Image matching method based on multiscale Fourier-Mellin transform

Abstract

The invention discloses an image matching method based on multiscale Fourier-Mellin transform. The method comprises the following steps of acquiring a reference image I1 and an image to be matched I2 respectively, determining a Gaussian kernel at a pixel position (x, y) in the I2, and calculating a scale space image pixel value L(x, y, t) at the pixel position (x, y) in the I2; further acquiring a scale space image L(t) of the image to be matched I2, and carrying out bilinear interpolation on the L(t) respectively so as to acquire simulation imaging S(t) after the bilinear interpolation; setting a blank image whose size is the same with the size of the L(t), embedding a gray value of each pixel position in the S(t) into the blank image respectively so as to acquire a multiscale image, and calculating a Fourier-Mellin invariant image of the I1 and a Fourier-Mellin invariant image of the multiscale image S'(t) respectively; and calculating a phase correlation matching image between the scale space images L(t) of the I1 and the I2, calculating a convergent-divergent scale estimation value of a pixel position in the I1 and a rotation angle estimation value of the pixel position in the I1 respectively and then acquiring an optimal matching image of the I2.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a frequency-domain image matching method based on Fourier Merlin transform, which is applicable to target tracking, target recognition, video monitoring, video conferencing, biomedicine, information security, remote sensing and telemetry, and aerospace Or frequency-domain image matching in video multimedia. Background technique [0002] At present, with the continuous popularization of computer vision technology, image matching technology is widely used in various fields, and the requirements for image matching technology are also increasing. Traditional image matching algorithms, such as the mean absolute difference (MAD) algorithm is aimed at The spatial domain is processed pixel by pixel. Although the final matching strategies are described in different ways, the premise of using the algorithm is that the size of the target does not change greatly, and the rel...

AI Technical Summary

Problems solved by technology

[0005] However, through the simulation analysis of a large number of images, it is found that the image matching based on the traditional Fourier Merlin transform is only robust to scale changes in a small range, and when the scale difference between the two images is large, the matching fails; therefore , the traditional Fourier Mellin transform method cannot adapt to the situation with large scale differences

Images