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Raiser, S.; Lughofer, E.; Eitzinger, C.; Smith, J. (2010)
Publisher: Springer Verlag
Languages: English
Types: Article
Subjects:
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen. Classification and Regression Trees. Chapman and Hall, Boca Raton, 1993.
    • [2] P. Caleb-Solly and J.E. Smith. Adaptive surface inspection via interactive evolution. Image and Vision Computing, 25(7):1058-1072, 2007.
    • [3] M. Daszykowski, B. Walczak, and D. L. Massart. Looking for Natural Patterns in Data. Part 1: density based approach. Chemometrics and Intelligent Laboratory Systems, 56(2):83-92, 2001.
    • [4] Martin Ester, Hans-Peter Kriegel, Joerg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge Discovery and Data Mining, pages 226-231. AAAI Press, 1996.
    • [5] G. Gan, C. Ma, and J.Wu. Data Clustering: Theory, Algorithms and Applications. Siam, Society for Industrial and Applied Mathematics, American Statistical Association, 2007.
    • [6] Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing 2nd Edition. Prentice Hall, Inc., New Jersey, USA, 2002.
    • [7] Maria Halkidi, Yannis Batistakis, and Michalis Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 17(2/3):107-145, 2001.
    • [8] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer Verlag, New York, Berlin, Heidelberg, Germany, 2001.
    • [9] B. Hopkins. A new method for determining the type of distribution of plant individuals. Annals of Botany, 18:213-226, 1954.
    • [10] J. Iivarinen and J. Rauhamaa. Surface inspection of web materials using the self-organizing map. In Proc. SPIE Vol. 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, David P. Casasent; Ed., pages 96-103, 1998.
    • [11] A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM Computing Surveys, 31(3):264- 323, 1999.
    • [12] A.A. Hashim J.G. Campbell and F. Murtagh. Flaw detection in woven textiles using space-dependent fourier transform. In ISSC '97, Irish Signals and Systems Conference, F.J. Owens, editor, pages 241-252.
    • [13] C.W. Kim and A.J. Koivo. Hierarchical classification of surface defects on dusty wood boards. Pattern Recognition Letters, 15:713-712, 1994.
    • [14] T. Kubota, P. Talekar, X. Ma, and T.S. Sudarshan. A nondestructive automated defect detection system for silicon carbide wafers. Machine Vision and Applications, 16:170-176, 2005.
    • [15] M. Niskanen. A Visual Training based Approach to Surface Inspection. PhD thesis, Department of Electrical and Information Engineering, University of Oulu, June 2003.
    • [16] S. Ozdemir, A. Baykut, R. Meylani, and A. Ertu¨zu¨n A. Er¸cil. Comparative evaluation of texture analysis algorithms for defect inspection of textile products. In Proceedings Int. Conf. on Pattern Recognition, pages 1738-1741.
    • [17] G. Papari and N. Petkov. Algorithm that mimics human perceptual grouping of dot patterns. In Proc. First Int. Symp. on Brain, Vision and Artificial Intelligence BVAI, Naples, volume 3704, pages 497-506. Springer-Verlag Berlin Heidelberg, October 2005.
    • [23] Linda G. Shapiro and George C. Stockman. Computer Vision. Prentice Hall, Inc., New Jersey, USA, 2001.
    • [24] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
    • [25] R. Sibson. Slink: An optimally efficient algorithm for the single-link cluster method. Computer Journal, 16(1):30-34, 1973.
    • [26] M.L. Smith. Surface Inspection Techniques: Using the Integration of Innovative Machine Vision and Modelling Techniques. Professional Engineering Publishing, 2000.
    • [27] M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36:111-147, 1974.
    • [29] V. Vapnik. Statistical Learning Theory. Wiley and Sons, New York, 1998.
    • [30] K. Wagstaff and C. Cardie. Clustering with instance-level constraints. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 1103-1110, 2000.
    • [31] Wei Wang, Jiong Yang, and Richard R. Muntz. STING: A statistical information grid approach to spatial data mining. In Twenty-Third International Conference on Very Large Data Bases, pages 186-195, Athens, Greece, 1997. Morgan Kaufmann.
    • [32] P.D. Wasserman. Advanced Methods in Neural Computing. Van Nostrand Reinhold, New York, 1993.
    • [33] D.H. Wolpert. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82, 1997.
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