Self-similar texture characterization using a Fourier-domain maximum likelihood estimation method

Authors:
C. -Y. Wen;R. Acharya
Affiliations:
Department of Computer Science and Information Management, Hung-Kuang Institute of Technology, Sha-Lu, 34 Chung-Chie Rd., Taichung, Taiwan;Department of Electrical and Computer Engineering, State University of New York at Buffalo, Buffalo, NY, USA
Venue:
Pattern Recognition Letters
Year:
1998

Citing 3
Cited 1

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Abstract

A Maximum Likelihood Estimator (MLE) has been applied to estimating the Hurst parameter H on a self-similar texture image. Much of the work done so far has concentrated on the spatial domain. In this paper, we propose an approximate MLE method for estimating H in the Fourier domain. This method saves computational time and can be applied to estimating the parameter H directly from the Fourier-domain raw data collected by the Magnetic Resonance Imaging (MRI) scanner. We use synthetic fractal datasets and a human tibia image to study the performance of our method.