Threshold Optimization Framework of Global Thresholding Algorithms Using Gaussian Fitting

There is a certain deviation to obtain the threshold in three classical global thresholding algorithms which are Otsu algorithm, maximum entropy algorithm and minimum error algorithm. To solve this problem, a threshold optimization framework (TOF) of global thresholding algorithms using Gaussian fitting is proposed. Firstly, take advantage of the global threshold method to obtain the initial threshold in the optimization framework and divide the image into two parts of the background and object roughly. And then, Two Gaussian distributions are fitted by calculating the mean and variance of each part. Since the optimal threshold value is in the intersection location of two Gaussian distributions, the presented framework optimizes the thresholds using iterative approach until eventually converging to the optimal threshold position. In order to improve anti-noise performance, combined with the reconstruction of three-dimensional histogram and thinking of reducing the dimensionality, we propose a robust threshold optimization framework (RTOF) of global thresholding algorithms using Gaussian fitting. Finally, extensive experiments are performed and the results show that those thresholds derived from Otsu scheme, maximum entropy scheme and minimum error scheme using the proposed threshold optimization framework can converge to the optimal threshold position. Plus, the presented algorithm has robust anti-noise performance and high-efficiency.