基于图像局部奇异值向量和BP神经网络分类器的道路导航方法

A ROAD NAVIGATION METHOD BASED ON IMAGE LOCAL SINGULAR VALUE VECTOR AND BP NEURAL NETWORK CLASSIFIER

提出以道路图像矩阵的局部奇异值向量作为特征输入 ,以BP神经网络作为分类器的道路导航方法 .首先将图像分割成若干子图像 ,然后分别对子图像进行奇异值分解 ,提取子图像的代数特征向量 .子图像的特征奇异值组成整个图像的局部奇异值向量 ,作为分类器的输入 .再利用BP神经网络分类器对道路图像进行训练及识别 .实验中处理了三类道路图像 (偏左、偏右、正确方向 ) ,每类用 2 0幅图像作为训练样本 ,30幅用作测试 .结果表明 ,这种道路导航方法的识别率达到了 10 0 % .

This paper presents a method for road navigation,which takes local singular value vectors of image matrix as the feature input and a BP neural network as the classifier. At first,the road image is divided into some sub-images. The algebra feature vectors of all sub-images are extracted using Singular Value Decomposition (SVD) method. With the data of characteristic singular values of sub-images,the Local Singular Value Vector of the whole image are combined,and are used as the input of the classifier. The road images are trained and identified using a BP neural network classifier here. In the experiments,three kinds of road images (left side,right side,and straight direction) are used. Among them,20 pieces of each image are used as training samples,and 30 pieces are used for checking. The results show that the image recognition rate is up to 100% for road navigation with the proposed method.