Near optimum estimation of local fractal dimension for image segmentation

被引：59

作者：

Novianto, S ^{[1
]}

Suzuki, Y ^{[1
]}

Maeda, J ^{[1
]}

机构：

[1] Muroran Inst Technol, Dept Comp Sci & Syst Engn, Muroran, Hokkaido 0508585, Japan

来源：

PATTERN RECOGNITION LETTERS | 2003年 / 24卷 / 1-3期

关键词：

local fractal dimension; blanket method; optimization; local image feature; image segmentation;

D O I：

10.1016/S0167-8655(02)00261-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 x 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. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：365 / 374

页数：10

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