NDLI: Camera Calibration Based on the RBF Neural Network with Tunable Nodes forVisual Servoing in Robotics

Content Provider	IEEE Xplore Digital Library
Author	Xiaoping Zong Yan Xu Lei Hao Xiaoli Huai
Copyright Year	2006
Description	Author affiliation: Coll. of Electron. & Inf. Eng., Hebei Univ., Baoding (Xiaoping Zong; Yan Xu; Lei Hao; Xiaoli Huai)
Abstract	In this paper, a new approach based on the radial basis function network for solving the camera calibration problem in visual servoing robot is proposed. In this approach, an extended multi-input and multi-output orthogonal forward selection algorithm based on the leave-one-out criterion is applied for the construction of radial basis function (RBF) networks with tunable nodes. This algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. The constructed parsimonious multi-input and multi-output RBF network can complete camera calibration automatically and rapidly, and the simulation has proved that the approach is feasible
Starting Page	5708
Ending Page	5712
File Size	5920283
Page Count	5
File Format	PDF
ISBN	1424402581
DOI	10.1109/IROS.2006.282374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot vision systems Calibration Neural networks Nonlinear distortion Intelligent robots Smart cameras Robot kinematics Radial basis function networks Visual servoing Lenses OFS-LOO Algorithm Visual servoing robot Camera calibration Radial basis function network
Content Type	Text
Resource Type	Article

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