期刊
PATTERN RECOGNITION
卷 42, 期 11, 页码 2460-2469出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2009.03.001
关键词
Fractal dimension; Box-counting dimension; Fractional Brownian motion; Texture image; Remote sensing image
资金
- 111 Project from the Ministry of Education, China [B08036]
- New Century Excellent Talents in University [NCET-06-0763]
Fractal dimension (FD) is a useful feature for texture segmentation, shape classification, and graphic analysis in many fields. The box-counting approach is one of the frequently used techniques to estimate the FD of an image. This paper presents an efficient box-counting-based method for the improvement of FD estimation accuracy. A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates. The experiments using synthesized fractional Brownian motion images, real texture images, and remote sensing images demonstrate this new method can outperform the well-known differential boxing-counting (DBC) method. (C) 2009 Elsevier Ltd. All rights reserved.
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