Infrared image segmentation based on PCNN with genetic algorithm parameter optimization

A PCNN infrared image segmentation algorithm based on genetic algorithm parameter optimization is proposed. The algorithm first carries out the ignition process for input images using PCNN global coupling and pulse synchronization. Entropy is calculated according to the PCNN output and used for the fitness function of genetic algorithm. The entropy change is used as the convergence criterion of genetic algorithm. Combination of optimization is made for parameters effecting image segmentation in PCNN model. To find the optimal values of key parameters biological visual characteristics of PCNN and solution space random search of the genetic algorithm are adopted. Combination of genetic algorithm and PCNN can make full use of the advantages. Compared with OTSU, maximum entropy histogram segmentation algorithm and PCNN segmentation method, quantitative analysis is conducted for the image after segmentation using cross entropy and region contrast objective index. Simulation results show that, judging either by subjective vision or by objective index, the proposed method is superior to other comparative method in segmentation effect.