自适应粒子群集优化二维OSTU的图像阈值分割算法  

Image threshold segmentation algorithm based on adaptive particle swarm optimization of two-dimensional OSTU

作　　者：于洋[1] 孔琳[1] 虞闯[2] Yu Yang Kong Lin Yu Chuang(School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China)

机构地区：[1]沈阳理工大学自动化与电气工程学院,沈阳110159 [2]沈阳理工大学信息科学与工程学院,沈阳110159

出　　处：《电子测量与仪器学报》2017年第6期827-832,共6页Journal of Electronic Measurement and Instrumentation

基　　金：辽宁省自然科学基金(201602652)资助项目

摘　　要：为了解决红外相机采集行人图片时图像分割效果问题,提出一种自适应粒子群优化二维OSTU的阈值分割算法。利用当前帧图像的灰度级和当前帧图像像素的邻域灰度级构成二元组,通过计算二者的均值和方差,建立二维最大类间方差模型,结合自适应粒子群集算法,估计出图像的最佳阈值,该方法不仅能够准确估计阈值且计算时间减少。仿真结果表明,阈值最佳时,当结合自适应粒子群集优化算法后计算时间减少到原来的50%,所提出的算法能够快速准确得到最佳阈值,提高了图像预处理的分割效果。In order to solve the effect of the image segmentation when the pedestrian image is collected by infrared camera,an image threshold segmentation algorithm based on adaptive particle swarm optimization of two-dimensional OSTU is utilized. The gray scale of the current frame image and the neighborhood gray level of the current frame image pixel form a binary image. A 2D maximum betweencluster variance model is built up through calculating the average and variance between them,and combining with adaptive particle swarm optimization algorithm the best threshold image value is estimated. The algorithm can accurately estimate the threshold and reduce the calculation time. The simulation results demonstrate that the best image value is proper,the calculation time is shortened 50% when combine with adaptive particle swarm optimization algorithm. The proposed algorithm can get the optimal threshold quickly and accurately,and improve the segmentation effect of image preprocessing.

关 键 词：自适应粒子群优化 二维OSTU阈值 图像预处理 阈值分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]