Randomized Neural Network Based Signature for Classification of Titanium Alloy Microstructures

  • Jarbas Joaci de Mesquita Sá Junior
  • André R. Backes
  • Odemir Martinez Bruno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


This paper presents the application of the randomized neural network based signature, an innovative and powerful texture analysis algorithm, to a relevant problem of metallography, which consists of classifying zones of titanium alloys Ti-6Al-4V into two categories: “alpha and beta” and “alpha + beta”. The obtained results are very promising, with accuracy of 98.84% by using LDA, and accuracy of 98.64%, precision of 99.11% for “alpha and beta”, and precision of 98.09% for “alpha + beta” by using SVM. This performance suggests that this texture analysis method is a valuable tool that can be applied to many other problems of metallography.


Randomized neural network based signature Titanium alloy Texture Metallography 



Jarbas Joaci de Mesquita Sá Junior thanks CNPq (National Council for Scientific and Technological Development, Brazil) (Grant: 152054/2016-2 and 453835/2017-1) for the financial support of this work. André R. Backes gratefully acknowledges the financial support of CNPq (Grant #302416/2015-3) and FAPEMIG (Foundation to the Support of Research in Minas Gerais) (Grant #APQ-03437-15). Odemir M. Bruno gratefully acknowledges the financial support of CNPq (307797/2014-7 and 484312/2013-8) and FAPESP (14/08026-1). We also thank the authors of the paper [9] for kindly providing the titanium alloy images used in this paper.


  1. 1.
    Backes, A.R., Martinez, A.S., Bruno, O.M.: Texture analysis based on maximum contrast walker. Pattern Recogn. Lett. 31(12), 1701–1707 (2010)CrossRefGoogle Scholar
  2. 2.
    Kaplan, L.M.: Extended fractal analysis for texture classification and segmentation. IEEE Trans. Image Process. 8(11), 1572–1585 (1999)CrossRefGoogle Scholar
  3. 3.
    Al-Kadi, O.S.: Texture measures combination for improved meningioma classification of histopathological images. Pattern Recogn. 43(6), 2043–2053 (2010)CrossRefGoogle Scholar
  4. 4.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49(2), 117–125 (2010)CrossRefGoogle Scholar
  5. 5.
    Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634–3641 (2012)CrossRefGoogle Scholar
  6. 6.
    Jantzen, J., Norup, J., Dounias, G., Bjerregaard, B.: Pap-smear benchmark data for pattern classification. In: Proceedings of the NiSIS 2005, Albufeira, Portugal. NiSIS, pp. 1–9 (2005)Google Scholar
  7. 7.
    Casanova, D., de Mesquita Sá Junior, J.J., Bruno, O.M.: Plant leaf identification using Gabor wavelets. Int. J. Imaging Syst. Technol. 19(1), 236–243 (2009)CrossRefGoogle Scholar
  8. 8.
    Tang, Z., Su, Y., Er, M.J., Qi, F., Zhang, L., Zhou, J.: A local binary pattern based texture descriptors for classification of tea leaves. Neurocomputing 168, 1011–1023 (2015)CrossRefGoogle Scholar
  9. 9.
    Ducato, A., Fratini, L., La Cascia, M., Mazzola, G.: An automated visual inspection system for the classification of the phases of Ti-6Al-4V titanium alloy. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 362–369. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  10. 10.
    Mishra, R., Ma, Z.: Friction stir welding and processing. Mat. Sci. Eng.: R: Rep. 50(12), 1–78 (2005)CrossRefGoogle Scholar
  11. 11.
    Schmidt, W.F., Kraaijveld, M.A., Duin, R.P.W.: Feedforward neural networks with random weights. In: Proceedings of the 11th IAPR International Conference on Pattern Recognition, vol. II, Conference B: Pattern Recognition Methodology and Systems, pp. 1–4 (1992)Google Scholar
  12. 12.
    Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. IEEE Comput. J. 25(5), 76–79 (1992)CrossRefGoogle Scholar
  13. 13.
    Pao, Y.H., Park, G.H., Sobajic, D.J.: Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163–180 (1994)CrossRefGoogle Scholar
  14. 14.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)CrossRefGoogle Scholar
  15. 15.
    de Mesquita Sá Junior, J.J., Backes, A.R.: ELM based signature for texture classification. Pattern Recogn. 51, 395–401 (2016)CrossRefGoogle Scholar
  16. 16.
    Lehmer, D.H.: Mathematical methods in large scale computing units. Ann. Compt. Lab. Harv. Univ. 26, 141–146 (1951)MathSciNetMATHGoogle Scholar
  17. 17.
    Park, S.K., Miller, K.W.: Random number generators: good ones are hard to find. Commun. ACM 31(10), 1192–1201 (1988)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar
  19. 19.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  20. 20.
    Backes, A.R., Gonçalves, W.N., Martinez, A.S., Bruno, O.M.: Texture analysis and classification using deterministic tourist walk. Pattern Recogn. 43(3), 685–694 (2010)CrossRefMATHGoogle Scholar
  21. 21.
    Campiteli, M.G., Martinez, A.S., Bruno, O.M.: An image analysis methodology based on deterministic tourist walks. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA/SBIA -2006. LNCS (LNAI), vol. 4140, pp. 159–167. Springer, Heidelberg (2006). CrossRefGoogle Scholar
  22. 22.
    Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)CrossRefMATHGoogle Scholar
  23. 23.
    Chang, T., Kuo, C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)CrossRefGoogle Scholar
  24. 24.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)MATHGoogle Scholar
  25. 25.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jarbas Joaci de Mesquita Sá Junior
    • 1
    • 2
  • André R. Backes
    • 3
  • Odemir Martinez Bruno
    • 1
  1. 1.São Carlos Institute of PhysicsUniversity of São PauloSão CarlosBrazil
  2. 2.Department of Computer EngineeringCampus de Sobral - Universidade Federal do CearáSobralBrazil
  3. 3.School of Computer ScienceUniversidade Federal de UberlândiaUberlândiaBrazil

Personalised recommendations