基于MMC与CV模型的苗期玉米图像分割算法

Color Image Segmentation Algorithm of Corn Based on MMC and CV Model

针对苗期玉米田复杂土壤背景噪声,提出一种基于MMC(最大间隔准则)与CV(Chan-Vese)模型的玉米彩色图像分割算法。利用MMC对玉米彩色图像灰度化,用TV(全变分)滤波器对灰度图像进行去噪,用CV模型对去噪图像进行图像分割。试验结果表明,算法优于传统的颜色因子与Otsu组合算法,能有效去除图像中的小杂草和青苔,实现玉米目标提取,错分率为4.32%,漏分率为9.69%,相似度为86.57%。

Aiming at removing complex soil background noise in the corn seedling filed, a color image segmentation algorithm based on MMC （Maximum margin criterion） and C V （Chan - Vese） was proposed. The corn color image was transformed into gray image by using MMC, and the grayscale image was denoised by TV （Total variation） filter. Then filtered image was segmented by the C V model. The results of the experiment by Matlab showed that the algorithm could effectively get the extraction of the objection of corn and noise reduction of weed and moss simultaneously in the image. The misclassification rate and the leakage rate were 4.32% and 9.69% respectively, and the similarity was 86.57%.