Neural network digital fringe calibration technique for structured light profilometers

We present a novel neural network signal calibration technique to improve the performance of triangulation-based structured light profilometers based on digital projection. The performance of such profilometers is often hindered by the capture of aberrated pattern intensity distributions, and hence we address this problem by employing neural networks in a signal mapping approach. We exploit the generalization and interpolation capabilities of a feed-forward backpropagation neural network to map from distorted fringe data to nondistorted data. The performance of the calibration technique is gauged both through simulation and experimentation, with simulation results indicating that accuracy can be improved by more than 80%. The technique requires just one image cross section for calibration and hence is ideal for rapid profiling applications.