In order to improve the accuracy and stability of stereo vision calibration, a novel stereo vision calibration approach based on the group method of data handling (GMDH) neural network is presented. Three GMDH neural networks are utilized to build a spatial mapping relationship adaptively in individual dimension. In the process of modeling, the Levenberg–Marquardt optimization algorithm is introduced as an interior criterion to train each partial model, and the corrected Akaike’s information criterion is introduced as an exterior criterion to evaluate these models. Experiments demonstrate that the proposed approach is stable and able to calibrate three-dimensional (3D) locations more accurately and learn the stereo mapping models adaptively. It is a convenient way to calibrate the stereo vision without specialized knowledge of stereo vision.
© 2012 Optical Society of America
Original Manuscript: August 24, 2011
Revised Manuscript: November 18, 2011
Manuscript Accepted: November 27, 2011
Published: February 27, 2012
Vol. 7, Iss. 5 Virtual Journal for Biomedical Optics
Bingwen Chen, Wenwei Wang, and Qianqing Qin, "Stereo vision calibration based on GMDH neural network," Appl. Opt. 51, 841-845 (2012)