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An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

机译:具有自适应参数的图像增强和PCNN的图像分割方法

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PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.
机译:PCNN模型特别适用于图像分割和边缘提取,但其效果取决于PCNN模型中参数的选择和网络迭代设置,这需要大量的人工交互,并且具有有限的PCNN图像处理实用性。本文通过组合图像和PCNN模型的统计特性,我们介绍了一种基于像素分布以替换人工交互的自适应算法。实验结果表明,使用具有自适应参数的图像增强和PCNN的图像分割显着优于传统的PCNN图像分割并验证该方法的有效性。

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