用简化脉冲耦合神经网络实现交通标志图像的类Euclidean距离变换类内特征提取

Abstract

The recognition accuracy of with-in class matching traffic signs is dropped because of the rotation invariance in feature sequences extracted by conventional Pulse Coupled Neural Network(PCNN).In order to get a new feature vector with stronger classification ability,the PCNN mode was simplified according to its automatic wave diffusion characteristics.An Euclidean-like distance image was used as the new feature vector,and a match analysis was carried out by the minimum variance.Then,optimal parameters of PCNN were acquired through experiments.The experimental results based on GB5768-1999 traffic signs show that when the Euclidean-like distance transform based on simplified PCNN is used as the feature vector for classification matching,the variance of test image corresponding to standard images can achieve the minimum value in acquiring edge image with the number iterations of 16,magnitude adjustments of feeding input of 0.65,magnitude adjustments of dynamic threshold of 100,and convolution kernel matrix in 5 × 5.It concludes that the Euclidean-like distance transform based on simplified PCNN can expand the shape information of edge images effectively.It is superior to the feature vector based on entropy sequence,and is fit for the identification of target images in with-in classes.