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Image stitching by feature positioning and seam elimination

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Abstract

Parallax, exposure differences, ghost and efficiency handling are the challenging tasks for image stitching, which is regarded as the promising approach to resolve the issues in the tasks. In this paper, we propose a novel stitching method that locates the overlapped regions of the input images, and records the feature points at the same time. The warping of each image is then guided by a mesh interpolation map in a local warp model. We also propose an arc function weight model to eliminate image chromatic aberration. It is proved via the validation cases that our approach shows constantly the better performance than the AutoStitch, APAP, SPHP, ANAP, ELA and many other state-of-the-art methods. Our method can effectively avoid mismatched points, improve the matching efficiency of feature points of large-size images by about 60%, eliminate the color difference seam and ghost of the image, and still have good accuracy and stability in complex scenes.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (61762018), the Guangxi 100 Youth Talent Program (F-KA16016), the Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, China (GXZDSY2016-03), the research fund of Guangxi Key Lab of Multi-source Information Mining & Security (18-A-02-02), Natural Science Foundation of Guangxi (2018GXNSFAA281310) and the Innovation Project of Guangxi Graduate Education (XYCSZ2019075).

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Author notes

  1. Yunbai Qin and Jialiang Li contributed equally to this work.

Authors and Affiliations

  1. College of Electronic Engineering, Guangxi Normal University, Guilin, 541000, Guangxi, China

    Yunbai Qin, Jialiang Li, Pinqun Jiang & F. Jiang

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  1. Yunbai Qin
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  2. Jialiang Li
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  4. F. Jiang
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Correspondence to Yunbai Qin or F. Jiang.

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Qin, Y., Li, J., Jiang, P. et al. Image stitching by feature positioning and seam elimination. Multimed Tools Appl 80, 20869–20881 (2021). https://doi.org/10.1007/s11042-021-10694-6

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  • DOI: https://doi.org/10.1007/s11042-021-10694-6

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