Image splicing method and system based on straight line feature and key point feature fusion

Abstract

The invention discloses an image splicing method and system based on straight line feature and key point feature fusion, and relates to the technical field of image splicing and pattern recognition. In order to extract more features on a low-texture image as much as possible, linear features are introduced, that is, the linear features and key point features on the image are detected at the same time, feature extraction of the to-be-spliced image is enriched, and a moving direct linear transformation algorithm combining the linear features and the key point features is introduced. Linear features and key point features are used for jointly guiding local homography transformation of each grid, in order to prevent the linear features from weakening the alignment capacity of the key point features, the fusion degree of the linear features is balanced through lambda constant coefficients, and it is verified that the moving direct linear transformation algorithm with the balance factor hasbetter low texture image alignment capacity, and the problem of misalignment caused by insufficient SIFT feature points of a low-texture image is effectively solved.

Description

technical field [0001] The invention belongs to the field of image splicing and pattern recognition, and more particularly, relates to an image splicing method and system based on the fusion of straight line features and key point features. Background technique [0002] Image stitching is the process of combining multiple images into a larger image with a wider field of view. The quality of stitching needs to be judged from three aspects: image alignment accuracy, shape distortion, and overall naturalness. [0003] In order to improve the alignment ability and flexibility of the model, a method based on grid deformation (APAP algorithm) is proposed, which divides the target image into grids of R*C size, and the pixels in each grid are calculated separately The corresponding local homography matrix, such an image stitching method is more delicate, which greatly improves the alignment accuracy of image stitching. In order to further improve the alignment accuracy of the stitc...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the defects and improvement needs of the original AANAP algorithm in the prior art for low-texture images and weak-texture areas of natural images, poor alignment accuracy and improvement requirements, the present invention provides an image stitching method based on the fusion of straight line features and key point features And the system, its purpose is to enhance the feature extraction ability of AANAP algorithm, improve the alignment ability of the algorithm in the weak texture area of ​​the image, further reduce the perspective distortion and deformation of the non-overlapping area of ​​the stitched image, improve the overall stitching quality, and then enhance its ability in low texture Robustness and versatility in texture image and natural image stitching

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