ISSN 1000-1239 CN 11-1777/TP

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### 采用高斯拟合的全局阈值算法阈值优化框架

1. 1(吉林大学计算机科学与技术学院 长春 130012);2(符号计算与知识工程教育部重点实验室(吉林大学) 长春 130012);3(重庆理工大学计算机科学与工程学院 重庆 400054) (xjshen@jlu.edu.cn)
• 出版日期: 2016-04-01
• 基金资助:
国家自然科学基金项目(61305046,61502065);吉林省自然科学基金项目(20140101193JC,20130532117JH,20150101055JC);重庆理工大学科研启动基金项目(2014ZD27)

### Threshold Optimization Framework of Global Thresholding Algorithms Using Gaussian Fitting

Chen Haipeng1,2, Shen Xuanjing1,2, Long Jianwu3

1. 1College of Computer Science and Technology, Jilin University, Changchun 130012); 2Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012); 3College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054)
• Online: 2016-04-01

Abstract: 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.