本文提出了一个新的二维直方图(2D-WLDH)，同时提出了基于2D-WLDH 和最大类间方差的图像阈值选取方法，并导出相应快速递推算法。新提出的2D-WLDH 在区域划分时可以避免传统直方图区域划分时面临的不合理的假设，通过计算比较小的归一化的WLD 值来准确估计目标和背景的概率。本文实验结果表明：与现有的有关算法相比，本文提出的阈值选取快速递推算法不仅使分割后的图像区域内部更均匀、边界形状更准确、抵抗噪声稳健，而且同时其运行时间还减少了约84.93%。

A new 2D-histogram called 2D-WLDH is proposed. At the same time, a new image thresholding method based on 2D-WLDH and maximum between-cluster variance is proposed. Moreover, the corresponding fast recursive algorithm is deduced. Regional division of the proposed 2D-WLDH can avoid the shortcomings of the traditional 2D histogram. The probability of the target and background of the image can be accurately estimated by calculating the small normalized Weber Local Descriptor (WLD) value. The experimental results show that, compared with the existing corresponding algorithm, the proposed fast recursive algorithm for maximum between-cluster variance threshold selection based on 2D-WLDH, achieves better segmentation quality, which obtains uniform regions, accurate borders and robust noise resistances. Furthermore, the running time of the proposed algorithm reduces by about 84.93%.