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Enhancing LBP by preprocessing via anisotropic diffusion

Author

Listed:
  • Mariane Barros Neiva

    (Institute of Mathematics and Computer Science, University of São Paulo, USP, Avenida, Trabalhador São-Carlense, 400, 13566-590, São Carlos, SP, Brazil)

  • Patrick Guidotti

    (Department of Mathematics, University of California Irvine, 340 Rowland Hall, Irvine, CA 92697, USA)

  • Odemir Martinez Bruno

    (São Carlos Institute of Physics, University of São Paulo, São Carlos – SP, PO Box 369, 13560-970, Brazil)

Abstract

The main goal of this paper is to study the addition of a new preprocessing step in order to improve local feature descriptors and texture classification. The preprocessing is implemented by using transformations which help highlight salient features that play a significant role in texture recognition. We evaluate and compare four different competing methods: three different anisotropic diffusion methods including the classical anisotropic Perona–Malik diffusion and two subsequent regularizations of it and the application of a Gaussian kernel, which is the classical multiscale approach in texture analysis. The combination of the transformed images and the original ones are analyzed. The results show that the use of the preprocessing step does lead to an improvement in texture recognition.

Suggested Citation

  • Mariane Barros Neiva & Patrick Guidotti & Odemir Martinez Bruno, 2018. "Enhancing LBP by preprocessing via anisotropic diffusion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-29, August.
    • Handle: RePEc:wsi:ijmpcx:v:29:y:2018:i:08:n:s0129183118500717
    DOI: 10.1142/S0129183118500717
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    References listed on IDEAS

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    1. Diego Raphael Amancio & Cesar Henrique Comin & Dalcimar Casanova & Gonzalo Travieso & Odemir Martinez Bruno & Francisco Aparecido Rodrigues & Luciano da Fontoura Costa, 2014. "A Systematic Comparison of Supervised Classifiers," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
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