A computationally efficient affine-invariant feature for image matching with wide viewing angles

Abstract

This paper proposes an efficient affine-invariant feature image matching method, especially when there are wide viewing angles. In previous studies, all the AKAZE-based algorithms are built on nonlinear scale-space by using Fast Explicit Diffusion (FED) scheme, which has the advantage of high computational efficiency. But all of them do not have affine invariance and cannot favor the image matching when the image views are too different. Unlike AKAZE features, AFREAK features are extracted from the simulated images obtained by sampling the latitude and longitude angles of an original image, which have good robustness for viewpoint changes. Therefore, we combine the idea of simulating an affine transformation with the construction of a nonlinear scale-space to build an affine-invariant nonlinear scale-space. Then, in order to reduce the redundancy information, we optimize the sampling of longitude angles and simplify the FREAK descriptor. Moreover, a coarse-to-fine matching strategy based on Vector Field Consistency (VFC) and Progressive Sampling Consensus (PROSAC) is developed to further improve the rapidity and correct matching rate. Experimental results on various benchmark image datasets demonstrate that the proposed method is computationally efficient for image matching while has the fully affine invariance.