基于改进Harris角点的图像配准算法
摘要
Harris corner detector is a classic tool to extract feature. It is stable to illumination change and rotation but unstable to more complicated transform. In order to register images with different viewpoints, we extend Harris corner detector to scale-space to gain invariance to scale change, then we apply affine shape adaptation to the scale invariant point until convergence is reached, giving it invariance to affine transform. With these local features, we use general feature descriptor and matching algorithm to generate matches and then use the matches to calculate the geometric transform matrix, which enables the final registration. Result shows that our algorithm can get more accurate matches than scale invariant feature transform SIFT, and less difference exists between registered images.