Fast fractal feature extraction for texture segmentation using wavelets

Lance M. Kaplan; C.-C. Jay Kuo

doi:10.1117/12.179222

30 June 1994 Fast fractal feature extraction for texture segmentation using wavelets

Lance M. Kaplan, C.-C. Jay Kuo

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179222
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

Citation Download Citation

Lance M. Kaplan and C.-C. Jay Kuo "Fast fractal feature extraction for texture segmentation using wavelets", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179222

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