Depth object recovery using radial basis functions

The main task of robot vision systems is to recover the shape of the objects in a scene and use this information for recognition and classification. The fringe projection technique may be used to recover depth range data. To use this technique, we need to estimate the phase of the projected fringes (related to the object height) by using conventional phase demodulation techniques (phase shifting, phase locked loop, or spatial synchronous detection among others). The final step of the process is to recover the object surface using a calibration procedure. This calibration procedure uses a phase-to-depth conversion obtained from the experimental optical geometry. Set-up parameters to consider are the optical characteristics of the lenses used to project and acquire the fringe patterns, which are usually unknown. Some experimental factors arise when the object under analysis is located near the projector-camera system, such as the diverging fringe projection, the video camera perspective distortion and the use of crossed-optical-axes geometry. In this work, we present a neural network based on radial basis functions (RBF) to estimate actual depth data of the object from the recovered phase using the projected fringe pattern technique. The training set of the neural network is the phase recovered of several calibration planes whose locations in space are known. An advantage of our method is that knowledge of the optical parameters of the experiment is not explicitly required. (C) 1999 Published by Elsevier Science B.V. All rights reserved.