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.
Corresponding Author(s):
LIU Huanjun,Email:hjliu74@163.com
引用本文:
. Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision[J]. Frontiers of Electrical and Electronic Engineering in China, 2010, 5(4): 430-440.
Huanjun LIU. Empty glass bottle inspection method based on fuzzy support vector machine neural network and machine vision. Front Elect Electr Eng Chin, 2010, 5(4): 430-440.
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 doi: 10.1016/j.jfoodeng.2004.05.044
3
Shankar N G, Zhong Z W. Defect detection on semiconductor wafer surfaces. Microelectronic Engineering , 2005, 77(3-4): 337–346 doi: 10.1016/j.mee.2004.12.003
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 doi: 10.1109/TSM.2002.804906
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 doi: 10.1109/TSMC.1979.4310076
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 doi: 10.1162/neco.1994.6.5.851
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 doi: 10.1145/288627.288651
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 doi: 10.1109/72.991427
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 doi: 10.1109/TKDE.2004.1269664
15
Smola A J, Sch?lkopf B, Müller K R. The connection between regularization operators and support vector kernels. Neural Networks , 1998, 11(4): 637–649 doi: 10.1016/S0893-6080(98)00032-X
16
András P. The equivalence of support vector machine and regularization neural networks. Neural Processing Letters , 2002, 15(2): 97–104 doi: 10.1023/A:1015292818897
