1. bookVolume 12 (2012): Issue 6 (December 2012)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Camera Calibration by Hybrid Hopfield Network and Self- Adaptive Genetic Algorithm

Published Online: 15 Dec 2012
Volume & Issue: Volume 12 (2012) - Issue 6 (December 2012)
Page range: 302 - 308
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

A new approach based on hybrid Hopfield neural network and self-adaptive genetic algorithm for camera calibration is proposed. First, a Hopfield network based on dynamics is structured according to the normal equation obtained from experiment data. The network has 11 neurons, its weights are elements of the symmetrical matrix of the normal equation and keep invariable, whose input vector is corresponding to the right term of normal equation, and its output signals are corresponding to the fitting coefficients of the camera’s projection matrix. At the same time an innovative genetic algorithm is presented to get the global optimization solution, where the cross-over probability and mutation probability are tuned self-adaptively according to the evolution speed factor in longitudinal direction and the aggregation degree factor in lateral direction, respectively. When the system comes to global equilibrium state, the camera’s projection matrix is estimated from the output vector of the Hopfield network, so the camera calibration is completed. Finally, the precision analysis is carried out, which demonstrates that, as opposed to the existing methods, such as Faugeras’s, the proposed approach has high precision, and provides a new scheme for machine vision system and precision manufacture.

Keywords

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