基于均值—中值—梯度共生矩阵模型的最大熵分割算法

Maximum entropy thresholding algorithm based on mean-median-gradient co-occurrence matrix model

针对基于灰度—梯度共生矩阵模型的最大熵阈值分割算法抗噪声差的缺点,引入了均值—中值—梯度共生矩阵模型,并提出了基于该模型的最大熵阈值分割算法。为了有效地节省计算时间与存储空间,进而导出了该方法的快速递推公式。实验结果表明,该算法优于灰度—梯度模型分割方法,并能抑制高斯噪声、椒盐噪声以及其混合噪声对分割结果的影响,提高了分割的鲁棒性。

In order to overcome the shortcomings of maximum entropy thresholding algorithm based on gray level-gradient cooccurrence 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.