BP Neural Network Fuse with Morphology Edge Detection Method

Abstract

In order to obtain better image edge information, an edge detection algorithm combining BP (Back Propagation) neural network and morphology is proposed. The Sigmoid function is commonly used as the excitation function in BP neural networks, but the traditional Sigmoid function is single in form and lacks flexibility. Therefore, it is very important to provide an adjustable Sigmoid function. First, a fully smooth Sigmoid function construction method is given, which is used as the excitation function in the BP neural network to detect the edge of the image effectively. Then, using the idea of multi-scale and multi-structure, an improved morphological edge detection algorithm is proposed, and the edge image with small noise and continuous is obtained by applying this algorithm. Finally, the wavelet analysis is used to fuse the BP neural network and the improved morphological algorithm, and then an edge detection fusion algorithm is obtained. The simulation results show that the evaluation index of the fusion algorithm is better than a single edge detection algorithm and the detected image edge lines are complete and clear.

Keywords

• back propagation neural network
• sigmoid function
• mathematical morphology
• edge detection
• fusion algorithm