基于二维EMD驱动的水平集模型用于声纳图像分割

Xiufen Ye, Lei Wang, Tian Wang, G. Wang
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引用次数: 6

摘要

本文提出了一种新的多相水平集模型。它的能量函数由二维经验模态分解(two - dimensional EMD, Empirical Mode Decomposition)驱动来解决声呐图像的分割问题。介绍了EMD和BEMD,并给出了BEMD的步骤。它用于提取图像的内在成分。然后,我们将它们整合到VC (Vese-Chan)多相水平集能量函数中,以解决水平集模型对噪声的敏感性。实验结果表明,该方法的分割效果优于VC水平集模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A level-set model driven by Bidimensional EMD for sonar image segmentation
This paper proposes a new multiphase level set model. Its energy function is driven by Bidimensional EMD (Empirical Mode Decomposition) to resolve the segmentation problem of sonar image. We introduce the EMD and BEMD, and give the steps of BEMD. It is used to extract intrinsic components of images. Then, we integrate them into the VC's (Vese-Chan) multiphase level set energy functions to resolve the sensitiveness of level set models to noise. Experimental results show that the segmentation results of our method is superior than the VC level set model.
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