2023
DOI: 10.3390/e25010106
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Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching

Abstract: Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this furthe… Show more

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Cited by 4 publications
(3 citation statements)
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“…NIS (natural image stitching) [12] method estimates a pixel-to-pixel transformation based on feature matches and the depth map to achieve accurate local alignment. In [13], by increasing feature correspondences and optimizing hybrid terms, sufficient correct feature correspondences are obtained in the low-texture areas to eliminate misalignment. The two methods require additional calculations to enhance robustness, but also are susceptible to the uneven distribution and false matches of feature points.…”
Section: Related Workmentioning
confidence: 99%
“…NIS (natural image stitching) [12] method estimates a pixel-to-pixel transformation based on feature matches and the depth map to achieve accurate local alignment. In [13], by increasing feature correspondences and optimizing hybrid terms, sufficient correct feature correspondences are obtained in the low-texture areas to eliminate misalignment. The two methods require additional calculations to enhance robustness, but also are susceptible to the uneven distribution and false matches of feature points.…”
Section: Related Workmentioning
confidence: 99%
“…The NIS (natural image stitching) [12] method estimates a pixel-to-pixel transformation based on feature matches and the depth map to achieve accurate local alignment. In [13], by increasing feature correspondences and optimizing hybrid terms, sufficient correct feature correspondences are obtained in the low-texture areas to eliminate misalignment. The two methods require additional runtime to enhance robustness, but also are susceptible to the uneven distribution and false matches of feature points.…”
Section: Related Workmentioning
confidence: 99%
“…In order to improve the quality and performance of stitching, many researchers have introduced the concepts of entropy and information theory into image stitching [ 23 , 24 ]. Among existing research, some methods utilize entropy or information theory to select appropriate features for matching.…”
Section: Introductionmentioning
confidence: 99%