Projective transformation image matching method based on transformation-invariant low-rank texture

Abstract

The invention discloses a projection transformation image matching method based on transformation-invariant low-rank texture, which mainly solves the defect that the existing technology cannot complete projection transformation image matching. The solution is: 1. Input two images containing projection transformation and perform automatic detection and extraction of low-rank texture areas respectively; 2. Perform TILT transformation on the detected low-rank texture areas to obtain their respective local transformation matrices, and use local The transformation matrix corrects the two input images; 3. Detect feature points on the two corrected images, and establish scale-invariant feature descriptors and geometric shape descriptors for the feature points; 4. Combine scale-invariant feature descriptors and geometry The shape descriptor establishes a new feature descriptor, and uses Euclidean distance to measure the similarity of the new descriptor to complete image matching. The invention can extract feature points with higher repetition rate and correct matching rate, improves calculation efficiency, and can be used for image fusion, image splicing and three-dimensional reconstruction.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a projective transformation image matching method, which can be applied to the fields of target recognition and tracking, image splicing and three-dimensional reconstruction. Background technique [0002] In the fields of target recognition, image stitching and 3D reconstruction, it is necessary to match multiple views of the same scene. In general, feature-based image matching methods can be used for image matching, mainly because some image features are invariant to image scale, rotation and affine transformation, and only using feature information to find the geometric relationship between images has The advantages of high computational efficiency. However, when there is a large degree of projection distortion between the two images, it is often difficult to extract features with projection invariance in the existing technology, resulting in insufficient ...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art that cannot complete projection transformation image matching, and propose a projection transformation image matching method based on transformation invariant low-rank texture, which eliminates projection distortion of the input image through TILT transformation, and projects The matching problem of transformed image is transformed into the image matching problem of similar transformation to obtain more accurate matching point pairs

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