Image splicing method based on feature points, virtual reality system and camera

Abstract

The invention belongs to the technical field of image splicing, and discloses an image splicing method based on feature points, a virtual reality system and a camera. The method comprises the following steps: firstly, extracting fast angular points from two images, calculating harris angular point responses, carrying out grid division, carrying out harris angular point response sorting on the fastangular points in each grid, and screening out strong feature points; secondly, performing orb and brief descriptor description on the strong feature points, performing violent matching respectively,screening feature point matching pairs which are completely consistent in matching, and calculating a guide matrix; mapping the previously extracted fast corner points by using a guide matrix, and carrying out region limited matching; after the feature point matching pairs are obtained, ransac is improved according to the similar position conflict relation of similar homography matrixes, and finally a good result can be obtained. According to the method, the homography between the images can be quickly calculated, and a better splicing result is obtained.

Description

technical field [0001] The invention belongs to the technical field of image stitching, and in particular relates to an image stitching method based on feature points, a virtual reality system, and a camera. Background technique [0002] At present, the closest existing technology: with the rapid development of computer technology, some more traditional representation methods are increasingly unable to meet the requirements of most customers for display methods. Panoramic image stitching is undoubtedly the best choice when a scene needs to be truly, comprehensively and intuitively represented. At present, the image stitching method based on feature points is still the mainstream method. First, the feature points of the image are detected and registered. According to the image matching situation, the transformation model between the images can be calculated, and the stitched image is treated with the obtained model. Transformation is performed, and then the illumination cons...

AI Technical Summary

Problems solved by technology

[0004] To sum up, the problems existing in the prior art are: the current image mosaic algorithm based on feature points has high computational complexity in the process of feature point extraction and registration, The calculation speed is slow, and it cannot cope with some poor image quality situations, such as few feature points or uneven illumination; the ransac time is too long, and the correctness of the obtained homography matrix cannot be guaranteed, resulting in too much time for the entire image stitching Long, not suitable for real-time application

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