基于水平集和先验信息的农业图像分割方法

Segmentation of Agricultural Images Using Level Set and Prior Information

提出了一种基于先验信息的C-V模型并对杂草﹑小麦﹑苹果进行分割研究。根据某类农业图像的特点,把图像表示为易于分割的模型,提取模型中感兴趣目标的信息量作为先验信息,通过H分量得到初始轮廓,并以此初始化提出的模型,迭代求解水平集函数,得到收敛的目标轮廓曲线。对杂草﹑小麦﹑苹果分割结果统计分割面积正确率为0.999、0.9990、.846,面积错误率为0、0、0.125。

A C- V model based on level set and prior information was proposed and was applied to segment weed, wheat and apple images. Based on the characteristics of the image, the image was represented by a model which made the image easy to segment at first, and then the data contents of a region of interest in this model were extracted as the prior information. An initial contour by hue was obtained and the proposed model by this contour was initialized, the level set function was iteratively solved. Finally, a stationary contour was obtained. The correct rates of weed, wheat and apple were 0. 999, 0. 999 and 0. 846 respectively and the error rates were 0, 0 and 0. 125 respectively.