The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-266

“Texture Segmentation via Haar Fractal Feature Estimation”

by Lance M. Kaplan and C.-C. Jay Kuo

August 1994

We examine an approach for texture segmentation by using the fractal dimensions along the 1-D cross sections of 2-D texture data as image features, where an effective Haar transform fractal estimation algorithm is utilized. The major advantage of the Haar fractal estimator is its computational efficiency along with robustness. 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, real Brodatz textures, and natural scenes.

To download the report in PDF format click here: USC-SIPI-266.pdf (1.2Mb)