Center Extraction of Structured Light Stripe Based on Back Propagation Neural Network

To accurately and rapidly extract the center of the structured-light stripe, we propose a center extraction method based on the back-propagation neural network (BPNN). The basic principle of stripe-center extraction using the BPNN, the method that calculates the ideal center points for network training, and the network-weight tuning algorithm arc presented successively. Factors affecting the center extraction accuracy, such as the number of hidden layer neurons m, number of hidden layers h, and training samples arc investigated. The center-extraction results show that the network can achieve a better stripe center when m=3 and h=1, and the training sample is a random stripe with noise. From the comparison analysis, it can be concluded that the proposed method can achieve higher center-extraction accuracy than both the Steger method and the gray gravity method. The average center-extraction time for a stripe image with the size of 1280 pixel x 960 pixel is 0.04 s, which is only 0.27% of the time required by the Steger method. This further demonstrates that the proposed method has the advantages of high precision and high efficiency. Therefore, it is adequate for sub-pixel center extraction of complex light stripes.