Automatic defects classification with p-median clustering technique

2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Volumn , Issue , 2008, Pages 775-780

  Sidorov, Denis ^a   Wei, Wong Soon ^a   Vasilyev, Igor ^b   Salerno, Saverio ^c  

^a ASTI Holdings   (Singapore)

^b MATROSOV INSTITUTE FOR SYSTEM DYNAMICS AND CONTROL THEORY   (Russian Federation)

^c UNIVERSITY OF SALERNO   (Italy)

Author keywords

Automatic defect classification; Clustering; Content based image retrieval; Semiconductor manufacturing

Indexed keywords

AUTOMATIC DEFECT CLASSIFICATION; AUTOMATIC DEFECTS CLASSIFICATIONS; CLASSIFICATION FUNCTIONS; CLASSIFICATION METHODS; CLUSTERING; CLUSTERING TECHNIQUES; COMPARATIVE ANALYSIS; CONTENT-BASED IMAGE RETRIEVAL; DEFECT RECOGNITION; INSPECTION IMAGES; MANUFACTURING PROCESS; OFFLINE; P MEDIANS; REAL TIME; SEMICONDUCTOR INDUSTRIES; SEMICONDUCTOR MANUFACTURING; TEACHING PROCESS; TWO STAGES; VISION SYSTEMS;

CLUSTER ANALYSIS; CONTENT BASED RETRIEVAL; DEFECTS; ELECTRIC CONDUCTIVITY; INFORMATION RETRIEVAL; ROBOTICS; SEMICONDUCTOR DEVICE MANUFACTURE; SEMICONDUCTOR MATERIALS;

COMPUTER VISION;

EID: 64549117741     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICARCV.2008.4795615     Document Type: Conference Paper

References (46)

1. Avella P., Boccia, M., Di Martino, C., Oliviero G., Sforza A., and Vasil'ev, I. A decomposition approach for a very large scale optimal diversity management problem 4OR, 3:23-37 (2005)
2. Avella, P. and Sassano A. and Vasil'ev, I. Computational study of large-scale p-Median problems. Mathematical Programming. in press, available online (2006)
3. Freund Y. and Schapire R. E. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1): 119-139 (1997)
4. Avella, P. and Sforza, A. Logical reduction tests for the p-median problem. Annals of Operations Research. 86: 105-115 (1999)
5. Beasley, J. E. Lagrangean heuristics for location problems. EJOR, 65:383-399 (1993)
6. Bradley P.S. and Mangasarian O.L. Feature selection via concave minimization and support vector machines. Machine Learning Proceedings of the Fifteenth International Conference (ICML'98), San Francisco, California, J. Shavlik (ed). 82-90 (1998)
7. Briant, O. and Naddef, D. The optimal diversity management problem. Operations Research, 52(4): 515-526 (2004)
8. Burke, E.K., Newall, J.P. and Weare, R.F. A memetic algorithm for university exam timetabling. The Practice and Theory of Automated Timetabling II: Selected Papers from the 2nd International Conference on the Practice and Theory of Automated Timetabling, August 20th-22nd, University of Toronto. Vol.1408:241-250 (1998)
9. Burke, E. and Carter, M. The practice and theory of automated timetabling II Selected Papers from the 2nd International Conference on the Practice and Theory of Automated Timetabling. August 20th-22nd, Springer Lecture Notes in Computer Science Series. 1408, University of Toronto (1998)
10. Chang, C.-C and Lin, C-J. LIBSVM: a library for support vector machines. Dept. of Computer Science and Information Engineering, National Taiwan University (2001)
11. Corne, D. P. Ross, P. and Fang, H.-L. Evolutionary timetabling: practice, prospects and work in progress Proc. of UK planning and Scheduling SIG Workshop (1994)
12. Cornuejols, G., Fisher M.L., and Nemhauser, G.L. Location of bank accounts to optimize float : An analytic study of exact and approximate algorithms. Management Science. 23:789-810 (1977)
13. Pavlidis, T. Algorithms of machine graphics and image processing. Radio i Svyaz, (1986).
14. Joachims, T. Making large-scale SVM learning practical. Advances in Kernel Methods. Support Vector Learning (B. Scholkopf (Edt)), MIT Press, 23-34 (1999).
15. Scholkopf, B., Burges, C.J.C and Smola, A.J. Making large-scale SVM learning practical. Advances in Kernel Methods. Support Vector Learning, MIT Press (1999).
16. Grötschel, Lovasz, M. L. and Schrijver, A. Geometric algorithms and combinatorial optimization Springer-Verlag, (1993)
17. Gonzalez, R. and Woods, R. Digital image processing, Prentice Hall (2002).
18. Nemhauser, G.L. and Wolsey, L.A Integer and combinatorial optimization, Willey (1988).
19. Martello, S. and Toth, P. Knapsack problems: algorithms and computer implementations John Wiley and Sons (1990)
20. Papoulis, S. Characteristic functions McGraw-Hill, New York (1991)
21. Haykin, S. Neural networks: a comprehensive foundation MacMillan Publ. Company (1994)
22. Hu, H. Visual pattern recognition by moment invariants IRE Trans. Information Theory, 8(1), 1962, 179-187.
23. Kokaram, A., Bornard, B., Rares, A., Sidorov, D.N., Chenot, J.-H., Laborelli, L. and Biemond J. Robust and automatic digital restoration systems SMPTE Motion Imaging Journal, 112(7):225-231,(2003).
24. Marshall, S. Review of shape coding techniques Image and Vision Computing, 7(4):281-294 (1989)
25. Kumar A. and Pang, G. K. H Defect detection in textured materials using Gabor filters, IEEE Transactions on Industry Applications, 32(2):425-440 (2001)
26. Kunttu, I., Lepisto L., and Visa, A. Efficient Fourier shape descriptor for industrial defect images using wavelets. SPIE Optical Engineering Letters, 44(8):345-351 (2005)
27. Niskanen, M. Silven, O. and Kauppinen, H. Wood inspection with non-supervised clustering. Machine Vision and Applications Journal, 13(4-6):275-285, (2003)
28. Vinod, H.D. Integer programming and the theory of groups. Journal of the American Statistical Association. 6:506-519 (1969)
29. Rao, M.R. Cluster analysis and mathematical programming. Journal of the American Statistical Association. 6:622-626 (1971).
30. Mulvey, J.M. and Crowder, H.P. Cluster analysis: an application of Lagrangian relaxation Management Science, 25:329-340 (1979)
31. Hansen, P. and Jaumard, B. Cluster analysis and mathematical programming Mathematical Programming, 79:191-215 (1997)
32. Fung G. and O.L. Mangasarian, O. L. Semi-supervised support vector machines for unlabeled data classification Optimization Methods and Software. 1:29-44 (2000)
33. Kariv, O. and Hakimi, L. An algorithmic approach to network location problems. II: the p-medians. Operations Research. 37(3):539-560 (1979)
34. Hansen, P., Mladenovic, N., and Perez-Brito, D. Variable neighbourhood decomposition search. Journal of Heuristics, 7:335-350 (2001)
35. Mladenovic, N., Brimberg J., Hansen, P. and Perez, J. p-Median problem: a survey of metaheuristic approaches. European Journal of Operations Research. 179(3):927-939 (2007)
36. Tamura H., Mori, S. and Yamawaki, T. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man and Cypernetics. 8:460-472 (1978)
37. Ohanian, P. and Dubes, R. Performance evaluation for four classes of textural features. Pattern Recognition. 25:819-883 (1992)
38. Resende, M. G. C. and Werneck, R. F. F. A hybrid heuristic for the p-median problem. J. Heuristics 10(1):59-88 (2002)
39. Resende, M.G.C. and Werneck, R.F. On the implementation of a swap-based local search procedure for the p-median problem AT T L a b s , T D -5E4QKA (2002)
40. Iivarinen, J. and Visa, A. Shape recognition of irregular objects. SPIE Proceedings: Intelligent Robots and Computer Vision XV. Vol. 2904 (1996)
41. Mery, D. and Berti, M.A. Automatic detection of welding defects using texture features. CTIP2003 Proceedings, Berlin 676-681 (2003)
42. Sidorov, D. N., Lerallut, J.-F., Cocquerez, J.-P. and Azpiroz, J. Robust retrieval from compressed medical image archives Proceedings of SPIE: Medical Imaging, 5748:419-426 San Diego (2005)
43. CPLEX, ILOG, ILOG CPLEX 8.0 Reference manual (2002)
44. MPI, University of Tennessee, MPI standard 1.1 (1995)
45. MPICH, Argonne National Laboratory, University of Chicago Installation and User's Guide to MPICH (2003)
46. Whitaker, R. F. F. A fast algorithm for the greedy interchange for large-scale clustering and median location problems. INFORS, 21: 95-108 (1983)