Using neural networks for 3D measurement in stereo vision inspection systems
提出一种立体视觉检测流程，用Hopfield网络解决立体匹配、用反向传播网络校正测量误差，实验表明三维测量结果令人满意。
This paper presents a stereo vision inspection process which derives precise 3D measurements. Two artificial neural networks are used to facilitate the whole measurement process. At first, a simple camera calibration process is developed to derive the focal lengths and the relative information. A Hopfield neural network is used to solve the stereo matching problem, which has been constructed as an energy function. By means of a recursive process, the disparities of extracted feature points are obtained. In addition, a backpropagation neural network-based measurement error correction model for 3D measurement is proposed. It reduces the errors of 3D measurement associated with a part's orientation, position, magnitude and distance between the object and cameras. Four procedural processes are designed to implement this model. Our laboratory experiments demonstrate that the proposed measurement process has a satisfactory measurement result.