Multiscale structural feature transform for multi-modal image matching

Abstract

Robust and reliable multi-modal image matching is an essential and challenging technique in the multi-modality involved scenarios for image fusion, image mosaic, and visual navigation. Existing image matching methods cannot simultaneously solve the scale and rotation distortion caused by different viewpoints and the nonlinear radiation distortion caused by different imaging environments or mechanisms. To address these problems, a multiscale structural feature transform method is proposed for multi-modal image matching, which consists of three blocks: multiscale structural feature detector, symmetric log-polar descriptor, and point matching with position correction. The multiscale structural feature detector constructs the phase congruency map based Difference of Gaussian image pyramid for the scale-invariant feature points detection to tackle the nonlinear radiation distortion. The feature points are detected within 10-neighborhood instead of 26-neighborhood to produce sufficient points on the phase congruency map with very sparse texture. The symmetric log-polar descriptor utilizes the multiscale structure principal direction and symmetric bins to improve the robustness of the descriptor to rotation and nonlinear radiation distortion. At last, the point matching with position correction corrects the position of feature points to compensate the position offset between different images caused by the scale and rotation distortion and the nonlinear radiation distortion, which increases the number of correct matches and improves the transformation consistency between matched points. In the experiment, the proposed method in this paper is compared with seven state-of-the-art methods. Experimental results verify the superiority of the proposed method and its robustness to the scale and rotation distortion and the nonlinear radiation distortion. In addition, we demonstrate the effectiveness of the proposed three blocks by the ablation experiments.