Automatic classification of defects on the product surface in grinding and polishing

International Journal of Machine Tools and Manufacture

Volumn 46, Issue 1, 2006, Pages 59-69

  Zhang, Xiang ^a   Krewet, Carsten ^a   Kuhlenkötter, Bernd ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Classification; Defects; Gabor filter; Grinding and polishing; Support vector machine

Indexed keywords

FEATURE EXTRACTION; MATRIX ALGEBRA; POLISHING; STATISTICAL METHODS;

GABOR FILTERS; GRINDING AND POLISHING; SUPPORT VECTOR MACHINES;

GRINDING (MACHINING);

EID: 27144540437     PISSN: 08906955     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmachtools.2005.03.013     Document Type: Article

References (23)

1. V. Vapnik The Nature of Statistical Learning Theory 2000 Springer New York
2. M. Tuceryan, and A. Jain Texture Analysis The Handbook of Pattern Recognition and Computer Vision 1998 World Scientific Publishing Co.
3. T. Randen, and J.H. Husøy Filtering for texture classification: a comparative study IEEE Transaction on Pattern Analysis and Machine Intelligence 21 4 1999 291 310
4. S.K. Sinha, and F. Karray Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm IEEE Transaction on Neural Networks 13 2 2002 393 401
5. J. Lampinen, and E. Oja Distortion tolerant pattern recognition based on self-organizing feature extraction IEEE Transaction on Neural Networks 6 3 1995 539 547
6. I. Ng, T. Tan, and J. Kittler On local linear transform and Gabor filter representation of texture International Conference of Pattern Recognition, Conference C Image, Speech and Signal Analysis vol. III 1992 pp. 627-631
7. J. Iivarinen, and A. Visa An Adaptive Texture and Shape Based Defect Classification Proceeding of International Conference on Pattern Recognition 1998 pp. 117-123
8. A.K. Jain, P.W. Robert, J. Duin, and C. Mao Statistical pattern recognition: a review IEEE Transaction on Pattern Analysis and Machine Intelligence 22 1 2000 4 37
9. J.G. Zhang, and T.N. Tan Brief review of invariant texture analysis methods Pattern Recognition 3 35 2002 735 747
10. K. Laws Rapid Texture Indentification Proceedings of Society of Photo-optical Instrumentation Engineers-Image Processing for Missile Guidance vol. 238 1980 pp. 367-380
11. K. Laws, Textured Image Segmentation. PhD Dissertation, University of Southern California, 1980.
12. J. Daugman Two-dimensional spectral analysis of cortical receptive field profiles Vision Research 20 1980 847 856
13. J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters Journal of Optical Society American 2 7 1985 1160 1169
14. J. Daugman Complete discrete 2D Gabor transforms by neural networks for image analysis and compression IEEE Transaction on Acoustics, Speech, and Signal Processing 36 7 1988 1169 1179
15. A.K. Jain, and F. Farrokhnia Unsupervised texture segmentation using Gabor filters Pattern Recognition 24 1991 1167 1186
16. J. Lampinen, and S. Smolander Self-organizing features extraction in recognition of wood surface defects and color images International Journal of Pattern Recognition and Artificial Intelligence 10 2 1996 97 113
17. B.S. Manjunath, and W.Y. Ma Texture features for browsing and retrieval of image data IEEE Transaction on Pattern Analysis and Machine Intelligence 18 8 1996 837 842
18. B.S. Manjunath, P. Wu, H.D. Shin, and A. texture A texture descriptor for browsing and similarity retrieval Journal of Signal Processing: Image Communication 16 1 2000 33 43
19. R. Haralick, K. Shanmugam, and I. Dinstein Textural features for image classification IEEE Transaction on Systems, Man, and Cybernetics 3 6 1973 610 621
20. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297
21. B. Schoelkopf, C. Burges, and V. Vapnik Extracting Support Data for a Given Task, Extracting Support Data for a Given Task Proceedings of the First International Conference on Knowledge Discovery and Data Mining 1995 AAAI Press Canada
22. T. Joachims Text Categorization with Support Vector Machines: Learning with Many Relevant Features Proceedings of the European Conference on Machine Learning, Germany 1998
23. C. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Transaction on Neural Networks 13 2 2002 415 425