粒子群算法在二维Otsu图像分割中的应用研究  

Application of PSO Algorithm in 2D Otsu Image Segmentation

作　　者：钱晓军[1] 

机构地区：[1]南京师范大学计算机科学与技术学院,江苏南京210097

出　　处：《计算机仿真》2010年第12期279-281,353,共4页Computer Simulation

基　　金：江苏省教育厅基金项目(08KJD520008)

摘　　要：研究图像识别优化提取目标问题,噪声影响使图像目标识别精度差,效率低。传统Otsu算法的阈值的选取大多采用穷尽的搜索方式,运算效率较低,抗噪能力不强,容易产生误分割。为了提高图象分割效率和分割精度,提出一种粒子群优化算法的二维Otsu图像分割方法。方法首先对图像进行去噪处理,绘制出图像的二维直方图,根据二维直方图信息选取适当灰度值作为混沌粒子群算法中的初始粒子,每个粒子代表一个可行的二维阈值向量,通过粒子群之间的协作来获得最优阈值,可采用最优阈值划分像素,实现图像分割。实验结果表明,相对于传统Otsu图像分割算法,不仅得到了更高的图像分割精度,计算量也大大减少,提高分割效率,有利于提高图像处理的实时性,也证实了将粒子群算法用于阈值分割是可行的。In image segmentation algorithms,the selection of optimal threshold is the key to segmentation.However,most threshold selection methods adopt the mode of exhaustive search so that the operation efficiency is low,the capability of noise resisting is weak,and error segmentation happens easily in these methods.A new algorithm of select optimal threshold is proposed based on chaos particle swarm optimization.The first step of image threshold method discussed in this paper is image denoising and making two-dimensional histogram of the image.The second one is to select appropriate values of gray level as initial population according to the two-dimensional histograms.Finally,the output of this algorithm is the optimal threshold.Using this threshold to partition off the pixels,image segmentation is implemented.Experimental results show that the proposed method can not only obtain ideal segmentation results but also decrease the computation cost reasonably,and it is suitable for real time application and conducive to improving the realtmime image processing.

关 键 词：图像分割 阈值选取 粒子群 混沌优化 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]