A Novel Camera Calibration Method for Binocular Vision Based on Improved RBF Neural Network

Considering the problems that camera imaging model is complex and operation is complicated, a binocular camera calibration method of RBF neural network based on k-means and gradient method is proposed in this paper. The data center selection method based on the law of clustering error function can obtain hidden nodes and data centers of RBF network accurately. Dynamic learning of data centers, spread constants and weight values based on gradient method can contribute to improving the precision. Experimental results show that the proposed method has high precision and can be well applied in machine vision.