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Image feature extraction and analysis based on Empirical mode decomposition

机译:基于经验模态分解的图像特征提取与分析

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This Empirical mode decomposition (EMD) is a kind of multi-scale transformation theory which is suitable for nonlinear and non-stationary signal processing. It is not necessary to select the basis function in advance, and can adaptively adjust according to the characteristics of the signal itself. Extracting the intrinsic mode function (IMF) is an important process for the applications for empirical mode decomposition in one or two-dimensional image processing. The choice of decomposition scale and the extraction and selection of the intrinsic mode function are the principal and basic content for the right application and understanding. Aiming at these problems, this paper has discussed and studied them in depth, and some actual images are used to verify the feature extraction method, and the corresponding conclusions are obtained from the experimental results.
机译:经验模态分解(EMD)是一种适用于非线性和非平稳信号处理的多尺度变换理论。不必预先选择基函数,而是可以根据信号本身的特性进行自适应调整。提取固有模式函数(IMF)是一维或二维图像处理中经验模式分解应用程序的重要过程。分解尺度的选择以及固有模式函数的提取和选择是正确应用和理解的主要和基本内容。针对这些问题,本文进行了深入的讨论和研究,并通过实际图像验证了特征提取方法,并从实验结果中得出了相应的结论。

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