二维经验模态分解在图像特征提取中的仿真研究

Qin Wang, Ran Jin, Kun Gao
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引用次数: 1

摘要

图像边缘特征提取是图像处理理论与应用的研究领域之一。传统的图像特征提取方法简单、熟练,但特征提取的准确率却不高。为此,提出了一种利用二维经验模态分解(2d EMD)和Riesz变换提取图像边缘特征的新方法。首先,将图像进行二维EMD分解为多层内禀模态函数(IMF),然后利用具有较强局部保持能力的Riesz变换代替Hilbert变换分析图像的局部特性,进行更高分辨率的边缘特征提取;最后通过仿真验证了该方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation Research of Applying Two-dimensional Empirical Mode Decomposition on Image Feature Extraction
The edge feature extraction of image is one of the research fields of image processing theory and application. The traditional methods of image feature extraction are simple and proficient, but the accurate performance of the feature extracted is not even higher. As such, a new method for extracting image edge feature was proposed by using the two-dimensional Empirical Mode Decomposition (2-D EMD) and Riesz Transform. Firstly, the image was decomposed to multi-level Intrinsic) Mode Function (IMF) by 2-D EMD, and then the Riesz Transform with stronger local preserving ability was used for analyzing their local properties instead of the Hilbert Transform, by which the edge feature extraction with higher resolution was carried on. Finally, simulation shows that the proposed method is feasible and valid.
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