Abstract
In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation.
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References
Albuquerque, V.H.C., Alexandria, A.R., Cortez, P.C., Tavares, J.M.R.S.: Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E International 42(7), 644–651 (2009)
Albuquerque, V.H.C., Cortez, P.C., Alexandria, A.R., Tavares, J.M.R.S.: A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network. Non-destructive Testing and Evaluation 23(4), 273–283 (2008)
Callister, W.D.: Materials Science and Engineering: An Introduction. John Wiley & Sons Inc., New York (2006)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libSVM
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
De Santis, A., Di Bartolomeo, O., Iacoviello, D., Iacoviello, F.: Quantitative shape evaluation of graphite particles in ductile iron. Journal of Materials Processing Technology 196, 292–302 (2008)
Falcão, A.X., Stolfi, J., Lotufo, R.A.: The image foresting transform: Theory, algorithm and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 19–29 (2004)
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Gomes, O.F.M., Paciornik, S.: Automatic classification of graphite in cast iron. Microscopy and Microanalysis 11, 363–371 (2005)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1998)
Jiang, H., Tan, Y., Lei, J., Zeng, L., Zhang, Z., Hu, J.: Auto-analysis system for graphite morphology of grey cast iron. Journal of Automated Methods & Management in Chemistry 25(4), 87–92 (2003)
Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology 19(2), 120–131 (2009)
Papa, J.P., Suzuki, C.T.N., Falcão, A.X.: LibOPF: A library for the design of optimum-path forest classifiers (2008), http://www.ic.unicamp.br/~afalcao/LibOPF
Reyzin, L., Schapire, R.E.: How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd International Conference on Machine Learning, vol. 8, pp. 753–760 (2006)
Scozzafava, A., Tomesani, L., Zucchelli, A.: Image analysis automation of spheroidal cast iron. Journal of Materials Processing Technology 153-154, 853–859 (2004)
Tang, B., Mazzoni, D.: Multiclass Reduced-set Support Vector Machines. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 921–928 (2006)
Zhibin, C., Yongquan, Y., Heqing, C., Shaomin, Y.: Fuzzy recognition of graphite morphology in nodular cast iron based on evolution strategy. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 8, pp. 4930–4935 (2005)
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Papa, J.P., de Albuquerque, V.H.C., Falcão, A.X., Tavares, J.M.R.S. (2010). Fast Automatic Microstructural Segmentation of Ferrous Alloy Samples Using Optimum-Path Forest. In: Barneva, R.P., Brimkov, V.E., Hauptman, H.A., Natal Jorge, R.M., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Represented in Images. CompIMAGE 2010. Lecture Notes in Computer Science, vol 6026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12712-0_19
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DOI: https://doi.org/10.1007/978-3-642-12712-0_19
Publisher Name: Springer, Berlin, Heidelberg
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