Method for image mosaic based on feature detection operator of second order difference of Gaussian

Abstract

The invention relates to a method for image mosaic based on a feature detection operator of second order difference of Gaussian, which carries out mosaic on sequence images which vary in viewpoint, rotation, proportion, illumination and the like to a certain degree. The method provided by the invention adopts zero crossing point detection of second order difference of Gaussian (D<2>oG) pyramid to replace extreme point detection of the original difference of Gaussian (DoG) pyramid so as to extract scale invariant feature points, thereby effectively simplifying the structure of the Gaussian pyramid. The method comprises the steps of: first, extracting image feature points by using an improved SIFT (Scale Invariant Feature Transform) algorithm; then, searching a rough matching point pairs for the extracted feature points through a BBF (Best-Bin-First) algorithm, and purifying the feature point matching pairs by adopting an RANSAC algorithm so as to calculate an invariant transformation matrix H; and finally, completing seamless mosaic for the images by adopting a fading-in-and-out smoothing algorithm. Experimental results show that the method improves the accuracy and the real-time performance of image mosaic, can well solve problems such as illumination, rotation, scale variation, affine and the like, and realizes automatic mosaic without manual intervention.

Description

technical field [0001] The invention relates to the field of digital image splicing, in particular to an image automatic splicing method based on improved feature detection operator matching, which is used to realize fast and accurate sequence image seamless splicing. Background technique [0002] Image mosaic (Image Mosaic) technology is a technology that spatially matches and aligns a group of image sequences with overlapping parts, and forms a complete new image of a wide-visual scene containing the information of each image sequence after resampling and fusion. As close as possible to the original image, with as little distortion as possible, and without noticeable seams. With the development of digital imaging technology, wide-field and high-resolution images are becoming more and more important in some important fields, and image stitching technology is widely used in panorama stitching, medical image analysis, seabed image analysis, remote sensing Important fields su...

AI Technical Summary

Problems solved by technology

[0006] Research in recent years has shown that SIFT features are unique and can be correctly matched with a high probability during feature matching, which is very important for image stitching. However, the SIFT algorithm has high complexity and a large amount of calculation, which makes the calculation time long and cannot Satisfy real-time splicing

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