Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1872
Title: Morphology-based multifractal estimation for texture segmentation
Authors: Xia, Yong
Feng, D. David
Zhao, Rongchun
Subjects: Fractal dimension
Image segmentation
Mathematical morphology
Multifractal estimation
Issue Date: Mar-2006
Publisher: IEEE
Source: IEEE transactions on image processing, Mar. 2006, v. 15, no. 3, p. 614-623.
Abstract: Multifractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based multifractal dimension estimations are most commonly used. However, in spite of its computational efficiency, the regular partition scheme used by various box-counting methods intrinsically produces less accurate results. In this paper, a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors, namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures. A series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture mosaics and real images. The comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1872
DOI: 10.1109/TIP.2005.863029
ISSN: 1057-7149
Appears in Collections:EIE Journal/Magazine Articles

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