An Iterative Maximum Entropy Thresholding Algorithm
Jianwu Long, Jianxun Zhang, Nan Xiang, Jinrong Zhang, Dong Wang · 2016
In this work, an iterative maximum entropy thresholding algorithm is proposed, due to the fact that the thresholds derived from the maximum entropy method are non-optimal. Firstly, the maximum entropy thresholding algorithm is applied to the grey images which are roughly grouped into background and object. Secondly, two Gaussian distributions are fitted according to the mean and variance of each group. Because the optimal threshold is located at their intersection of two Gaussian populations, the presented scheme optimizes thresholds using an iterative scheme until converging to the optimal position. Finally, extensive experiments are performed, and the results show that the thresholds derived from the maximum entropy scheme using the proposed iterative threshold optimization approach can be converged to the optimal position.