Coupled Parameter Optimization of PCNN Model and Vehicle Image Segmentation

The pulse coupled neural network (PCNN) has good properties for image segmentation. While the segmentation effect significantly depends on the parameter selection of PCNN. Thus, the adaptive parameter choice is important for the PCNN application research. The coupled parameter optimization algorithm based on the coupling characteristics is proposed, which combines the neural calculation principle and characteristics of gray-scale in the image local area. First, the connection weight matrix of the PCNN model is updated in terms of the Hebb rule. Then the correlation strength factor between different regions is adaptively determined by local mean square deviation. Finally, the vehicle images are segmented by the PCNN model with optimized parameters. Compared with traditional PCNN image segmentation, the proposed method increases the coupling strength between neurons and avoids over-segmentation and under-segmentation. The segmentation quality of license plate images on the moving vehicles is improved, which lays a good foundation for the subsequent feature extraction.