Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision

Abstract

This paper develops a computerized empty glass bottle inspection method. Wavelet transform and morphologic methods were employed to extract features of the bottle body and the finish from images. Fuzzy support vector machine neural network was adopted as classifiers for the extracted features. Experimental results indicated that the accuracy rate can reach up to 97% by using the method developed to inspect empty glass bottles.

Keywords

machine vision / support vector machine (SVM) / neural network (NN) / morphologic method / wavelet transform

References

[1] Aleixos N. Blasco J, Molto E, Navarron F. Assessment of citrus fruit quality using a real-time machine vision system. In: Proceedings of the 15th International Conference on Pattern Recognition. 2000, 1: 482–485

[2] Du C J, Sun D W. Comparison of three methods for classification of pizza topping using different colour space transformations. Journal of Food Engineering, 2005, 68(3): 277–287

[3] Shankar N G, Zhong Z W. Defect detection on semiconductor wafer surfaces. Microelectronic Engineering, 2005, 77(3-4): 337–346

[4] Jiang B C, Tasi S L, Wang C C. Machine vision-based gray relational theory applied to IC marking inspection. IEEE Transactions on Semiconductor Manufacturing, 2002, 15(4): 531–539

[5] Ma H M, Su G D, Wang J Y, Ni Z. A glass bottle defect detection system without touching. In: Proceedings of the First International Conference on Machine Learning and Cybernetics. 2002, 2: 628–632

[6] Shafait F, Imran S M, Klette-Matzat S. Fault detection and localization in empty water bottles through machine vision. In: Proceedings of E-Tech 2004. 2004, 30–34

[7] Vapnik V N. Statistical Learning Theory. New York: John Wiley & Sons Inc, 1998

[8] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66

[9] Gonzale R C, Woods R E. Digital Image Processing. 2nd ed. Upper Saddle River: Prentice Hall, 2002

[10] Mallet S. A Wavelet Tour of Signal Processing. 2nd ed. New York: Academic Press, 1999

[11] Vapnik V, Levin E, Le Cun Y. Measuring the VC-dimension of a learning machine. Neural Computation, 1994, 6(5): 851–876

[12] Dumais S, Platt J, Heckerman D, Sahami M. Inductive learning algorithms and representations for text categorization. In: Proceedings of the Seventh International Conference on Information and Knowledge Management. 1998, 148–155

[13] Hsu C W, Lin C J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415–425

[14] Tan Y, Wang J. A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(4): 385–395

[15] Smola A J, Schölkopf B, Müller K R. The connection between regularization operators and support vector kernels. Neural Networks, 1998, 11(4): 637–649

[16] András P. The equivalence of support vector machine and regularization neural networks. Neural Processing Letters, 2002, 15(2): 97–104

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