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Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

机译：基于极值约束的多元自对偶形态算子

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摘要

Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation; they treat the image foreground and background identically. However, it is difficult to extend SDMO to multichannel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Furthermore, utilizing extremum constraint to optimize multivariate morphological operators, we construct multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by applications of noise removal and segmentation performance. The experimental results show that the proposed multivariate SDMO achieves better results, and they suppress noises more efficiently without losing image details compared with other filtering methods. Moreover, the proposed multivariate SDMO is also shown to have the best segmentation performance after the filtered images via watershed transformation.
机译：自对偶形态运算符（SDMO）不依赖于以腐蚀还是扩张开始序列；他们以相同的方式对待图像前景和背景。但是，很难将SDMO扩展到多通道图像。基于传统形态算子的自对偶性和极值约束理论，本文对多元SDMO的构造给出了完整的刻画。我们引入一对对称向量排序（SVO）来构造多元对偶形态算子。此外，利用极值约束来优化多元形态算子，我们构建了多元SDMO。最后，我们通过应用噪声去除和分割性能来说明多元SDMO的重要性和有效性。实验结果表明，与其他滤波方法相比，提出的多元SDMO算法取得了更好的效果，并且在不损失图像细节的情况下，可以更有效地抑制噪声。此外，在通过分水岭变换对滤波后的图像进行处理后，所提出的多元SDMO还具有最佳分割性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|596348.1-596348.16|共16页
  • 作者

    Lei Tao; Wang Yi; Luo Weiwei;

  • 作者单位

    Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China|Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

    Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China;

    Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;

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