蔡荣太, 吴庆祥. 基于脉冲神经网络的边缘检测[J]. 微电子学与计算机, 2010, 27(10): 178-181,185.
引用本文: 蔡荣太, 吴庆祥. 基于脉冲神经网络的边缘检测[J]. 微电子学与计算机, 2010, 27(10): 178-181,185.
CAI Rong-tai, WU Qing-xiang. Edge Detection Based on Spiking Neural Networks[J]. Microelectronics & Computer, 2010, 27(10): 178-181,185.
Citation: CAI Rong-tai, WU Qing-xiang. Edge Detection Based on Spiking Neural Networks[J]. Microelectronics & Computer, 2010, 27(10): 178-181,185.

基于脉冲神经网络的边缘检测

Edge Detection Based on Spiking Neural Networks

  • 摘要: 提出了一种利用脉冲神经元的脉冲放电时间对图像像素进行编码和边缘提取的方法.首先将图像像素转化为神经元的输出脉冲序列.再用脉冲序列的首个脉冲放电时间编码图像的像素.并将一个窗口内的首个脉冲同时输入下一层的一个脉神经元, 若窗口内对应的像素在一个平坦的区域, 这些脉冲同时到达神经元, 只能激发出稀疏的脉冲序列;如果窗口内对应的像素在一个边缘区域, 这些脉冲在不同时刻到达神经元, 将激发出稠密的脉冲序列.最后设定一个阈值, 根据输出脉冲序列的密度区分边缘像素和非边缘像素, 提取图像的边缘.实验结果表明该方法具有良好的边缘检测效果, 更加符合生物信息处理机制.

     

    Abstract: A bio-inspired edge detection method based on spiking neural networks is proposed.Firstly, the image pixels are transferred into spiking trains.The first firing time of the spiking trains are used to encode the image pixels.Then, the coding in a window are inject into a neuron.If the corresponding pixels of the window are in a flat region, the coding will go to the neuron in a same time and force the neuron to produce a sparse spike trains;If the corresponding pixels of the window are in edge region, the coding will go to the neuron in different time and force the neuron to produce a dense spike trains.The edge pixel is determined by whether the firing density of the corresponding neuron is beyond a threshold.The experimental results show that the method is better than many traditional methods in mathematical sense, and is more biological realistic than the existing methods.

     

/

返回文章
返回