Establishing a large amount of point correspondences using patch-based affine-scale invariant feature transform for fisheye images

Affine-scale invariant feature transform (ASIFT) has performed very well for perspective images, but the intrinsic nonlinear distortion makes it difficult to utilize ASIFT directly for fisheye images to find a large amount of correspondences. We reuse ASIFT and propose a new feature matching method for uncorrected fisheye images. First, we employ the Gaussian hemi-image to divide fisheye images into patches. In this process, we introduce Plucker coordinates and side operator to establish the correspondences between the hemispherical projection model, Gaussian hemi-image, and fisheye images. Simultaneously, maximum stable extreme region algorithm is used to detect the target regions. After that, each patch in these regions is simulated at different orientation parameters in ASIFT, and SIFT algorithm is applied to all simulated patches. Experiments on real-world images show that the proposed method can achieve good performance: the numbers of the point correspondences increase greatly with satisfactory accuracy, reliability, and efficiency.