Infrared Image Segmentation Using Two-Dimensional Fisher Linear Optimal Discriminant Analysis and Particle Swarm Optimization

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.