Visible light image registration method based on salient region

Abstract

The invention relates to the field of image registration, in particular to a visible light image registration method based on salient region. The method comprises: (1) loading an image; (2) extracting the salient region of the image; (3) calculating the region feature describer for the extracted salient region and performing salient region match according to the similarity of the region feature describer; (4) performing local rigid registration to the salient region initially matched in step (3); and (5) adopting the center of the salient region after local rigid registration as a control point to perform overall quadratic polynomial transformation registration. The invention is an automatic image registration method characterized in fastness, accuracy and robustness, and has critical application value in image registration.

Description

technical field [0001] The invention relates to image processing and pattern recognition technology, in particular to an automatic image registration technology based on salient regions. Background technique [0002] At present, the mainstream automatic image registration methods mainly include registration based on feature points, registration based on image gray value, and registration methods based on mutual information. These methods still have some shortcomings. Based on the registration of feature points, for some visible light images with poor quality, it is difficult to accurately extract the feature points. The registration based on image gray value requires that the gray value of the two images must be equal. Consistent, the registration accuracy of images affected by lighting and other environments is not high. Registration based on mutual information takes a long time for registration, and may fall into local extremum, and accurate registration results cannot be ...

AI Technical Summary

Problems solved by technology

These methods still have some shortcomings. Based on the registration of feature points, for some visible light images with poor quality, it is difficult to accurately extract the feature points. The registration based on image gray value requires that the gray value of the two images must be equal. Consistent, the registration accuracy of images affected by lighting and other environments is not high. Registration based on mutual information takes a long time for registration, and may fall into local extremum, and accurate registration results cannot be obtained.

Therefore, manual registration is still used for many low-quality images. The success rate and accuracy of manual registration are relatively high, but it increases the burden on the operator and the registration speed is relatively slow.

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