Abstract
This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
Similar content being viewed by others
References
Chang, C. A., Chen, L. and Ker, J. (1991) Efficient measurement procedures for compound part profile by computer vision. Computer and Industrial Engineering, 21, 375–377.
Dubois, S. R. and Glanz, F. H. (1986) An autoregressive model approach to two dimensional shape classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 55–66.
Fu, K. (1982) Pattern recognition for automatic visual inspection. IEEE Computer, 15, 34–40.
Gonzales, R. C. and Safabaksh, R. (1982) Computer vision techniques for industrial applications and robot control. IEEE Computer, 15, 17–32.
Gonzales, R. C. and Wintz, P. (1987) Digital Image Processing, Addison-Wesley, Reading, MA.
Haralick, R. M. and Shapiro, L. G. (1992) Computer and Robot Vision, Addison-Wesley, Reading, MA.
Haykin, S. (1994) Neural Networks: A Comprehensive Foundation, Macmillan, Hampshire, UK.
Hsieh, K. and Chang, C. A. (1994) Automated part recognition and profile inspection for ¯exible manufacturing systems, in Proceedings of Mid-America Conference on Intelligent Systems, Kansas State University, Overland Park, KS, pp. 73–79.
Kashyap, R. L. and Chellappa, R. (1981) Stochastic models for plane closed boundary analysis: representation and reconstruction. IEEE Transactions on Information Theory, 27, 627–637.
Koplowitz, J. and Bruckstein, A. M. (1989) Design of perimeter estimators for digitized planar shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 611–622.
Makridakis, S. and Wheelwright, S. C. (1978) Forecasting Methods and Application, John Wiley & Sons, New York.
Mokhtarian, F. and Mackworth, A. (1986), Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 34–44.
Ventura, J. A. and Dung, Z. (1993) Algorithms for computerized inspection of rectangular and square shapes. European Journal of Operation Research, 68, 256–277.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
LEE , YH., MOON , S. & LEE , H. A new approach for automated parts recognition using time series analysisand neural networks. Journal of Intelligent Manufacturing 8, 167–175 (1997). https://doi.org/10.1023/A:1018565022922
Issue Date:
DOI: https://doi.org/10.1023/A:1018565022922