Genetic optimized BP network method for camera calibration in binocular vision
- 10.2991/iccia.2012.261How to use a DOI?
- genetic algorithms, neural network, binocular vision, UAV
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.
- © 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/).
Cite this article
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 -