为了能准确快速提取特征和可靠匹配特征点对,提出一种稳健的基于特征点的配准算法。首先改进了Plessey角点检测算法,有效提高所提取特征点的速度和精度。然后利用相似测度归一化互相关(Normalized cross correlation, NCC),通过双向最大相关系数匹配的方法提取出初始特征点对,用随机采样符合法(Random sample consensus, RANSAC)来剔除伪特征点对,实现特征点对的精确匹配。最后用正确匹配特征点对实现图像的配准。实验表明,该方法能够快速准确地提取两幅图像间的对应特征点,大大降低了误匹配的概率,两幅图像光照不一致、重复性纹理、旋转角度比较大等较难自动匹配情形下,仍能有效地实现图像的配准。

To extract exactly feature points and match reliably feature point pairs, a robust image registration method based on feature points matching is proposed. Firstly, corners are extracted with the improved Plessey operator which can improve the precision and speed of the feature points extraction, the initial feature point pairs are extracted with the methods of normalized cross correlation (NCC) and bidirectional greatest correlative coefficient (BGCC), and the false feature point pairs are rejected by the random sample consensus (RANSAC) algorithm. Finally, the correct matching feature point pairs are used to realize image registration. The experimental results indicate that this method can fast and accurately extract the corresponding feature points between two images and greatly reduce the probability of false feature points matching. Image registration can be carried out effectively even under varions of conditions of different light, bigger rotation, and repetitive texture.