基于射影变换模型的图像特征点集配准

Image Registration from Feature Point Sets Based on Projective Transformation

图像配准是计算机视觉中目标识别的一种基本方法,其目的是在待识别图像中寻找与模型图像的最佳匹配。该文讨论以特征点表示的图像间的配准问题,利用矩阵分解理论推导出射影变换下特征点集配准的闭合公式,给出变换参数估计的算法,并用模拟数据和图像角点检测的真实数据加以验证。实验表明该方法精确、稳定、受噪声影响小。

This paper investigates the image registration from feature point sets.Image registration is a fundamental object recognition method in computer vision and it aims to find best matches between two or more point sets when there are geometric distortions,point measurement errors and contamination present.Up to now,closed form solution has been developed for the similarity transformation.This paper concentrates on image registration from feature point sets whose transformation is projective and gives the closed form solution of the transformation parameters using the matrix decomposition theories.The algorithms are evaluated on both synthetic and real world images and the experiment results show that the methods given in this paper are accurate,stable and are only affected slightly by noise.