article · 2023
Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection
Cited by 0Abstract and details only
Abstract
In order to solve the problems of long matching time and high mis-matching rate in the feature point matching process of traditional panoramic image stitching algorithms, this paper proposes a panoramic image stitching algorithm based on SIFT and RANSAC.SIFT's algorithm has been advanced to only extract features for overlapping areas (valid regions), thus lessening the computational effort of feature detection, better aligning image features, and enhancing alignment accuracy. On the ground of their corresponding degree, the RANSAC algorithm has been increasingly improved and thus allocated varying weights to distinct feature points, and the higher the matching degree, the higher the weight assigned to the points, thus more precisely discarding mismatched point pairs. A comparison of the number of mismatched points involving relevant parameters, like degree and time of matching, the SIFT and corresponding RANSAC algorithm-based panoramic image stitching algorithm are more excellent.
Cite this paper
Wang, Y. (2023). Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection. https://doi.org/10.1117/12.3004634
Wang, Yubin. “Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection.” vol. 36, 2023, p. 6, https://doi.org/10.1117/12.3004634.
Wang, Yubin. 2023. “Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection.” https://doi.org/10.1117/12.3004634.
Wang, Y. (2023) ‘Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection’. Available at: https://doi.org/10.1117/12.3004634.
@article{wang2023investigation,
title = {{Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection}},
author = {Wang, Yubin},
volume = {36},
pages = {6},
year = {2023},
doi = {10.1117/12.3004634}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
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