摘要
Otsu算法,也被称之为最大类间方差算法,是实现阈值分割的经典算法之一。二维Otsu算法是一维Otsu算法的推广,它充分考虑了图像的灰度信息和空间邻域信息,可以有效滤除噪声影响,但是同样存在着运算量大、时效性差的问题。对此提出了一种改进的二维Otsu快速阈值分割算法,先将二维Otsu算法分解为两个一维Otsu算法,并集成类间和类内方差信息构造了一种新的阈值判别函数,同时通过降维,进一步降低计算量。实验结果表明,该算法在时间效率与分割效果两方面明显优于传统的二维Otsu算法与快速二维Otsu算法。
Otsu algorithm, also called the method of maximum classes square error, is one of classical methods for image threshold segmentation. As generalization of 1D Otsu algorithm, 2D Otsu algorithm fully considers information of both the image gray and the neighborhood relationship among pixels, thus it is able to filter noise effectively. However, it is time consuming because of its huge amount of calculation. Concerning the problem, this article presents an improved fast 2D Otsu segmentation algorithm, which further cuts down the amount of computation by decomposing the original 2D Otsu algorithm into two 1D Otsu algorithm, constructing a new threshold recognition function through integrating inter-class variance with intra-class variance, and reducing dimension. Experiment results show that the improved method is superior to the other two methods in terms of segmentation efficiency and effect.
出处
《电子技术应用》
北大核心
2016年第12期108-111,共4页
Application of Electronic Technique
基金
国家自然科学基金项目(41501451)
关键词
阈值分割
二维OTSU
类间方差
类内方差
threshold segmentation
2D Otsu
inter-class variance
intra-class variance
