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<p>The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture characterization method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows discrimination between figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it is invariant to image scaling, (iv) it can be used for both black and white and color images. Validations have been performed not only using synthetic images but also using the well-known Brodatz image database.</p>

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Texture discrimination via Hilbert curve path based information quantifiers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月15日

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  • Aurelio F. Bariviera,
  • Roberta Hansen,
  • Verónica E. Pastor

摘要

The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture characterization method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows discrimination between figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it is invariant to image scaling, (iv) it can be used for both black and white and color images. Validations have been performed not only using synthetic images but also using the well-known Brodatz image database.