Maximum entropy thresholding algorithm based on mean-median-gradient cooccurrence matrix model
基于均值—中值—梯度共生矩阵模型的最大熵分割算法*

In order to overcome the shortcomings of maximum entropy thresholding algorithm based on gray level-gradient co-occurrence matrix model with poor antinoise performance, this paper introduced a mean-median-gradient co-occurrence matrix model. Based on this model, proposed a maximum entropy thresholding algorithm simultaneously. For the purpose of saving computing time and storage space, presented a fast recursive method in the end. Experimental results show that the algorithm is superior to gray level-gradient model segmentation approach, and can suppress Gaussian noise, impulse noise and their hybrid noise, improves the robustness of the segmentation effectively.