基于改进型C-V模型的植物病斑图像分割  

Segmentation of Plant Lesion Image Using Improved C-V Model

作　　者：胡秋霞[1,2] 田杰[1,2] 何东健[1] 宁纪锋[2] 

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西杨凌712100 [2]西北农林科技大学信息工程学院,陕西杨凌712100

出　　处：《农业机械学报》2012年第5期157-161,共5页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(60975007;61001100;61003151)

摘　　要：针对植物病斑图像背景复杂且分割难问题,提出一种基于水平集和加权颜色信息的C-V模型。借助水平集方法对病斑图像的R、G、B分量图像颜色信息取加权值,以差分图像能量作为能量函数最终值,以适应不同的病害种类。试验结果表明,经过R、G、B加权的黄瓜红粉病病斑图像使用4R-G图像模型、苹果锈病病斑图像使用3R-G-B图像模型自动分割的效果较好,比传统C-V模型分割性能好,抗噪性好,可扩展性好。In view of complex background of lesion images and the difficulty in segmentation,an improved C-V model based on level set and weighted color information was proposed and applied into agricultural lesion image segmentation.The segmentation model of weighted color information based on level set was suitable to different diseases identification and could identify lesion disease automatically.Experimental results show that the proposed model has better property than C-V model,and has many advantages such as anti-noise and scalability properties on 4R-G image model for cucumber pink and 3R-G-B image model for apple rust disease.

关 键 词：植物病害 病斑图像 图像分割 C-V模型 加权颜色 水平集 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]