基于K_means硬聚类算法的葡萄病害彩色图像分割方法

为了提高植物病害图像的分割精度与效果，根据植物病害症状及图像的特点，提出了一种基于K_means硬聚类算法（HCM）的葡萄病害彩色图像非监督性分割处理方法。该方法是在L*a*b*颜色空间模式下利用ab二维数据空间的颜色差异，以平方欧式距离作为像素间的相似度距离、以均方差作为聚类准则函数对颜色进行二分类聚类，并通过数学形态学运算对聚类结果进行校正。利用该方法对3种葡萄病害彩色图像进行分割的结果表明，该方法能够较为准确地将病斑区域从彩色图像中分割出来，对葡萄病害彩色图像的分割处理比较理想，鲁棒性好，分割准确率

Segmentation of color images of grape diseases using K_means clustering algorithm

To improve the segmentation precision and effectiveness of plant disease images, a kind of unsupervised segmentation processing method based on K_means clustering (HCM) algorithm was proposed according to the properties of the symptoms and images of plant diseases. On the basis of the color differences of ab two-dimension data space from L*a*b* color space model, iterative color clustering of two clusters was conducted using squared Euclidian distance as the similarity distance and mean square deviation??as the clustering criterion function. And the mathematics morphology algorithm was used to correct the clustering results. The proposed method was used to segment the color images of three kinds of grape diseases. The results show that it can satisfactorily segment the diseased regions from the color images of grape diseases with good robustness and good accuracy.