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Proceedings Paper

Three-dimensional object recognition in intelligent assembly system
Author(s): Yingen Xiong; Guangzhao Zhang
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Paper Abstract

In this paper, a new three dimensional 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 three dimensional 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 into stereo image pair. The Hopfield neural network is adopted to implement the stereo matching process. A suitable architecture of neural network is established, so that the computation can be implemented efficiently in parallel. A three dimensional 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 id 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.

Paper Details

Date Published: 25 March 1998
PDF: 12 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304853
Show Author Affiliations
Yingen Xiong, Zhongshan Univ. (United States)
Guangzhao Zhang, Zhongshan Univ. (China)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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