Research on Seamless Image Stitching based on Depth Map

Chengming Zou, Pei Wu, Zeqian Xu

2017

Abstract

Considering the slow speed of panorama image stitching and the ghosting of traditional image stitching algorithms, we propose a solution by improving the classical image stitching algorithm. Firstly, a SIFT algorithm based on block matching is used for feature matching which was proposed in our previously published paper. Then, the collaborative stitching of the color and depth cameras is applied to further enhance the accuracy of image matching. Finally, according to a multi-band blending algorithm, we obtain a panoramic image of high quality through image fusion. The proposed algorithm is based on two problems in the technology of feature-based image stitching algorithm, the algorithm’s real-time and ghosting. A series of experiments show that the accuracy and reliability of the improved algorithm have been increased. Besides a comparison with AutoStitch algorithm illustrates the advantage of the improved algorithm in efficiency and quality of stitching.

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Paper Citation


in Harvard Style

Zou C., Wu P. and Xu Z. (2017). Research on Seamless Image Stitching based on Depth Map . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 341-350. DOI: 10.5220/0006146303410350


in Bibtex Style

@conference{icpram17,
author={Chengming Zou and Pei Wu and Zeqian Xu},
title={Research on Seamless Image Stitching based on Depth Map},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={341-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006146303410350},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Research on Seamless Image Stitching based on Depth Map
SN - 978-989-758-222-6
AU - Zou C.
AU - Wu P.
AU - Xu Z.
PY - 2017
SP - 341
EP - 350
DO - 10.5220/0006146303410350