融合视觉模型和最大类间方差的阈值分割算法

针对传统二维直方图的区域划分方法存在把图像的部分目标点和背景点错误划分为边缘点或噪声点,而把部分边缘点和噪声点划分为目标点和背景点的缺点,以及传统二维最大类间方差阈值分割算法的时间复杂度较高的缺点,提出了采用视觉模型构造二维直方图,并提出了该二维直方图的区域划分方法,同时还把提出的二维直方图应用到最大类间方差阈值分割算法中。根据分割时间、分类误差、均匀性等定量评价标准,做了一系列实验,与几种典型的二维阈值分割算法相比,提出的阈值分割算法在降低计算复杂度的同时还具有很好的分割性能。

Image thresholding segmentation based on human vision model and maximum between-cluster variance

Because the area division of the traditional two-dimensional histogram has such shortcomings as part of the target points and background points are divided into edge points or noise points, while part of the edge points and noise points are divided into the target point and background points and the traditional two-dimensional image thresholding segmentation algorithms' time complexity are high, a new two-dimensional histogram was proposed by using human vision model, and a new region division method was proposed to the proposed histogram, and at the same time, the proposed histogram was applied to image thresholding segmentation based on maximum between-cluster variance. According to segmentation time, misclassification error and uniformity, a series of experiments were carried out to show the proposed algorithm reduces the time complexity and has good segmentation performance compared with several typical two-dimensional threshold segmentation algorithms.