电子器件2009，Vol.32Issue(1)：12-16,5.
基于二维Fisher线性鉴别分析和粒子群优化的红外图像分割
Infrared Image Segmentation Using Two-Dimensional Fisher Linear Optimal Discriminant Analysis and Particle Swarm Optimization
摘要
Abstract
Two-dimensional (2-D) Fisher linear optimal discriminant analysis, which considers the gray information and spatial neighbor information between pixels in the image simultaneously, overcome especially if the histogram of images in reality has no distinct sharp valleys or the valley is flat and broad, the proposed is an efficient image segmentation method. However, finding the optimal threshold vector using exhaustive searching is expensive for 2-D fisher criterion function thresholding method. In this paper, an optimization method, i.e., particle swarm optimization (PSO) is used to find the optimal 2-D threshold vector, in which each particle represents a possible 2-D threshold vector and the best 2-D threshold is obtained through the cooperation among particles. To show the validity of the proposed method, this paper uses several infrared images to segment. Analysis and 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 applications.关键词
红外图像/图像分割/Fisher线性鉴别分析/粒子群优化Key words
infrared image/image segmentation/Fisher linear optimal discriminant analysis/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
唐英干,黄娜,关新平..基于二维Fisher线性鉴别分析和粒子群优化的红外图像分割[J].电子器件,2009,32(1):12-16,5.基金项目
This work is supported by National Natural Science Foundation of China for Distinguished Young Scholars under Grant.60525303 and Doctoral Foundation of Yanshan University under Grant.B243 （）
