Advertisement

Classification of Textures for Autonomous Cleaning Robots Based on the GLCM and Statistical Local Texture Features

  • Andrzej Seul
  • Krzysztof Okarma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 764)

Abstract

In the paper a texture classification method utilizing the Gray Level Co-occurrence Matrix (GLCM) is proposed which can be applied for autonomous cleaning robots. Our approach is based on the analysis of chosen Haralick features calculated locally together with their selected statistical properties allowing to determine the additional features used for classification purposes. To verify the presented approach a dedicated color image dataset containing textures selected from the Amsterdam Library of Textures (ALOT) representing surfaces typical for the autonomous cleaning robots has been used. The results obtained for various color models and three different classifiers confirm the influence of the color model as well as the advantages of the proposed extended GLCM based approach.

Keywords

Texture analysis GLCM Haralick features Cleaning robots 

References

  1. 1.
    Arvis, V., Debain, C., Berducat, M., Benassi, A.: Generalization of the cooccurrence matrix for colour images: application to colour texture classification. Image Anal. Stereol. 23(1), 63–72 (2011)CrossRefGoogle Scholar
  2. 2.
    Burghouts, G.J., Geusebroek, J.M.: Material-specific adaptation of color invariant features. Pattern Recogn. Lett. 30(3), 306–313 (2009)CrossRefGoogle Scholar
  3. 3.
    Fastowicz, J., Okarma, K.: Texture based quality assessment of 3D prints for different lighting conditions. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) Computer Vision and Graphics: International Conference, ICCVG 2016. Lecture Notes in Computer Science, vol. 9972, pp. 17–28. Springer International Publishing, Cham (2016)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    Komorkiewicz, M., Gorgoń, M.: Foreground object features extraction with GLCM texture descriptor in FPGA. In: Proceedings of 2013 Conference on Design and Architectures for Signal and Image Processing (DASIP), Cagliari, Italy, pp. 157–164, October 2013Google Scholar
  6. 6.
    Li, Y., Birchfield, S.T.: Image-based segmentation of indoor corridor floors for a mobile robot. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 837–843. IEEE (2010)Google Scholar
  7. 7.
    Murray, H., Lucieer, A., Williams, R.: Texture-based classification of sub-antarctic vegetation communities on heard island. Int. J. Appl. Earth Obs. Geoinf. 12(3), 138–149 (2010)CrossRefGoogle Scholar
  8. 8.
    Nixon, M.S., Aguado, A.S.: Feature Extraction & Image Processing for Computer Vision. Academic Press, San Diego (2012)Google Scholar
  9. 9.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, Stamford (2014)Google Scholar
  10. 10.
    Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 7(3), 1–22 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

Personalised recommendations