An Image Stitching Method Based on Best Stitching Plane and Local Features

Abstract

The invention relates to an image splicing method based on the optimal splicing plane and local features. The method includes the following steps: S1: Estimating the depth information of the image; S2: Performing image segmentation based on the depth information, and segmenting the original image into to-be-matched images. The accurate area and the non-registration area are determined, and the best splicing plane is determined with the two dividing lines as the sides; S3: Using the best splicing plane as the reference, the respective dividing lines are used as the axes to calculate the rotation angles, and transform the image into the reference plane; S4: Extract FAST feature points and feature descriptions of feature points in the area to be spliced; S5: Calculate Hamming distance for feature point matching, use depth information and RANSAC algorithm to perform precise matching, and obtain matching point pairs; S6: Use registered features Point calculation transformation matrix is ​​obtained to obtain the transformed registration area image, and a weighted fusion method based on focus distance is used to obtain the spliced ​​image. This method can overcome the stretching distortion caused by global correction under large viewing angle differences and obtain a spliced ​​image that is more in line with human vision.

Description

technical field [0001] The invention belongs to the technical field of image stitching, and relates to an image stitching method based on an optimal stitching plane and local features. Background technique [0002] Image stitching technology is to stitch multiple small-view images with overlapping areas into a complete large-view image, which has better image quality than the large-view images obtained by hardware. Image stitching mainly includes the following three steps: preprocessing, image registration and image fusion. Among them, image registration is the core and key of image stitching, and image fusion is the last step in generating a wide-view image. The current image registration methods are mainly divided into three categories: methods based on transform domain, methods based on gray correlation and methods based on features. The feature-based image registration method is the best method, but the current algorithm generally uses an image to be stitched as a refe...

AI Technical Summary

Problems solved by technology

When there is a large viewing angle difference between the processed images to be stitched, it is easy to generate a large stretching distortion, which affects the stitching result

At the same time, in order to improve the real-time performance of the algorithm, most of the splicing algorithm fusion methods use the general weighted fusion method, which is prone to splicing traces and ghost images.

Images