一种基于子图特征的快速图像配准算法

针对大面积图像配准鲁棒性和实时性差的问题,提出一种基于子图特征的快速图像配准算法. 提取对比度强、 结构清晰的子图,依次采用改进的Harris检测算法提取角点,4阶梯度向量对角点进行描述,欧氏距离进行特征向量相似性度量,并利用最小二乘法进行变换参数估计,采用双线性插值法重建待配准图像. 实验结果表明,子图法不但比大图法的配准精度高,配准速度快,而且这种思想可以推广到FMT和MI配准法等其他配准算法. 

Fast Image Registration Algorithm Based on Sub-Image Features

For the poor robustness and real-time problem of large area image registration, a new fast image registration algorithm based on the sub-image features was proposed. First, the sub-image with strong contrast, clear structure was extracted. And then sequentially, the improved Harris detector was taken to extract the corners, the 4-order gradient vectors was used to describe the corners, the improved Euclidean distance was taken to measure eigenvectors' similarity, and the transformation parameters were estimated with Least Squares. Lastly, bilinear interpolation was used to reconstruct the registered image. Experimental results show that the registration accuracy and registration speed of the proposed method are higher than the whole image method, and this idea can be extended to other registration algorithm such as FMT and MI registration methods.