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Image segmentation based on PCNN model combined with automatic wave and synaptic integration

机译:基于PCNN模型结合自动波与突触融合的图像分割。

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A PCNN model combined with synaptic integration and automatic wave is presented in this paper. The fired neurons and unfired neurons in neighborhood are taken as excitatory and inhibitory synapses respectively, and the result of synaptic integration serves as the PCNN linking input; the firing map of the image spreads in decaying automatic wave, then the segmentation result is obtained when the map turn to be stable. The experimental results demonstrate the proposed model perform well in edge areas and restrains the over segmentation phenomenon, the shape measure and the contrast measure are improved at the same time.
机译:提出了一种结合了突触整合和自动波的PCNN模型。邻近的激发神经元和未激发神经元分别被认为是兴奋性突触和抑制性突触,突触整合的结果作为PCNN链接输入。图像的发射图以衰减的自动波传播,当图变得稳定时,得到分割结果。实验结果表明,提出的模型在边缘区域表现良好,抑制了过度分割现象,同时改善了形状度量和对比度度量。

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