Near optimum estimation of local fractal dimension for image segmentation

Authors:
Sonny Novianto;Yukinori Suzuki;Junji Maeda
Affiliations:
Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto-Cho, Muroran-Shi 050-8585, Japan;Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto-Cho, Muroran-Shi 050-8585, Japan;Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto-Cho, Muroran-Shi 050-8585, Japan
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

This paper presents an algorithm for estimating the local fractal dimension (LFD) of textured images. The algorithm is established by an experimental approach based on the blanket method. The proposed method uses the near optimum number of blankets to obtain the LFD for a small local window. The robustness of the proposed method to consistently estimate the LFD using up to a 3 × 3 local window is confirmed by experimental evaluations. The LFD maps, created from natural scenes, are utilized in an image segmentation algorithm that demonstrates the capability of rough segmentation of fine-texture regions in natural images.