【摘要】 双目立体视觉标定模型具有非线性,难以建立完备的数学模型来描述不同的镜头畸变和噪声,而BP神经网络可以解决复杂非线性问题。采用BP网络对双目立体视觉进行标定,并在此基础上进一步研究网络层数对双目立体视觉标定的影响。构建了四种不同层数的BP网络模型,实验测试了四种网络的标定能力。结果表明,相对于四层、五层和六层网络模型,三层网络模型具有更快的标定速度、更强的泛化能力和更高的工作精度。更多还原

【Abstract】 BP neural network can solve the complex nonlinear problem,with which it is very difficult for a binocular stereovision to build a complete calibration mathematical model containing all lens distortions and noises. The BP neural network is used for calibrating a binocular stereovision. Furthermore,the effect of the number of network layer on the calibration of the binocular stereovision is studied. Four BP neural networks are constructed with different number of layer. And an experiment is performed to test the calibration capability of the four networks. The test results demonstrate that the network model with three layers has higher calibration efficiency,stronger generalization ability and higher working accuracy,compared with the network models with four,five and six layers.更多还原