基于不变矩和改进BP神经网络的目标识别  

Object Recognition Based on Invariant Moment and Improved Back Propagation Neural Network

作　　者：文华荣 李秩[2] 冯一[3] 吴晓迪[3] 

机构地区：[1]西藏军区指挥自动化站 [2]73677部队 [3]电子工程学院

出　　处：《光电技术应用》2013年第4期49-54,共6页Electro-Optic Technology Application

摘　　要：基于Hu不变矩的尺度不变性,以图像的不变矩特征作为输入,建立基于批训练的改进型误差反向传播(BP)神经网络。运用基于Bayesian正则化的Levenberg-Marquardt算法优化误差函数计算精度,改进网络,实现参数最优化组合。通过MAT LAB环境,建立了基于不变矩的改进BP神经网络目标识别模型。实验表明,该方法实现了对目标的准确识别和对干扰图像的正确判断。Based on the geometrical invariability of Hu invariant moment,and taking the characteristics of image invariant moment as an input,an improved error back propagation（BP） neural network for batch training is established.According to Bayesian normalization Levenberg-Marquardt algorithm,the calculation accuracy of error functions is optimized and the network is improved so as to realize the optimized parameter combination.An improved BP neural network object recognition model based on invariant moment is built in MATLAB envi ronment.Experimental results show that accurate target recognition and correct interferential image estimation are implemented with the method.

关 键 词：不变矩 BP神经网络 目标识别 LEVENBERG-MARQUARDT算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]