<title>Stereoscopic neurovision system for part recognition in intelligent assembly system</title>

Abstract

In this paper, a new 3D recognition method for intelligent assembly system is presented. In this method neural network technology is used to provide new methodologies for solving difficult computational problems in 3D recognition processes. The method can be divided into two parts. In the first part, phase based stereo matching techniques are used to find the correspondence between left and right image in stereo image pair. The Hopfield neural network is established, so that the computation can be implemented efficiently in parallel. A 3D object reconstruction neural network is constructed by using BP neural network. With the results of stereo matching, the 3D configuration and shape can be reconstructed. In the second part, the feature vector of 3D object is constructed by using 3D moment and its invariant. With the results obtained in first parts, ART2 neural network is adopted for neural network classifier. With the ART2 neural network classifier, the 3D objects can be recognized and classified. The method is tested with both synthetic and real parts in intelligent assembly system. Good results are obtained. It is proved through the experiments and actual applications that the method presented in this paper is correct and reliable. It is very suitable for intelligent assembly system.