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Proceedings Paper

A positioning method for the optimal seam in binocular visual image stitching
Author(s): Mei Hui; Zheng Zhou; Ruifeng Yuan; Liquan Dong; Ming Liu; Yuejin Zhao
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Paper Abstract

Image stitching technology is an important technology for generating large field of view images, and it is also a research hotspot in the field of digital image processing. Binocular imaging technology is closely related to image stitching technology and is widely used in robot vision, 3D reconstruction and other fields. However, in the existing image stitching methods, when the image with the distant view and the near object are coexisted, due to the parallax, the near object often has partial missing or unnatural transition, which leads to the poor image stitching effect and low naturalness. Aiming at this problem, we uses the characteristics of the binocular camera that can obtain depth information, and regards the information as one of the basis of the seam positioning. A strategy based on the combination of geometry, color and depth features is proposed, and we constructed a seaming algorithm model for positioning the optimal seam. The comparison experiment results show that the stitching naturalness is improved by 37% according to the proposed seaming algorithm.

Paper Details

Date Published: 12 March 2020
PDF: 10 pages
Proc. SPIE 11434, 2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 114340D (12 March 2020); doi: 10.1117/12.2542395
Show Author Affiliations
Mei Hui, Beijing Institute of Technology (China)
Zheng Zhou, Beijing Institute of Technology (China)
Ruifeng Yuan, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 11434:
2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments
Juan Liu; Baohua Jia; Xincheng Yao; Yongtian Wang; Takanori Nomura, Editor(s)

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