Genetic optimized BP network method for camera calibration in binocular vision

DOI

10.2991/iccia.2012.261How to use a DOI?

Keywords

genetic algorithms, neural network, binocular vision, UAV

Abstract

Camera calibration is the first step of positioning using binocular vision. Owning to the approximation capability of the neural network, a complex mathematical model needed by traditional calibration methods can be avoided. However the general neural network methods have their drawbacks to reduce its accuracy. This paper presents searching algorithm for the best structure and parameters of a neural network using an improved genetic algorithm (GA). The experiments show that this method can be used to establish a mapping between 2D coordinates and 3D coordinates directly and accurately, which is better than traditional calibration and general BP network methods.

Copyright

© 2013, 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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TY  - CONF
AU  - Xingchen Hu
AU  - Honglei An
AU  - Hongxu Ma
AU  - Haibin Xie
AU  - Hongtao Xue
PY  - 2014/05
DA  - 2014/05
TI  - Genetic optimized BP network method for camera calibration in binocular vision
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1064
EP  - 1067
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.261
DO  - 10.2991/iccia.2012.261
ID  - Hu2014/05
ER  -