用遗传BP网络进行图像边缘检测

Genetic BP Neural Networks Used in Edge of Image Detection

该文提出了一种基于遗传算法与图像特征向量的边缘检测方法。由于噪声的干扰,常规的图像边缘检测方法往往效果不佳,因此在充分考虑边缘和噪声本质区别的基础上,构造具有较强抗噪能力的特征向量;然后用样本图像对多层前馈神经网络采用遗传学习算法和误差反向传播算法(BP)相结合进行训练,即先用遗传学习算法进行全局训练,再用BP算法进行精确训练,使网络收敛速度加快和避免局部极小。最后,将训练后的网络用于图像的边缘检测。实验证明这种方法是有效的。

A kind of edge detection method based on image features and genetic algorithms neural network is proposed in this paper.Disturbed by noise,Edge of image can not be detected finely by normal method.Therefore the essential differences between noise and edge are fully considered while selecting the image features and forming feature vector with better anti-noise performance,then use sample image to train multi-layered and interconnected neural networks.In the training,a general -purpose global search algorithm is used to train the network with updating the weights to minimize the error between the network output and the desired output.Then the back-propagation(BP)algorithm is used to further train the neural network.Finally this trained network is used to detect other images' edge.The experimental results show that this method is very effective.