Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)

Aerial target recognition based on Zernike moments of information fusion

Authors
Tai Fu, Xiangyi Sun, Dan Fu
Corresponding Author
Tai Fu
Available Online November 2016.
DOI
https://doi.org/10.2991/icmia-16.2016.136How to use a DOI?
Keywords
OpenGL; Pseudo-Zernike; Pose Estimation;.
Abstract

Based on OpenGL and Google library we established the target model library and the canny operator is used for edge detection. Combined with D-S evidence theory and Pseudo-Zernike moments, the air target recognition algorithm based on information fusion is proposed. Pseudo-Zernike moments are invariant to rotation, translation and scale invariance and easy to construct high order moment, compared with the Hu moment redundancy small, and not easy to affect by noise. The D-S theory of evidence solved the conflict absorption which make the result more reliable. The reliability and robustness of the method are verified by the simulation experiment of the target image in Gauss noise

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-256-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmia-16.2016.136How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Tai Fu
AU  - Xiangyi Sun
AU  - Dan Fu
PY  - 2016/11
DA  - 2016/11
TI  - Aerial target recognition based on Zernike moments of information fusion
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.136
DO  - https://doi.org/10.2991/icmia-16.2016.136
ID  - Fu2016/11
ER  -