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The PCNN adaptive segmentation algorithm based on visual perception

机译:基于视觉感知的PCNN自适应分割算法

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To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.
机译:解决脉冲耦合神经网络(PCNN)的网络自适应参数确定问题,提高图像分段导致图像分割。提出了基于视觉感知信息的PCNN自适应分割算法。基于视觉感知和光神经细胞接收场的Gabor数学模型的图像信息,该算法自适应地确定图像的每个像素的接收场。通过Gabor数学模型自适应地确定PCNN的网络参数W,M和β,这可以克服图像分割领域中传统PCNN参数确定的问题。实验结果表明,该算法可以提高分割图像的区域连接和边缘规律性。并且还示出了用于分割图像的视觉感知信息的PCNN。

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