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

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.