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Morphology-based multifractal estimation for texture segmentation

机译:基于形态的多重分形估计用于纹理分割

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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.
机译:多重分形分析在图像分割社区中变得越来越流行,在这种方法中,最常用的是基于盒计数的多重分形维数估计。但是,尽管其计算效率高,但是各种盒计数方法所使用的常规分区方案本质上却产生了较不准确的结果。提出了一种新的基于数学形态学的多重分形估计算法,并定义了一组新的多重分形描述符,即局部形态多重分形指数来表征纹理的局部缩放特性。利用了一系列立方结构元素和迭代扩张方案,从而可以大大降低形态运算的计算复杂度。所提出的算法和基于盒计数的方法都已应用于纹理镶嵌和真实图像的分割。比较结果表明,形态多重分形估计可以更有效地区分纹理图像并提供更鲁棒的分割。

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