03-03  修订日期:2017-03-06  点此下载全文
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作者单位E-mail
李凯 甘肃农业大学工学院 474503718@qq.com 
张建华 中国农业科学院农业信息研究所 zhangjianhua@caas.cn 
冯全 甘肃农业大学工学院  
孔繁涛 中国农业科学院农业信息研究所  
韩书庆 中国农业科学院农业信息研究所  
吴建寨 中国农业科学院农业信息研究所  
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:为实现自然条件下棉花叶片的精准分割,提出一种粒子群(PSO)优化算法和K-means聚类算法混合的棉花叶片图像分割方法。首先将棉花叶片图像在RGB颜色空间模式下采用二维卷积滤波进行去噪预处理;其次将预处理后的彩色图像从RGB转换到目标与背景差异性最大的Q分量、超G分量、a*分量;再次,在K均值聚类的一维数据空间中,利用PSO算法向全局像素解的子空间搜寻,通过迭代搜寻得到全局最优解,确定最佳聚类中心点,改善K均值聚类的收敛效果;最后,对像素进行聚类划分,从而得到棉花叶片分割结果。按照不同天气条件和不同背景采集了1200幅棉花叶片样本图像,对本文算法进行测试,试验结果表明:该算法对于晴天,阴天和雨天图像中目标(棉花叶片) 分割准确率分别达到92.39%,93.55%,88.09%,总体平均分割精度为91.34%,并与传统K均值算法比较,总体平均分割精度提高了5.41%。分割结果表明,本文算法能够对3种天气条件(晴天,阴天,雨天)与4种复杂背景(白地膜,黑地膜,秸秆,土壤)特征混合的棉花叶片图像实现准确分割,为棉花叶片的特征提取与病虫害识别等后续处理提供支持。
中文关键词:棉花叶片  复杂背景  天气条件  K均值聚类  粒子群优化(PSO)  图像分割
 
Image segmentation method for cotton leaf under complex background and weather conditions
Abstract:In order to realize the accurate segmentation of cotton leaf under natural conditions, a new image segmentation algorithm was put forward based on particle swarm optimization (PSO) algorithm and K-means clustering algorithm. Firstly, we denoised the cotton leaf images by the two-dimensional convolution filter in the RGB color space model; Secondly, we converted the processed color image from RGB to the Q component , the super G component and the a* component, which had the largest difference between the target and the background in 3 color components; Again, in one dimension data space of K means clustering, we searched for subspace of solutions to the global pixel by using the PSO algorithm, through the iterative search, we could receive global optimal solution and make sure optimal clustering center, so that we could improve the convergence effect of K means clustering; Finally, we divided the pixels, which come from cotton leaf images, into clusters to obtain the results of cotton leaf segmentation. Considering the effects of different weather conditions and different backgrounds on imaging, we took 1200 pictures of cotton leaves under various imaging conditions, and evaluated the segmentation performance of the proposed algorithm on these images. Experimental results showed that, for the images taken in sunny day, cloudy day and after raining, the accuracies of segmentation of the algorithm reached 92.39%, 93.55% and 88.09% respectively, the whole average segmentation accuracy is 91.34% . Though comparing with the traditional K means algorithm, the whole average segmentation accuracy of the proposed algorithm had improved by 5.41% than the traditional K means algorithm. Segmentation results showed that, the proposed algorithm from this paper can accurately segment the cotton leaf images, which combined with 3 weather(sunny day, cloudy day and rainy day) and 4 complex background features(white ground membrane, black film, straw, soil). The algorithm in this paper can provide support for the subsequent processing of feature extraction and identification of plant diseases and insect pests.
keywords:cotton leaves  complex background  weather condition  K means clustering  particle swarm optimization  image segmentation
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