基于奇异值分解的图像匹配方法

传统的图像匹配方法中， 由于实时图和参考图之间存在着灰度差异和几何形变，仅用灰度作为特征进行匹配算法的性能很容易受到影响。文中提出了一种基于奇异值分解的图像匹配方法。该方法首先利用奇异值分解方法，求出模板图像矩阵的奇异值及奇异值向量，用它们作为模板图像的特征代替传统算法中的灰度对两幅待匹配图像进行全局搜索定位。由于奇异分解方法所特有的优越性，匹配实验取得了良好效果。实验结果验证了该方法的有效性。

Image matching method based on singular value decomposition

Because of the presence of the difference in grey level and the distortion between object image and reference image, traditional matching method will be degraded. We need more useful characters. In this paper, a new image matching method based on singular value decomposition (SVD) is proposed. Firstly, object image is decomposed, its SVD value and SVD eigenvector are obtained, and then they are applied as characters of the object image in matching process. Because of the excellent characters of SVD, the experimental results show that this image matching method could bear image distortion to some extent and is very promising.