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Proceedings Paper

Fast fractal feature extraction for texture segmentation using wavelets
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Paper Abstract

In this work, we use the 1D Haar transform fractal estimation algorithm to calculate the local fractal dimension estimates of 2D texture data. The new algorithm provides directed fractal dimension estimates which are used as features for texture segmentation. The method is fast due to the pyramid structure of the Haar transform and nearly optimal in the maximum likelihood sense for fBm data. We compare the low complexity of this new algorithm with the complexity of existing fractal feature extraction techniques, and test our new method on fBm data and real Brodatz textures.

Paper Details

Date Published: 30 June 1994
PDF: 12 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179222
Show Author Affiliations
Lance M. Kaplan, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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