Image analysis by bidimensional empirical mode decomposition
Image analysis by bidimensional empirical mode decomposition
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DOI：
10.1016/s0262-8856(03)00094-5
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发表时间：
2003-11-01
影响因子：
4.7
通讯作者：
Bunel, P
中科院分区：
文献类型：
--
作者：
Nunes, JC;Bouaoune, Y;Bunel, P
非线性和非平稳数据分析方法的最新发展受到图像分析人员的极大关注。1998年，Huang提出了信号处理中的经验模式分解（EMD）。EMD方法，完全无监督，证明可靠的一维（地震和生物医学）信号。我们的方法的主要贡献是应用经验模态分解的纹理提取和图像滤波，这被广泛认为是一个困难和具有挑战性的计算机视觉问题。我们开发了一种基于二维经验模式分解（BEMD）的算法，以提取多尺度或空间频率的特征。这些特征，称为固有模式函数，通过筛选过程提取。二维筛选过程是实现使用形态算子检测区域最大值，并由于径向基函数的表面插值。实验结果表明，采用BEMD方法的纹理提取算法的性能与合成和自然图像。(C)2003 Elsevier B.V.保留所有权利。
Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images. (C) 2003 Elsevier B.V. All rights reserved.