一种结合粒子群算法和自适应加权窗的二维Otsu图像分割新方法

New 2D Otsu Image Segmentation Method via Particle Swarm Algorithm and Adaptive Weighted Window

针对传统二维Otsu门限分割方法中滤噪和小目标保持性能的不足,提出了一种基于自适应加权窗的二维Otsu门限分割的新方法。新方法对二维Otsu的邻域窗口设置方法做了改进,使用中心点的局部平稳特征来自适应地确定下一邻域窗口的尺寸大小,然后利用粒子群算法来加快门限的计算速度,从而提高门限分割的性能。实验结果表明:与目前广泛使用的一维Otsu、二维Otsu方法以及直线型门限二维Otsu方法相比,新方法有着更好的门限分割效果,并且有更好的噪声抑制和目标保持效果。

Aimed at the shortage of the abilities of noise removing and small target preservation for the conventional two-dimensional Otsu thresholding method, a new two-dimensional （21）） Otsu method based on adaptive weighted win- dow was proposed. The new method improves the window setting method of the 2D Otsu,and the window size is adap- tively determined by the local stationarity character. Then, the threshold is computed by the particle swarm algorithm, in order to improve the segmentation performance and shorten the computational time. Compared with the commonly-used one-dimensional Otsu, 2D Otsu method and line-type threshold 2D Otsu method, the proposed method has the better segmentation performance,with better performance for noise removal and small target preservation.