BP网络实时图像自动选取算法研究

实时图像的自动选取是景像匹配自动化的关键技术.提出了一种基于神经网络的实时图像自动选取算法.仿真研究表明,引入Sobel算子的3层BP网络实时图像自动选取算法结构简单、训练时间短和分类准确性高,其并行处理能力能满足景像匹配技术的实时性要求.经最小二乘和Bayes精匹配算法检验,实时图经此网络选取、粗匹配和精匹配,可达到高于0.5像素的定位精度.

Study on Automatic Selection of Current Images

Automatic selection of Current Image (CI) is a key technology for automation of scene matching navigation system. An algorithm for automatic selection of CI based on 3 layers BP net integrated with Sobel template and on neural network was proposed. The simulation results indicated that the algorithm is simple in structure, short in time, and highly accurate in image classification. By analysis of testing samples, BP parallel structure was shown to be achievable in real time implementation. As tested by Least Square and Bayes Scene Matching algorithms, the current image could reach a final precision higher than 0.5 pixel by means of BP net automatic selection algorithm, simple Scene Matching and accurate Scene Matching.