Elsevier

Pattern Recognition

Volume 25, Issue 9, September 1992, Pages 1035-1041
Pattern Recognition

An efficient approach to estimate fractal dimension of textural images

https://doi.org/10.1016/0031-3203(92)90066-RGet rights and content

Abstract

Fractal dimension is an interesting parameter to characterize roughness in an image. It can be used in texture segmentation, estimation of three-dimensional (3D) shape and other information. A new method is proposed to estimate fractal dimension in a two-dimensional (2D) image which can readily be extended to a 3D image as well. The method has been compared with other existing methods to show that our method is both efficient and accurate.

References (14)

  • A.P. Pentland

    Shading into texture

    Art. Intell.

    (1986)
  • C. Pickover et al.

    Fractal characterisation of speech waveform graphs

    Comput. Graphics

    (1986)
  • B.B. Mandelbrot

    Fractal Geometry of Nature

    (1982)
  • A.P. Pentland

    Fractal based description of natural scenes

    IEEE Trans. Pattern Analysis Mach. Intell.

    (1984)
  • J.D. Orford et al.

    The use of fractal dimension to characterize irregular-shaped particle

    Sedimentology

    (1983)
  • B.H. Kaye

    Fractal dimension and signature wave form characterization of fine particle shape

    Am. Laboratory

    (1986)
  • J.P. Rigaut

    Automated image segmentation by mathematical morphology and fractal geometry

    J. Microscopy

    (1988)
There are more references available in the full text version of this article.

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