Method for Fused Phase and PCA Direction Based on a SIFT Framework for Multi-Modal Image Matching

Abstract

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching. To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper. This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image. Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious. Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points. Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate. The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.