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A new box-counting method for image fractal dimension estimation

Authors: Song Xue; Xinsheng Jiang; Jimiao Duan;

A new box-counting method for image fractal dimension estimation

Abstract

A fractal dimension is an effective feature for texture analysis, segmentation and classification in many fields. The most frequently used method to estimate the fractal dimension of an image is the differential box-counting method. This method is simple but not accurate enough. Many researches have been done to improve its counting accuracy. So far, the counting accuracy along z direction has been improved more effectively than along x and y directions. In this paper, some typical improved methods are summarized and analyzed. And then a new method based on the idea of dilation is proposed, which is simple and understandable, and has the same desired counting accuracy in x, y, and z directions. Simulation results demonstrate our method can outperform other existing methods in estimation accuracy and robustness.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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