Skip to main content

Classification of corrosion images by wavelet signatures and LVQ networks

  • Posters
  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 1995)

Abstract

In this paper, a method is described for the classification of corrosion images into two distinct classes. Since segmentation is very difficult, an automatic feature selection and classification procedure is preferred. This is done by performing a wavelet decomposition of the images, and computing energy signatures from the decomposition. Compact signature vectors represent the images and effectively characterize their type. The recognition is performed with a Learning Vector Quantization network. The method is tested on a set of 398 images, 260 of which were for training. High recognition scores are obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Smets, H., Bogaerts, W.: Deriving corrosion knowledge from case histories: the neural network approach. Materials and design. 13 (1992) 149–153

    Google Scholar 

  2. Smets, H., Bogaerts, W.: Neural Network Prediction of Stress Corrosion Cracking. Materials Performance. 31 (1992) 64–67

    Google Scholar 

  3. Bogaerts, W., Smets, H., Vancoille, M., Arents, H., Embrechts, M.: Computer aided corrosion engineering. NACE publ. (1993)

    Google Scholar 

  4. Peretto, P.: An introduction to the modeling of neural networks University Press. Cambridge UK. (1992)

    Google Scholar 

  5. Chang, T., Kuo, C. C.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Trans. Image Proc. 2 (1993) 429–441

    Google Scholar 

  6. Laine, A., Fan, J.: Texture classification by Wavelet Packet Signatures. IEEE Trans. PAMI. 15 (1993) 1186–1191

    Google Scholar 

  7. Shumacher, P., Zhang, J.: Texture Classification using Neural Networks and Discrete Wavelet Transform. Proc. IEEE Int. Conf. on Image Processing (1994) 903–907

    Google Scholar 

  8. Mallat, S.: A theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. PAMI. 11 (1989) 674–693

    Google Scholar 

  9. Daubechies, I.: Orthonormal bases of Compactly Supported Wavelets. Comm. Pure Applied Math., 44 (1988) 909–996

    Google Scholar 

  10. Coifman, R. R., Wickerhauser, M. V.: Entropy based methods for best basis selection. IEEE Trans. Info. Theory 38 (1992) 719–746

    Google Scholar 

  11. Reed, T. R., du Buf, J. M. H.: A review of recent Texture Segmentation and Feature Extraction Techniques. CVGIP Image Understanding, 57 (1993) 359–372

    Google Scholar 

  12. Kohonen, T.: The Self-organizing Map. Proc. IEEE 78 (1990) 1464–1480

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Václav Hlaváč Radim Šára

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Livens, S. et al. (1995). Classification of corrosion images by wavelet signatures and LVQ networks. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_341

Download citation

  • DOI: https://doi.org/10.1007/3-540-60268-2_341

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60268-2

  • Online ISBN: 978-3-540-44781-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics