图像配准是将不同条件下(时间、传感设备、气候、角度等)得到的两幅或者多幅图像进行匹配、叠加的过程。通常,每种配准技术都是针对某一类具体应用的,有一定的局限性。文中提出了一种融合的、基于特征点的图像配准方法,首先采用小波变换和USAN区域特性相结合的角点检测方法,然后利用互相关和RANSAC相结合的方法进行特征点匹配,最后采用薄板样条法求解点变换矩阵。通过这种方法可以弥补不同配准方法的不足,提高配准精度。通过对人物、景物等大量图像进行实验分析,证明此算法具有很好的配准精度、环境适应性和实时性。

Image registration is a process of matching and superimposing two or more images obtained under different conditions (time, sensing equipment, climate, angle, etc.). Typically, each registration technique is specific to a particular class of application, with some limitations. An fusion image registration algorithm based on feature is presented in this thesis. Firstly, the corner detection method is combined with wavelet transform and USAN regional features. Then, the feature points are matched by cross correlation and RANSAC(Random Sample Consensus). Finally, the thin-plate spline algorithm is adopted. This method can make up for the shortcomings of different registration methods to improve the accuracy. Through experiments on a large number of images, such as characters and scenes, it is proved that the algorithm has good registration accuracy, environmental adaptability and real-time.