Morphology-based multifractal estimation for texture segmentation

Yong Xia, Dagan Feng, Rongchun Zhao

科研成果: 期刊稿件文章同行评审

99 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)614-623
页数10
期刊IEEE Transactions on Image Processing
15
3
DOI
出版状态已出版 - 3月 2006

指纹

探究 'Morphology-based multifractal estimation for texture segmentation' 的科研主题。它们共同构成独一无二的指纹。

引用此