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Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm

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Abstract

In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching.

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  1. Margoon Waterfall in Fars Province, Iran.

  2. Uunderground city of Kariz, Kish, Iran.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments which improve the quality of the paper.

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  1. Department of Electrical Engineering, Sirjan Branch, Islamic Azad University, Sirjan, Iran

    Zahra Hossein-Nejad

  2. Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran

    Mehdi Nasri

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  1. Zahra Hossein-Nejad
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Hossein-Nejad, Z., Nasri, M. Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm. Vis Comput 38, 1991–2007 (2022). https://doi.org/10.1007/s00371-021-02261-9

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