为了更好的分割出图像中的目标,文中对最大类间方差法进行了改进。根据不同的分割次数,在图像中反复运用最大类间方差进行分割,再以子图像中所提取目标的灰度值方差、标准差以及目标面积在原图中所占的比例等为因子,利用贝叶斯估计进行预测,从而提取出图像中的目标。该方法不仅对目标与背景差异较大的图像有较好的分割作用,而且对于目标灰度分类不属于极大或极小类的图像也有较好的分割作用。实验证明,该方法效果良好。

In order to better segment the target in the image, the article improves the method of maximum inter-class variance. According to many numbers of segmentation, we used repeatedly the maximum between-cluster variance to segment the sub-image. There were many factors, such as variance, standard deviation, proportions of the area extracted in the original image, and so on. With the factors, we used Bayesian theory to predict and extract the target in the image. This method not only has a better effect on the images with obvious differences between the target and the background, but also has a better effect on the image in which the target gray level does not belong to the maximum or the minimal class. The algorithm is proved effective by experiments.